This study addresses enhancements to the Swedish national freight transport model, Samgods, focusing on improving its maritime transport modeling capabilities. The current model faces challenges in accurately representing maritime transport due to its limitations in consolidation of diverse cargo types, and modeling indirect sea routes. We propose an advanced model using a mixed integer linear programming (MILP) technique to integrate transport loops, thereby increasing the model's efficiency and realism in depicting maritime transport scenarios. A case study on the shipping of forest products from Northern Sweden to Western Europe illustrates the method's effectiveness. Introducing transport loops into the model results in a 10-21% reduction in logistics costs and a 2-4% increase in the utilization of maritime transport. The fleet composition also changes to more resemble real world data. These findings highlight the importance of loop structures in accurately capturing the full benefits of maritime transport in freight transport modeling.

The automotive industry is experiencing a profound shift with the integration of Internet of Things (IoT), encompassing predictive maintenance and autonomous driving. However, IoT implementation faces hurdles such as security, privacy, and interoperability concerns. This study identifies IoT adoption enablers in the automotive supply chain through literature review and expert insights. Methodology combines Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Interpretive Structural Modelling (ISM), and Matrice d'Impacts Croisés Multiplication Appliquée an un Classement (MICMAC) analysis. AHP-TOPSIS prioritizes alternatives based on importance levels, while ISM-MICMAC identifies causal relationships among enablers. Findings highlight Data Analytics, Predictive Maintenance, and Real-time Monitoring as key adoption drivers. Insights aid automotive stakeholders in developing strategies to overcome IoT deployment challenges and leverage its benefits.

This research explores the challenges that prevent chicken meat-based food supply chain actors from adopting blockchain-based halal traceability systems and the relationship between these challenges. The content validation method validated the proposed barriers identified in the literature review. The interpretative structural modeling (ISM) approach was also used to identify the relationship between the valid barriers. Six experts were involved in filling out the validation questionnaire, while six filled out the ISM questionnaire. The results of the content validation method indicate that out of the 15 barriers identified, ten are relevant and require further analysis. According to the results of the ISM, the limited knowledge or ability of the part supply chain actors to operate the systems is the most fundamental barrier that policymakers need to address as it lies in the root layer, which is the starting point of the ISM model

The Readymade Garments (RMG) industry in Bangladesh is a significant economic driver but faces challenges concerning environmental sustainability. This research explores optimizing production processes to balance profit maximization with environmental impact minimization. A multi-objective optimization model is developed and applied to a case study involving ten commonly produced garments. The analysis confirms the inherent trade-off between economic and environmental objectives. Increased production leads to higher profits but also a heavier environmental footprint. A feasible production range is identified, with key insights provided on navigating the trade-off between profit and environmental impact. This research proposes an implementation roadmap for RMG companies to utilize the developed multi-objective optimization model. This research demonstrates the potential of the model for promoting sustainable growth in the Bangladeshi RMG industry.

This research delves into the barriers hindering the effective integration of Environmental, Social, and Governance (ESG) principles within the supply chain management. Despite the growing regulatory demands and societal expectations for sustainable operations, numerous challenges stifle ESG adoption in supply chain management. Utilizing the group-based fuzzy best worst method, this study evaluates the relative significance of these barriers through expert pairwise comparisons. The findings reveal that unclear ESG metrics, accountability, and insufficient governmental support are the predominant obstacles. This paper aims to provide stakeholders with a deeper understanding of these challenges and suggest actionable strategies for enhancing ESG implementation in logistics, thereby contributing to the broader goals of sustainable development.

The growing interest in a sustainable society is influencing consumer behavior, leading to a more widespread circulation of reuse products than ever before. Companies are increasingly engaging in the business of selling reuse products, not just as a form of corporate social responsibility, but also as a business. Taking this into consideration, this study proposes a method for prioritizing used products that are purchased and refurbished during the reuse process. The proposed method is formulated as a QUBO problem, and its performance is evaluated using a hypothetical scenario, revealing situations in which the proposed method functions effectively. Refining this proposed method can be expected to contribute to the reuse product sales business.

Thailand's eastern seaboard is the fastest growing area because of several export economic zones. However, this region historically encountered flood problems because of the Monsoon season and urbanization. To be proactive, the authority must plan to locate relief facilities in advance. Hence, we deployed the P-median weight Minmax model with historical population data in flood areas to decide the relief facility locations. We also investigated three contingency plans where 1) we determined relief facilities from our model, 2) we assigned the number of relief facilities according to the population, and 3) we investigated the contingency plan to determine alternative relief locations when some districts are not suitable. We tested our model with 2015, 2016, 2017, 2018, and 2019 data.

Digital Product Passports (DPP) offer an attractive route for research and development due to their integration across numerous industries. DPP presents a new angle on transparency and has the potential to improve compliance and sustainability in a variety of supply chains. This paper has conducted a comprehensive literature review about the historical aspects of DPP, its role in European sustainability goals and implementation requirements. The paper has developed a DPP enabled model for vertical and horizontal data integration across multi-level supply chains for electric vehicle supply chain. Moreover, the paper investigates the DPP realization for improving cooperation and smooth information exchange between producers, suppliers, retailers, and customers, hence supporting the fundamentals of a circular economy model. By shedding light on the supply chain's vertical and horizontal facets, this model emphasises the critical role that data and technology integration play in achieving the objectives of DPP adoption. By include energy tracking during the consumption phase and covering the entire lifecycle from raw material procurement to end-of-life disposal, this model highlights the significance of a comprehensive and cooperative approach among stakeholders.  

Although deep learning techniques have been successfully applied in machinery fault diagnosis, the key problems of feature learning under multiple non-ideal conditions have not been well solved, such as strong noise interference, limited labeled data, and class imbalance. In this study, noise mask network (NMNet) is developed for feature learning and machinery fault diagnosis under multiple non-ideal conditions. Firstly, a temporal masking block (TMB) is proposed for imbalanced data enhancement. Secondly, a temporal self-supervised learning framework (TSLF) is developed for noise filtering and solving the problem of limited annotation data simultaneously. In addition, an asymmetric multi-scale autoencoder (AMSA) is constructed for deep feature learning and signal reconstruction. The experiment results on rotor testbed demonstrate the effectiveness of NMNet for machinery fault diagnosis under multiple non-ideal conditions.

Machinery health monitoring under time-varying speed conditions is an ongoing challenge, where the significant variations in rotational speed frequently result in false alarms and missed defect detections. In this paper, a simple method, i.e., dynamic contrast analyzer (DC- Analyzer), is proposed to generate health indicators for machinery self-adaptive monitoring. Contrastive learning is used to adjust the representational features of vibration signals with various speeds, which facilitates the self-extraction of manifold trend over speeds. A minor network is proposed to perform feature regression between rotational speeds and vibrational representations for condition alignment. The final health indicators of rotating machinery can be obtained by calculating the feature similarity of real-time vibrational representations and corresponding approximate features from the regressor. The effectiveness of DC- Analyzer is verified on a rolling bearing test rig. The results show that the proposed method outperforms those representative approaches in health indicator generating, which provides more potential for the issue of dynamic and time-varying conditions.

Reliability-redundancy allocation problems (RRAPs) have been widely studied to improve system reliability. In a system, there may be a case where components are homogeneous in one subsystem and heterogeneous in another. However, previous RRAPs only considered the cases of all homogeneous redundant subsystems and all heterogeneous redundant subsystems. In this study, a mixed form of homogeneous and heterogeneous redundant subsystems is considered, and the influence of the increase of the number of heterogeneous redundant subsystems on system reliability is analyzed. Moreover, an improved multi-population genetic algorithm (IMGA) is designed to solve the RRAP. The IMGA controls the spread of advantageous genes among populations through a specific network structure. Experimental results show that the system reliability will increase with the increase of the number of heterogeneous redundant subsystems, and the IMGA with Erdos-Renyi (ER) networks can get better solutions. The comparison with previous studies also proves the superiority of the algorithm designed in this paper.

Welding is both as art and science and its common use is the jointing of points. Some of the welding procedures used for jointing metal pipes are Tungsten Inert Gas and Gas Metal Arc welding and in common practice plastic pipes are bonded with the use of butt-fusion welding procedure. Since 1970’s conjoint analysis was used as a way of finding out what the preference of consumers. The objective of this study is to determine the combination welding attributes that were most preferred using a conjoint analysis approach. With conjoint analysis, together with, the orthogonal design the preference for welding materials and procedures were analyzed. It showed that pipe material with 29.24% was the most preferred attribute and Nondestructive test as the least preferred with 2.66%. The result of this study may be utilized in future related reviews and could be applicable in other related Mechanical systems requiring welding works.

The small damages such as cracks and scratches on the surface of aerospace products pose a serious threat to the safety of life and property, and manual visual inspection is prone to omissions, leaving great safety hazards. Using augmented reality (AR) assisted maintenance systems to assist visual inspection is one of the effective solutions. However, the limitations of computing power in augmented reality devices and the real-time requirements of augmented reality pose significant challenges to small-scale object detection algorithms. Therefore, this paper proposed a metal surface damage recognition method for augmented reality assisted maintenance system. Firstly, for the appearance characteristics of surface damage in the steel image database NEU-CLS, the histogram equalization was employed for image enhancement to improve image quality. Afterwards, a SURF + K-means + Bag-of-Features + the-number-of-feature-points feature extraction and dimensionality reduction method was proposed to improve recognition efficiency while ensuring the robustness of the method. Finally, adaptive boosting learning framework was utilized to construct a surface damage recognition model which has good accuracy and efficiency for common metal surface damages.

The Internet of Things (IoT) is transforming property management by enabling real-time monitoring and control of physical assets within buildings. Smart sensors can detect issues like water leaks or HVAC failures before they escalate, allowing for both preventative and predictive maintenance that can save significant costs and minimize disruptions to tenants. The research study of this paper is to leverages the power of data analytics and Internet of Things (IoT) technologies to enable real-time monitoring of water consumption, leak detection, and equipment performance. By collecting and analyzing data from various sensors and devices installed in water pumping systems, the solution provides valuable insights into water usage patterns, identifies potential leaks or faults, and predicts maintenance needs. To evaluate the effectiveness of the proposed solution, case studies were conducted in a residential building. The results demonstrate the significant benefits for this solution. Notably, it led to substantial reductions in water consumption, improved maintenance planning, and enhanced resource allocation. In conclusion, this paper presents a comprehensive data-driven monitoring and IoT-based predictive maintenance solution in water management for the property management industry

The global goal of achieving 2000 gigawatts of offshore wind power by 2050 has driven the development of the wind energy sector. This ambitious goal is facing a significant challenge in maintaining the efficiency and health of the wind turbines. Wind turbine Blade erosion is among the main critical failure modes that lead to high production losses and maintenance expenses. At present, the industry is utilizing manual or drone-based inspection, however, they are targeting more cost-effective and more informative like to predictive maintenance for erosion and severity by using data science techniques based on SCADA and sensors data. This paper reviews existing erosion blade analysis methods in wind turbines, highlighting their strengths and weaknesses. The method is based on the literature review to determine the state of the art in terms of monitoring, classification, and prediction. Moreover study will evaluate potential predictive maintenance concepts based on key performance matrix extracted from key stakeholders. It can be concluded that a combined concept of vibration, aerodynamic, acoustic, and production loss techniques supported with XGBoost, FFT, and LSTM are the most effective methods.

Deep excavation works in the building and construction industry present significant challenges in terms of safety, stability, and monitoring. In this paper, we present the development of an advanced monitoring system for deep excavation works based on Time Lapse Ground Penetrating Radar (TLGPR) technology. The proposed monitoring system utilizes TLGPR, a non-destructive testing technique that employs radar waves to generate subsurface images. By capturing and comparing multiple TLGPR scans over time, the system enables the detection and analysis of ground movement, soil deformation, and potential stability issues after persistent rain has driven a rise in excavation accidents on road and construction sites. The developed TLGPR used to capture detailed subsurface data. Data is processed using advanced image processing algorithms and machine learning techniques to identify and quantify ground movements and deformations. Case studies conducted on a deep excavation on the roads. The results demonstrate the system's capability to accurately detect and assess ground movements, soil deformations, and potential stability issues. TLGPR detection gives early warning and detection capabilities provided by the system enable proactive decision-making, enhancing safety and minimize risk.

Parking problems pose significant challenges in urban transportation planning and management, exacerbating congestion, economic strain, and environmental degradation. This study introduces a dynamic parking allocation framework aimed at minimizing total vehicular travel time. Our model innovatively integrates a network flow-based method and a greedy heuristic to prioritize parking assignments based on real-time vehicle arrival times. The proposed framework swiftly adjusts to fluctuations in parking demand and supply by incorporating both vehicles' stay durations and anticipated parking requests. Through rigorous numerical experiments, utilizing a real-world data, we validate the model's efficacy and flexibility across various urban settings. The results reveal the model's potential to significantly improve urban parking management by optimizing space utilization and reducing unnecessary vehicular travel, thereby contributing to more sustainable urban transportation systems.

This paper presents a framework for the portfolio selection in the transportation services, hotel and leisure, and education subsectors using the mean-variance model in the Philippine market. The service sector and market serve as benchmarks, while the investment pool, comprising these subsectors, undergoes a set of criteria for selection.  The analysis spans 7,670 test days from January 1, 1993, to December 31, 2022, to determine the optimal risk-return factor (RRF). According to the research, the RRF that is the most optimal in each subsector is 0.4 for the transportation services, 0.2 for the hotel and leisure subsector, and 0.3 for the education subsector. Furthermore, the higher percentage allocations the investors are recommended to invest in are International Container Terminal Services, Inc., Waterfront Philippines, Incorporated, and Far Eastern University, Incorporated. Pair t-test results yield p-values below 0.01 and 0.05 when comparing the subsectors to the service sector, denoting that there is sufficient evidence that the subsectors can outperform the service sector. The findings of this research may provide an alternative portfolio selection model for investors seeking to optimize their investment portfolios.

Process effectiveness in treatment shows how clean water can be treated, and maximizing it is important to achieve the water output requirements. In maximizing treatment effectiveness, optimizing the cost should also be considered to properly budget wastewater treatment plant design and operations. A mixed-integer linear programming model is developed for this, which considers treatment and storage capacity, and the treatment effectiveness of each process to ensure simultaneous minimization of cost and maximization of treatment effectiveness. The model showed that it is possible to skip treatments while having maximized effectiveness. Results show that it is mostly primary treatment that is skipped due to having high effectiveness in the secondary treatment since primary and secondary treatment removes the same byproducts. Future studies could strengthen the model by adding more factors affecting WWTP such as turbidity, temperature, and speed of flow.

Digitalization aims to automate processes, improve efficiency, increase productivity, and reduce carbon footprint compared to analog approaches on an industrial scale. It aligns with the UN Sustainable Development Goals, particularly the 8th goal of Decent Work and Economic Growth. This includes the goal of a Green Economy, which encompasses paperless industrialization. In the Philippines, digital innovations, especially in technical warehouse management, play a crucial role in achieving a Green Economy. This research focuses on optimizing warehouse management at an industrial scale while prioritizing eco-friendly practices. The objective is to provide a comprehensive analysis of the digitization roadmap in Philippine industrial plants and explore the pros and cons of using digital equipment from a user-experience perspective.

The world’s energy requirements have been on a steady increase; all the while governments have pushed for the shift toward renewable energy (RE). As such, numerous studies have focused on optimizing the design of hybrid renewable energy systems (HRES), integrating passive and controllable sources. However, these studies have been limited in scalability and scope, as the models still focus on specific layouts of HRES, and short-term forecasts for expected energy output. Researchers introduce a novel framework leveraging machine learning to predict HRES yield, addressing scalability and enabling long-term decisions. This novel framework allows decision makers to utilize the concepts of machine learning to process and analyze relevant parameters to predict the expected yield of the HRES components and consider every conventional RE technology in the subsections of the model, namely: Solar, Wind, Hydroelectric, Geothermal, Nuclear, Biomass and Fossil Fuel. After the framework was developed, it was tested on a hypothetical dataset. The framework and resulting model were found to be valid and were further tested through sensitivity analysis.

Here we consider a single vehicle routing problem (of unlimited capacity and capacity constraint can be easily included) that visits different dealers. As it visits the first dealer, it offloads the demand of first dealer and moves on to second dealer ‘lighter’, and so on. Fuel consumption depends on both weight carried and distance travelled. In this context we seek to minimize weight-distance travelled by the vehicle for the entire tour. We give two formulations of the above problem that has received very little or no attention in literature. It results in a ‘cubic’ and ‘quadratic’ terms in the objective function with negative cost coefficients in one formulation and positive cost coefficients (of ‘cubic’ and ‘quadratic’ terms) in other formulation. We give two linearization schemes for the two formulations. One linearization has less number of constraints and is expected to be more efficient CPU time wise.

Agriculture is the most water-demanding industry, accounting for approximately 85% of human water consumption. With the growing depletion of groundwater resources, it is essential that farmers find sustainable crop plans and irrigation practices to protect farmer income security while minimizing water consumption. Optimization studies utilizing Linear programming are a tool used to create optimal crop plans that satisfy both profitability requirements and sustainable water consumption. This study created a multi-objective optimization model utilizing Goal programming to create crop plans that protect farmer interests in terms of profitability and water consumption through leveraging environmental conditions such as rainfall, drought, crop seasonality, land requirements, and sustainable water consumption limits. Validated through a theoretical data set and scenario analysis, results were analyzed through performance variables that reveal crop plans that satisfy both farmer income and water conservation goals. 

This research presents a comprehensive study optimizing transportation logistics in the food industry, emphasizing efficiency and sustainability. A mathematical model is developed to minimize total transportation costs, considering fixed and fuel costs. Two scenarios are explored: baseline and cooperating, the latter involving collaborative logistics efforts. Analysis reveals the cooperating scenario reduces transportation costs by 7.4% and improves vehicle utilization rates to 89.7%. Extensive validation confirms the model's reliability. Sensitivity analyses demonstrate its adaptability to varying parameters. The study provides a conceptual framework, validated model, and comparative analysis to aid decision-makers in the industry. It addresses the crucial balance between cost optimization and environmental impact. Future research can build on these insights to refine transportation strategies and promote sustainability.

In the era of globalization, multinational companies (MNCs) conduct innovation in a global network. Japanese companies also conduct innovation in a global network. Since, among the nations across the globe, the member states of the Association of Southeast Asian Nations (ASEAN) hold significant economic importance for Japan, this paper discusses the innovation network patterns of Japanese companies in all ten ASEAN countries. In order to analyze this phenomenon, this paper used the international patent application data based on the Patent Cooperation Treaty (PCT) held by the World Intellectual Property Organization (WIPO). Specifically, the paper retrieved the patent application whose applicant is a Japanese company with at least one inventor in a specific ASEAN country. The paper has found that Japanese companies conduct innovation through the collaboration between a host country and Japan most frequently for any ASEAN country. For Vietnam and the Philippines, Japanese companies conduct innovation in a host country alone as frequently as the collaboration between a host country and Japan.

This study investigates the application of the Eliminate-Reduce-Raise-Create (ERRC) framework and emergent strategies across eight key sectors in Kazakhstan. While existing literature highlights the effectiveness of these frameworks, comparative analysis across sectors is limited. To address this gap, semi-structured interviews with senior management from 22 Kazakhstani companies were conducted providing a broader understanding of strategic innovation practices in a rapidly developing economy. Findings reveal common strategies, such as eliminating outdated technologies, reducing costs, raising digitization and innovation, and creating capability initiatives. However, sector-specific differences reflect unique industry challenges: the study suggests aligning ERRC priorities with emergent strategies and adopting a holistic approach integrating digitization, sustainability, and collaboration. This study contributes to strategic management literature and offers practical insights for business leaders and policymakers in emerging markets, identifying improvement areas and developing strategies to address critical industry factors.

Repatriates in Multinational Corporation as study object, from the new viewpoint of resource orchestration, the impact of subjective well-being, external knowledge acquisition on repatriates' innovation behavior are discussed, a moderated mediation model is established, and the mediating role of external knowledge acquisition and the moderating role of resource orchestration capacity are examined. Results based on empirical study indicate that subjective well-being has significant positive effect on innovation behavior, and this effect is partially mediated by external knowledge acquisition, while resource orchestration capability plays a moderation role in external knowledge acquisition and innovation behavior. Results testing by a moderated mediation also demonstrate that resource orchestration capacity moderates the mediated relationships between learning orientation and innovation behavior via external knowledge acquisition.

Bioscape at the macro scale and biophilia at the micro scale are approaches designed to make urban and corporate environments greener and more natural for humans. These ap-proaches are rooted from the idea that people are more ef-fective and successful in environments that resemble their historical natural habitats. Therefore, from both ecological and psychological perspectives, there is a need to integrate more greenery into cities and buildings. This multiple-case study examines smart city (Ülemiste City), university (Tallinn University), and high school (Mustamäe State High School) environments in innovation-oriented Estonia. The study focuses on small- and mid-scale implementations to illustrate lifecycle from education-to-practice and its various perspectives, presenting best green practices to consider. Not all practices or current situations proved equally valuable, and suggestions are provided for improvement.

This practitioner-focused article delves into the pivotal factors driving the successful adoption of Internet-of-Things (IoT) technology in port logistics. Technological readiness, sustainability, and globalization emerge as key drivers, with their relative importance varying based on port type and managerial domain. Practical implications underscore the need for collaborative efforts between management, government, and stakeholders to ensure seamless IoT integration. Tailored strategies for major and minor ports are essential, emphasizing infrastructure upgrades, modernization, connectivity, and industrialization. Port management plays a critical role in formulating strategic plans, fostering effective communication with supply chain partners, and investing in training and technology. Innovation and adaptability are highlighted as crucial elements, necessitating dedicated research and development efforts and potential consultant engagement. Embracing IoT technology transforms port operations and positions them as intelligent, competitive entities in the evolving logistics landscape. This article provides actionable insights for stakeholders, port management, and policymakers to navigate the path toward a technologically advanced and resilient port logistics industry.

This study explores the feasibility of Maritime Autonomous Surface Ships (MASS) for various shipping segments. Data was collected from a sample of 37 industry and academic respondents using a structured web-survey. The respondents were asked about the expected adoption timeline of MASS in various shipping segments. Within the next 5 years outlook, majority of the respondents (43.24%) expect MASS to be adopted in the regional passenger ferry segment followed by offshore wind farm, and ro-ro shipping. Within 5 to 10 years outlook, container shipping segment becomes replace the third position of ro-ro shipping. While examining the industry and academic sample separately, industry respondents are more optimistic in the 5-year outlook than academics. The study provides insights into future development of MASS into different shipping segments.     

The expansion of reverse innovation is addressed in this paper by reconstructing the concept and demonstrating cases to illustrate how it can be applied in areas beyond the development of physical products. Reverse innovation stands in contrast to the traditional approach of creating products for advanced economies. This new concept of reverse innovation has gained popularity and holds potential for broader applications since 2009. The first component of the paper involves redefining the concept of the reverse innovation to enhance understanding. Additionally, the second part focuses on expanding its applications into different industry sectors. A study case in a TV program demonstrates how the reverse innovation can be applied in the content industries, while another case provides insights into applying the reverse innovation in software engineering and machine learning studies.

This research explores the crucial role of Small and Medium Industries (SMIs) in waste management and environmental sustainability. SMIs not only contribute to responsible waste management, but also have the potential to adopt sustainable business models with the concept of reduce, reuse, recycle. Global challenges to environmental sustainability require SMIs to produce environmentally friendly goods. Environmental innovation is key, and SMIs, as information centers, can facilitate innovation through collaboration with various stakeholders. Knowledge Sharing is important in the context of open innovation, where SMIs share knowledge with industry, research institutions and others. Green Innovation, which involves innovation to improve a company's environmental performance, can be strengthened through effective Knowledge Sharing. This research model finds a positive impact of Open Innovation on Green Innovation, as well as a positive impact of Knowledge Sharing on Open Innovation. These results strengthen the argument for supporting sustainable practices in SMIs, strengthening their contribution to sustainability and sustainable business competitiveness.

Employees' "innovative work behavior" can drive innovation within corporate organizations, signifi-cantly impacting their survival and competitiveness. This study focuses on 356 employees in Taiwan who were recipients of the "National Talent Development Awards" (NTDA) from 2015 to 2023. It examines the relationship between their "work self-efficacy" and "innovative work behavior" while exploring the me-diating effect of "psychological capital" to under-stand its influence on both factors. The research con-cludes that if supervisors aim to enhance employees' innovative work behavior, besides boosting employ-ees' work self-efficacy, improving psychological cap-ital will yield even better results. Furthermore, the study presents future research directions and practi-cal recommendations based on its findings.

This study performs heterogeneous network link prediction to generate business model ideas. Company data were crawled from businessmodelideas, a platform that offers insights into corporate business models, to amass company descriptions and business model canvas information. Technology keywords the companies possess are extracted from the company description data using a technology keyword extraction tool. From the business model canvas data, keywords and phrases of revenue streams and value propositions are collected, embedded using SentenceBERT, and clustered based on semantic similarity through hierarchical clustering. A network is constructed based on the co-occurrence of technology, revenue stream, and value proposition keywords identified as the companies' current business models. Link prediction is then applied to the heterogeneous network to ascertain potential business model archetypes that can be derived according to the newly formed edges. The findings of this study are anticipated to aid in the strategic planning and development of innovative business models.

Due to the rapid development in automations, there is a growing need to improve automated customer support services (ACSS) in its performance to meet customer expectations and satisfactions. However, current objective performance measurements relevant to customer satisfaction are lacking and contradicting when considered with other target measurements in existing ACSS implementation frameworks to provide quantitatively accurate performance assessments of these platforms. The research aims to identify and align the key performance indicators (KPIs) of ACSS through literature review to achieve objective methods of performance measurement. To validate the framework, an ACSS prototype is developed, and data is collected for the KPIs. It is identified that the strong and weak ACSS factors in relation to customer satisfaction are data privacy protection, customer care, customer knowledge, sales and marketing tracking systems, information aggregation, AI training, and knowledge management. Further studies are to provide new measurements and explore the relationships with customer loyalty.

Subscription-based revenue models and platform business models are also becoming increasingly attractive for manufacturing companies. The introduction of such revenue models and business models usually requires a new dimension in customer centricity. Software-as-a-Service companies have demonstrated this. By introducing the Customer Success Management approach, these companies were able to capture cross-selling and up-selling potential and thus realize above-average profits. In this article, we will examine the Customer Success Management (CSM) approach. We will first look at subscription-based revenue and business models and platform business models and the importance of CSM in this context. For the implementation of CSM, we recommend considering six success factors, which we discuss in detail. Finally, the article provides an outlook for platform business models in the area of sustainability, for which the CSM concept is expected to be of significant relevance.

This research explores the impact of competence mapping and network position on the sustainability of small and medium enterprises (SMEs) using a decision tree approach, specifically the C4.5 algorithm. Through a survey of 203 batik SMEs in Indonesia, we assessed how external relationships and partnership capabilities contribute to sustainable business practices. The decision tree model identifies key factors influencing sustainability through competence mapping and network position. The C4.5 algorithm demonstrated the highest accuracy of 75% in predicting sustainability, highlighting the importance of strong relationship management and strategic competence mapping. These findings suggest that SMEs should focus on enhancing their role as reliable sources of information, fostering long-term partnerships, and adopting environmentally friendly technologies to achieve sustainability goals. This research provides valuable insights into sustainable development practices for SMEs in emerging markets.

In recent years, the value to customers has changed faster than ever due to the evolution of AI, such as LLM, and the proliferation of smartphones, and companies need to respond quickly to this change. Corporate R&D is no exception, and appropriate innovation management is essential. To instigate innovation, we extend the evaluation perspective of traditional software development projects, such as quality, cost and delivery to customer value and propose one example of indicators and frameworks for evaluating innovation activities. The performance of R&D organizations was evaluated based on actual data of FY2016. The results confirm the validity of the proposed methodology.

In today's competitive healthcare landscape, prioritizing quality and customer satisfaction is paramount, given the stakes of patient well-being and care excellence. This study delves into evaluating healthcare service quality in Iran, utilizing the renowned SERVQUAL model. By examining the five dimensions of tangibility, reliability, responsiveness, assurance, and empathy, significant gaps between patient expectations and perceptions are unveiled. The findings underscore the critical need for enhancements in care delivery timeliness and staff communication skills. Ultimately, this research contributes valuable insights for hospitals aiming to optimize patient-centered care and elevate the quality of healthcare services.

This study presents a comparative analysis of two machine learning models, decision tree and logistic regression, in predicting the outcomes of Dota 2 matches. Through rigorous evaluation using accuracy, precision, recall, and F1-score metrics, the study identifies the salient features influencing game results, including economic factors and strategic gameplay elements. The decision tree model exhibits a slight edge in overall accuracy and sensitivity towards positive outcomes, while logistic regression shows balanced predictive capabilities across both winning and losing instances. The findings reveal a nuanced understanding of each model’s strengths, suggesting their potential application in gaming analytics. With a focus on model performance in a complex, multifactorial environment, this study contributes to the strategic understanding and forecasting within competitive gaming domains.

In this study, we analyse the applicability of the A3 Architecture Overview (A3AO) method for technology assessment in a case company that operates Floating, Production, Storage, and Offloading (FPSOs). The primary objective is to enhance decision-making regarding technology selection, particularly within the framework of the "Fuel Cell Application for Next Generation FPSO Design" feasibility project. The authors conducted an iterative process involving the presentation of a high-level A3AO to engineers and managers to solicit feedback. Despite the conventional A3AO falling short of meeting expectations, its visual and concise attributes have potential merit for integration into future iterations that should have a more balanced approach between business and technological perspectives.

Product display is one of the key factors influencing consumer intentions and behavior in live streaming e-commerce. This study examines display attributes of live streaming in the agricultural field to analyze factors affecting consumer purchase decisions. Based on perceived value theory, this study constructs a theoretical model of consumer purchase decision influencing factors and uses structural equation modeling for data analysis. Results show that display authenticity, display visibility and cue multiplicity positively impact perceived value; display authenticity and display visibility enhance perceived entertainment; both perceived value and entertainment positively influence consumers' purchase intentions. On the basis of the findings, the live streaming display attributes should be improved in order to enhance consumer perceived values and purchase intentions.

Emergency decision-making requires perspectives from multiple decision-makers with different knowledge and experience to evaluate alternatives jointly and agree on the final solution. Researchers are developing the Large Group Emergency Decision-Making (LGEDM) framework, an assessment and evaluation process by 20 or more experts of alternative solutions in emergency cases. Trust relationship among experts participating in LGEDM is an important factor in the stages of LGEDM to reach the ideal consensus level. This literature review examines the development of research adapting trust relationships in the LGEDM framework, specifically from two perspectives (similarity-based trust and familiarity-based trust) and evidence of the effectiveness of trust relationships from previous studies. The study also identifies several opportunities from previous studies and provides recommendations for future studies.

Exchange-Traded Funds are essential instruments in global financial markets. Vanguard ETFs ranks as one of the most popular ETFs available. In exploring the potential of outperforming the market, this study investigates the efficacy of the extreme gradient boosting algorithm in the context of stock price prediction and portfolio optimization. The stocks considered for optimal allocation in outperforming the market include the top 10 stocks of the Vanguard ETF where the stock price predictions of those stocks are compared against historical data. The results indicate that while the extreme gradient boosting algorithm is effective in price prediction, its integration into portfolio optimization does not distinctly outperform its historical counterparts.

Conserving energy consumption and minimizing greenhouse gas emissions go a long way in improving the productivity and development of a nation and global community. Developing a model to handle both scenarios at the same time would be a plus to achieving desired economic development and social stability. Various models represent the attributes responsible for both energy consumption and greenhouse gas emissions. This study considered the Index Decomposition Analysis, Artificial Neural Networks and Data Envelopment Analysis in their various involvements to analyze energy consumption and greenhouse gas possible mitigation. Their involvement in the energy and greenhouse gas literature led to the proposed integrated model for this study with the three models for the purpose of conserving energy consumption and mitigating greenhouse gases at the same time.

With the increasing awareness of sustainability in the built environment, there is a pressing need to achieve a comfortable and healthy indoor environment with optimized energy consumption. In this context, artificial intelligence (AI) has shown its potential as a tool for energy optimization while upholding high IEQ standards. This research paper explores the current and future research trends in utilizing AI to achieve an energy-efficient indoor environment quality (IEQ). Bibliometric analysis is used as a methodology to identify key research themes and the thematic evolution of a research field. Based on a carefully formulated search term, a case study is performed using bibliometric data downloaded from the SCOPUS database. Upon data pre-processing steps, the research evolution of the field is presented visually using strategic mapping and thematic evolution networks over the years 2018–2023, with discovered insights discussed. Finally, some discussion on future works is given based on key insights.

Two types of technology development are conducted by firms: radical and incremental. While many studies have focused on the circumstances when firms actively conduct high-risk radical technology development, they have demonstrated that the more a firm’s technological performance rises above or falls below its aspiration, the more reluctant it is to try radical technology development. Moreover, some studies have suggested that the propensity of a firm’s technology development varies with the intensity of competition. Given these findings, our study aimed to clarify the following: 1) the impact of the difference between a firm’s technological performance and its aspiration on the firm’s effort to radical technology development; 2) how this impact varies with the intensity of competition, using patent data. Firms’ efforts to radical technology development were measured by the number of radical patents extracted using patent citation relationships. The performance and aspiration of firms were measured by the number of forward citations of their patents. This study revealed that the negative impact that occurs when performance falls below aspiration was stronger in more competitive technology areas.

Scaffolding is the first step as well as the last step in construction. Although the scaffold is not any part of the building, it plays a good role to safely finish the construction project. However, there have been accidents for scaffolders on installing the structure of the scaffold. Many studies have mainly focused on the accidents on construction project excluding the scaffolding. This paper investigates the industrial accidents on scaffolding in construction. This research focuses on the safety consciousness of the scaffolder. The safety consciousness may interact many factors including poorly behaviour, communications of scaffolders, and so on. This research uses a system dynamics simulation model to explore the interaction and dynamics among the factors and the safety consciousness of scaffolders. Numerical results provides the strategy of prevention of accidents on scaffolding.

As risk exposure in the supply chain increases, so does the imperative for an organisation to mitigate adverse consequences. All logistics operations are intricately connected to supply chain risk. As more businesses outsource their logistics operations to logistics service providers (LSP), the shipper or owner of the products becomes more susceptible to risk due to the LSP's lack of control. In response to the detrimental effects that supply chain risks can induce, the concept of supply chain risk management (SCRM) has surfaced. Cooperation among supply chain stakeholders is a prerequisite for SCRM. In light of the limited attention given to logistics service providers in SCRM analyses, this study examines the collaborative efforts between a carrier and a consignor in managing risks associated with logistics operations. This investigation employs the Soft System Methodology. The present study employed case studies methodology from ten manufacturing companies and fifteen LSPs to acquire insights into the collaborative process involving a manufacturing organisation and an LSP. Therefore, a conceptual SCRM model is constructed using the gathered data, which incorporates the risk management collaboration capabilities.

This study develops a predictive modeling approach for occupational ergonomic risk in manual lifting tasks to enhance real-time biomechanical risk assessment, aimed at mitigating occupational musculoskeletal disorders (MSDs). Wearable Electromyography (EMG) sensors were used to collect data on muscle activity of eight upper extremity muscles across ten participants engaged in both low-risk and high-risk tasks for MSDs. A machine learning model was developed and optimized for lifting task risk classification. The model shows high accuracy, precision, and recall values in classifying low and high-risk lifting tasks. This approach significantly surpasses traditional occupational risk assessment methods, which often rely on historical data and are reactive rather than proactive. The integration of wearable sensors with machine learning provides precise risk classification and facilitates the implementation of effective safety interventions across various occupational settings. This strategy has potential to improves safety planning and can contribute to substantial reduction in the occurrence and severity of MSDs, with ultimate goal of enhancing overall workplace safety and health.    

This article presents the preliminary results of a study that estimated the feasibility of implementing eye-tracking technology to evaluate the effectivenees of security screening operators. In the current system available at the airport, it is not verified that the operator marks the alarm randomly when he evaluates the X-ray scan image of the items transported in air transport. There may be a situation where the operator indicates the alarm with a different object in mind than the one that should searched for. This condition can interfere with the actual value of security screening operators' evaluation indicators. Thus, the system provides a result that does not indicate the exact effectiveness of the operator. This article proposes using eye-tracking technology to track eye focus points and verify this phenomenon.

Despite the increasing relevance of Occupational Safety and Health (OSH) and the growing knowledge in this field, the implementation of effective operational management of OSH remains complex due to continuous technological changes and the evolution of work contexts. To address this challenge, this study explores the introduction of an ecosystem perspective to enhance better OSH management across different – national, territorial, and local (company) – levels. Since this perspective is underexplored in the academic literature on OSH, the study examines ecosystem concepts from other fields, identifying five elements of circular ecosystems – Value, Actors, Data materials and flows, Circular activities and strategies, and Governance – that are also relevant in the OSH context. These elements have been integrated into the PDCA (Plan, Do, Check, Act) cycle to develop a research framework that has been corroborated by interviews with international experts to understand how an ecosystem perspective can improve the discussion on OSH prevention interventions.

This study examines the impact of mindfulness and reporting incentives on risk communication within Japanese high-reliability organizations (HROs). Eleven companies were categorized into three groups based on human and economic impact: Group I (high human and economic impact), Group II (low human, high economic impact), and Group III (low human and economic impact). Group I, including public and telecommunications infrastructure companies, emphasized risk reporting's effect on evaluations, strict manual adherence, and decentralized reporting. Group II, comprising banks and construction companies, had centralized authority and a culture promoting early reporting to supervisors. Group III, consisting of hotels, manufacturers, and advertising agencies, maintained traditional seniority-based systems with minimal performance-based incentives, relying on employee loyalty. The study reveals significant differences in risk management practices across groups and underscores the necessity for explicit risk communication and decision-making guidelines in Japanese firms, highlighting the need for industry-specific strategies to enhance mindfulness and incentivize reporting.

Work-related musculoskeletal disorders (WMSDs) are a critical concern for worker safety and productivity. This study proposes and develops a video-based work pose entry system for ergonomic postural assessment methods, specifically the Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA). Utilizing the YOLOv3 algorithm for human tracking and the SPIN approach for 3D human pose estimation, this system processes 2D video inputs to output RULA or REBA scores and the corresponding level of investigation and modification needed in the observed operations. Previous studies have relied on evaluator expertise, which is time-consuming and costly to acquire and subject to human error. To solve this problem, we conducted a study to improve the consistency of results. The system was validated through an experiment with 20 evaluators classified as experienced and novice based on their ergonomics knowledge. Results indicated that the system reduced differences and standard deviations between groups, suggesting improved consistency in ergonomic risk assessments and efficiency in the evaluation process.

Hybrid flowshop, as a flexible production mode, has been widely applied in various manufacturing scenarios. However, research related to hybrid flowshop scheduling often overlooks the scheduling of workpiece transportation within the workshop, focusing instead solely on the sequencing of workpiece processing and machine selection decisions. To enrich research in this area, this paper investigates a hybrid flow shop scheduling problem that includes shared-track transporting robots and considers conflicts in the paths between robots. To effectively address this problem, a Cauchy-mutation-based self-adapted multi-objective equilibrium optimizer (CSMEO) algorithm is proposed. Encoding and decoding are tailored to the specific characteristics of the problem to ensure the feasibility of the obtained scheduling solutions. Numerical experimental results indicate that the performance of CSMEO surpasses that of comparative algorithms in solving the problem presented in this paper.

– Multi-hole drilling stands as one of the critical machining processes in various manufacturing sectors. Sequencing non-productive drill tool path in multi-hole drilling poses a complex combinatorial challenge. Consequently, Researchers have relied on metaheuristic algorithms or their hybridized or modified versions to optimize drill tool paths. This paper utilizes a recently enhanced version of the Bees Algorithm (EBA), which is yet to be explored to optimize drill tool path sequencing. A well-established benchmark problem is used for optimization and outcomes are compared with the basic version of the Bees Algorithm (BA) using different parameter sets. Further, it compares the outcomes with the other highly successful algorithms in multi-hole drilling domain.  

To adapt to the real-life production environment, cell formation problems (CFPs) and cell layout problems (CLPs) with square constraints are simultaneously optimized. Firstly, CFPs and CLPs are formally described. To increase the accuracy of the inter- and intra-cell layout design, the coordinates are adopted and the material handling cost is calculated in terms of the actual position of machines within the cells and regarding the dimensions of the machines and aisle distances. To deal with the proposed problem, an improved multi-objective grey wolf algorithm with non-dominated sorting is proposed. Simulation experiments indicate that the proposed improved algorithm is feasible and effective.

Small and Medium-Scale Industries (SMIs) play a significantly important role in the nation's economy. The adoption of current technology is necessary for SMIs to remain agile amidst industrial changes or challenges. However, they often face limitations in utilizing technology, particularly information technology, in their business processes. One popular integrated information system is the Enterprise Resource Planning (ERP) system, which connects various business units within an organization into a single integrated system. The implementation of ERP systems in small and medium-scale industries is necessary to enhance the effectiveness and efficiency of their business processes. In this paper, we conduct a systematic review of the literature discussing the adoption of open-source ERP systems in SMIs. The conclusion drawn is that ERP implementation in SMIs occurs across various sectors, including services, trading, and manufacturing. Several forms of research related to the adoption of open-source ERP systems in SMIs exist, ranging from system usage analysis to identifying suitable open-source types and system development.

In labor-intensive cell production systems, it is important to accurately identify and effectively train operators' skills. Learning models that simulate proficiency are used to predict operators' skills. In reality, however, there are adverse effects due to fatigue accumulation. In this study, we compare several learning-fatigue models with the learning model and evaluate their features to propose a more realistic operator allocation. Then, we perform a computer experiments of task assignment for the purpose of skill education and compare the results of each model. The results show that the learning-fatigue model is highly practical for a variety of learning and fatigue patterns. This study aims to contribute to the optimization of operator allocation in practical workplaces, ultimately enhancing productivity and quality in manufacturing processes.

With the advancement of the modern manufacturing industry, fault diagnosis has become increasingly critical, especially for rotating machines operating under varying working conditions. While numerous deep learning-based methods have been proposed, they often require extensive labeled data for training, which is challenging due to data scarcity and limited label availability. Moreover, their performance tends to deteriorate when applied to different domains. To overcome these issues, this paper introduces an intelligent fault diagnosis technique that leverages L2 regularization and deep transfer learning with LSTM networks, capable of adapting to different environments. The approach involves a pre-training phase followed by fine-tuning, where knowledge from the pre-trained model is transferred and adjusted for new working conditions. The study finds that fine-tuning all layers of the model results in minimal variation, with accuracies within 0.05%, indicating high consistency. In contrast, fine-tuning only the final classification layers shows a broader range of accuracies, approximately within 6%, indicating moderate consistency­—a conclusion further supported by t-SNE feature visualization.

Business process management has become a critical focus for many manufacturing industries. The function of business process management is to achieve organizational goals through the improvement, management, and control of business processes. A crucial step for companies in visualizing, analyzing, and optimizing workflows is business process modeling. BPMN (Business Process Model and Notation) is a tool widely used to visualize business process flows. This study employs a qualitative approach, using interviews conducted with seven small and medium-sized industries (SMIs). Important findings were identified in SMIs, particularly related to record-keeping processes, both upstream and downstream, which are still done manually. Regarding the processes on the production floor, in addition to paying little attention to implementing record-keeping systems, SMIs also tend to neglect forecasting methods, warehouse management, and the use of material handling.

This review study investigates the effects of fly ash as a replacement for Portland cement with an eye on how it would affect the mechanical properties and durability of concrete. Fly ash is used in sustainable cement manufacture more and more to lower environmental impact and maintain concrete performance. We evaluated
concrete samples with different fly ash replacement levels for tensile, flexural, and compressive strengths. Our measurements of the durability criteria included drying shrinkage and chloride permeability. We also evaluated potential CO2 emissions from substitute fly ash. Measurements of quality provide constant observation of fly ash properties. Fly ash replacement levels exceeding 30% reduced compressive strength by 27% seven days after cure. At replacement levels up to 30%, flexural strength did, however, considerably increase. By displaying lower chloride permeability and less drying shrinkage than control mixes, concrete—including fly ash— increased durability. Additionally producing a minimum of a 62.73% drop in CO2 emissions, fly ash substitution helps to reduce the carbon footprint. Even with the environmental benefits and durability enhancements, the differences in fly ash quality need for strict quality control and mix design adjustments to guarantee constant performance. Future research should focus mostly on standardising hybrid cement manufacturing methods to maintain performance and quality in applications of sustainable concrete.

The rapid growth of e-commerce has posed logistical issues, especially in urban areas. Although an innovative distribution option called Self-Collecting Delivery has been suggested for last mile delivery, its acceptance rate remains relatively low when compared to Attended Home Delivery. The current study seeks to compare consumer attitudes towards Attended Home Delivery vs Self-Collecting Delivery. The Fishbein multi-attribute attitude model was utilized to assess the overall attitudes by incorporating eleven last-mile delivery factors derived from current research.  A quantitative survey was conducted, encompassing 330 participants who engage in online purchasing. The results suggest that each of the characteristics associated with last mile delivery has a beneficial effect on the attitude towards both Attended Home Delivery and Self-Collecting Delivery. The general perception of Attended Home Delivery was shown to be considerably more favorable compared to Self-Collecting Delivery, which accounts for the slower acceptance of Self-Collecting Delivery. The paper also examines the managerial implications and identifies potential areas for future research.

This study examines the alignment of Supply Chain Management (SCM) strategies with governmental policies to advance the Sustainable Development Goals (SDGs) in the Thailand context. Employing a two-stage approach, the Q-Sort method was utilized to categorize SCM policies based on expert consensus, effectively organizing these into groups reflective of their relevance and impact. Subsequently, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was applied to analyze the causal relationships among policies, categorized into three SCM strategy groups: digitalization, integration, and nearshoring. These strategies are pivotal in advancing SDGs related to innovation, responsible production, climate action, and global partnerships, specifically contributing to SDGs 8-9, 11-13 and 17. The findings highlight the significant influence of digitalization on other SCM strategies, emphasizing its role as a cornerstone in enhancing connectivity and efficiency within supply chains. The research provides valuable insights into the strategic SCM in emerging economies, offering a scalable model for integrating sustainability into supply chain operations, with broad applicability across various industries.

The daily operations of courier companies are essential for modern trade in the Philippines, supporting e-commerce and logistics. Flash Express a logistic company in San Jose, Occidental Mindoro is one of the leading courier companies, and this study focuses on analyzing its sorting process and presenting solutions for improvement. The research employs Lean Six Sigma techniques and tools such as the Flow Process Chart, Ishikawa Diagram, and Pareto Chart to enhance the delivery process and achieve optimal performance. This research demonstrates how implementing a continuous improvement mindset, and data-driven methodologies may lead to efficient delivery services that provide clients with unparalleled value.

Every year, natural catastrophes, such as floods and earthquakes, affect diverse parts worldwide. In addition to preparing sufficient supplies and distributing relief goods, including food and clothing, one of the most vital issues is medicine. Hence, designing a proper and practical drug supply chain is necessary before the crisis. The supply chain in this domain is drugs storage in the local warehouses to provide the affected population drugs without shortage at the time of the disaster and after it. The current study considers supply chain modeling decisions related to initiating the essential facilities and the number of drugs stored in them, ‌‌ pre-crisis and post-crisis drug distribution decisions.‌ The ultra-innovative algorithm optimization of particle swarm is used to the model resolution. Comparing the results with the purpose of the meta-heuristic algorithm with the exact method shows that the proposed performance algorithm can compete with the exact method in minor size predicaments. The best achievement of the ultra-innovative algorithm is shown in vital problems that cannot be solved using accurate methods at the appropriate moment.

The semiconductor industry, a linchpin in technological advancement, operates within a highly intricate and interdependent supply chain ecosystem. The improvement of supply chain efficiency in the semiconductor fabrication sector is crucial, given its pivotal role and the complexity of its interconnected supply chain involving many other suppliers and vendors. Employing machine learning models and operations research methods, the primary goal of this research is to enhance efficiency in semiconductor supply chain operations. Python is utilized to analyze open-source datasets, facilitating demand forecasting and delivery time optimization to alleviate logistical challenges. The comprehensive methodology covers exploratory data analysis, data preprocessing, feature selection, and the application of machine learning models. These methodologies, while focused on the semiconductor fabrication sector, could be applied to the whole phase of semiconductor industries, making this research both innovative and broadly applicable.

After a disaster, there's often a disparity in the distribution of supplies among affected areas, with some experiencing excesses while others face shortages. To support the affected people more effectively with limited resources, we propose a multi-period, multi-resource distribution model that considers the shortages and inventory levels  in disaster areas. This model integrates location, inventory, and flexible allocation of relief supplies for dynamic supply planning.  To illustrate the use of our model in practical operation, we conduct numerical experiments. The results show the fulfillment or unfulfillment, and inventory conditions of multiply supplies for affected people groups during each sub-period. Additionally, a sensitivity analysis also demonstrates the impact of the penalty coefficient on the model.

The prevalence of low carbon and environmental protection philosophy and the increasing importance of sustainable supply chain development have rendered cold-chain logistics increasingly significant. This study addresses the optimal route planning challenge from warehouses to distribution centers, focusing on minimizing costs and carbon emissions in cold-chain logistics. A novel vehicle route planning model for cold-chain logistics was developed, targeting cost savings and reducing carbon emissions. Genetic algorithms and ant colony optimization were employed to determine the optimal route, demonstrating effective performance in solving the optimization problem. The study achieved a more optimal distribution path by combining the rapid convergence of ant colony optimization with the computational efficiency of genetic algorithms. Experimental results validate the enhanced algorithm's ability to swiftly obtain an optimal cold-chain logistics distribution scheme at a lower cost. This research contributes to cost reduction in distribution and significantly lowers carbon emissions, thereby yielding dual economic and social benefits.

This paper examines the barriers and gaps affecting the export of agricultural products from India to the European Union (EU). Despite the economic importance and potential benefits of this trade corridor, several impediments hinder its full exploitation. Utilizing a comprehensive literature review and case study analysis, this study explores the existing trade agreements, successful exports, and key obstacles faced by Indian agricultural products in the EU market. The methodology includes a detailed review of peer-reviewed articles, government reports, and industry data from both Indian and EU sources. Key barriers identified include stringent phytosanitary and sanitary measures, tariff and non-tariff barriers, and logistical challenges. A comparative case study highlights the differential success of similar products from other nations, underscoring best practices and strategies that have overcome similar challenges. The paper concludes with strategic recommendations for policy adjustments and interventions aimed at enhancing the competitiveness of Indian agricultural exports. This research not only identifies critical gaps in current studies but also proposes actionable solutions to stakeholders in the India-EU trade corridor, aiming to facilitate smoother and more beneficial agricultural trade.

The generation of e-waste as a result of technological advancements and increased smartphone use has led to significant environmental issues. This study aims to investigate customer behavior toward smartphone replacement and their way of handling their old smartphones. The empirical survey involving 341 participants in Yogyakarta, Indonesia, was conducted. The findings showed that the most motivation to replace smartphones was due to non-functionality (62%), followed by a need for better specification (30%). Nevertheless, 53.37% of the respondents keep their end of useful life smartphones, and only 8.21% of the respondents handed over to the recycling unit. It appears that financial incentive is the perceived driver to encourage consumers to return their old smartphones to the collection center. Potential future researches are also discussed.

Blockchain technology, renowned for its robust security and immutability, places the responsibility of safeguarding credentials on users. However, the loss of cryptographic keys and credentials without recovery methods poses significant challenges in accessing wallets and reclaiming blockchain identities. Digital wallets are crucial for identity management and secure administration of cryptographic keys and credentials. This paper introduces an optimized key recovery model for blockchain-based digital wallets, leveraging a cascade blockchain framework to facilitate wallet recovery through seed phrases. The study aims to empower stakeholders by providing them with the means to regain control over their credentials, thereby enhancing security and resilience in Supply Chain Management practices.

The increasing penetration of electric vehicles has led to two major challenges. Electric vehicles with batteries that have reached their end-of-life require a  replacement battery. Additionally, an increasing number of waste batteries are accumulating. While these are common challenges worldwide, developing countries face greater uncertainty and faster waste accumulation due to the rapid electric vehicle imports. Numerous end-of-life practices can be adopted to improve the circularity and sustainability of electric vehicles. The most common end-of-life strategies are recycling, remanufacturing, and repurposing. Implementing these various strategies is complex and requires significant investment. In this paper, end-of-life strategies are investigated for Jordan so as to guide infrastructure and policy development. TOPSIS method is adopted to allow for a holistic multicriteria guide. Meanwhile, the revenue potential is modeled and used as one of the input criteria for the TOPSIS method. Data is analyzed to study status quo of Jordan and project electric vehicle spent battery accumulation. Afterward, the revenue potential from recycling, remanufacturing, and repurposing is calculated.  In all, various end-of-life strategies showed promising revenue streams and a roadmap for Jordan is proposed.

Agri-food supply chains in developing economies heavily rely on small and medium-scale farmer communities, and they are facing financial issues. Many developing countries are finding solutions through microfinance for this issue. The study is focusing on identifying the risks involved in the life cycle of the loan process from both microfinance institutions’ and rural communities’ perspectives, and proposing risk mitigation strategies that can be integrated throughout this process through a comprehensive risk assessment. The identified main risk categories are credit risk, interest rate risk, liquidity risk, operational risk, and regulatory risk from the microfinance perspective. From the perspective of the rural farmer community, involved risks are access to credit, production risks, market risks, and social and economic risks. The risk register method has been employed to assess the identified risks, propose risk mitigation strategies, and measure the residual risk after integrating the mitigation strategies. This study will help policymakers and researchers develop more effective microfinance operations that benefit both the microfinance institutions’ and rural communities' efforts to develop a more sustainable system through change management.

This study aims to map out supply chains in industrial estate research and their distribution, which can be used as a reference for supply chains in industrial estate research. Integrating supply chain management and industrial estates can improve the operational efficiency of industrial estates and increase the industry's competitive advantage and company performance. This study was conducted in 2022 and explored supply chains in industrial estate literature using bibliometric analysis. The data source was obtained from the Scopus database, with keywords related to supply chain and industrial estate. The visualization results showed the research trends,  paper distribution, insights, and impact of the supply chain in industrial estate from various domains. This study still has shortcomings, including the fact that the database source only uses Scopus, and many visualization results using VOSviewer can be explored further.  Keywords – bibliometric analysis, industrial estate, supply chain, systematic literature review, VOSviewer

This study provides an in-depth analysis of recent advancements in the production of green and blue hydrogen, its utilization in road freight transportation (RFT), and the global establishment of hydrogen refueling stations (HRSs). This paper assesses the advancement of hydrogen production projects globally, detailing their completion years, annual production capacities, distribution methods, collaborative partnerships between countries, and other relevant factors. Furthermore, we have investigated the contemporary trend of involving the adoption of hydrogen fuel cell vehicles (HFCVs) in RFT, aimed at reducing carbon emissions in transportation. Through analysis of 569 articles retrieved from the Scopus database spanning 2015 to 2024, conducted using VOSviewer®, we observed a notable uptick in the utilization of HFCVs in RFT. Additionally, we compiled a list of countries at the forefront of hydrogen production and utilization. Through a synthesis of recent literature and case studies, this study provides valuable insights into the evolving landscape of hydrogen application in RFT, offering practical recommendations for stakeholders aiming to promote sustainable transportation practices.

This study was dedicated to simulating the impact of defective rates on the bullwhip effect within the context of a supply chain, with a specific focus on a case study involving reciprocating compressor manufacturing. The supply chain configuration encompassed a distributor, a factory, customers, and a remanufacturer. Customer demand was forecasted utilizing the exponential smoothing method, while an order-up-to inventory policy was implemented. The results unveiled a direct correlation between an increase in defective rates and a heightened bullwhip effect. Moreover, the study demonstrated that an elevation in the average yield rate corresponded to a mitigation of the bullwhip effect.

AXY Company is a key player in the African agri-business sector, specializing in packaging sugar. Despite a robust legacy, the company struggles with supply chain inefficiencies, particularly in waste management. This paper investigates these inefficiencies using lean manufacturing principles, focusing on root causes and proposing solutions for enhanced supply chain performance. Through methodologies ABC analysis, significant waste was identified in the 500g and 1kg SKUs, accounting for 79% of total material waste. Key interventions include improving material handling, enhancing quality control, and optimizing machine performance. The findings provide a roadmap for reducing waste costs and achieving operational efficiency. Future research should focus on implementing these strategies across various SKUs and enhancing supplier collaboration to further minimize waste and optimize supply chain performance.

The use of energy has generated the greenhouse gases leading to the global warming impact on earth. Effective use of energy has become necessarily among countries as according to Paris Agreement put in force in Nov 2016. Total 196 countries have committed to achieve peak in Greenhouse gases emissions peak in 2025 and declined by 43% in 2030. In Hong Kong, the lighting facilities has remained the second highest in electricity consumption. Although the change of lighting technology from conventional fluorescent lights into LED have improved the energy efficiency and the performance of operational life time significantly, however, the energy reduction only shows 2% when compare the Government figures between 2011 and 2021. The vast amount of lighting quantity in buildings still contribute a high portion of energy consumption. This research paper is the study to further improve the lighting efficiency rather than LED technology itself, but through the operation, maintenance and control in associate with smart sensors, intelligent asset management and operational decision support, in react with human behaviors for building automation and optimization of energy performance in buildings.

The constant scheduling policy is commonly adopted in appointment scheduling to manage customer arrivals. To analyze the performance of a constant scheduling policy, this paper employs a queueing model. We find that the constant scheduling policy achieves optimality when the system reduces to a deterministic one and the slot duration aligns with multiples of the service time. However, in presence of randomness, the constant scheduling policy tends to be suboptimal. To address this, we introduce bracket scheduling policies that outperform the constant scheduling policy. Furthermore, we observe that policies generally perform better with less randomness in the system. Based on our findings, we provide suggestions to system managers to promote operational efficiency.

Facility Layout Problem (FLP) is an optimization problem focused on configuring facilities to optimize specific objectives. This study employs NSGA-II, a non-dominated sorting genetic algorithm, to solve a multi-objectives UA-FLP model considering distance, adjacency, and space utilization ratio. Additional department orientation is included in the algorithm to enhance solution quality in the solution searching process. This orientation information considers that the width and height of a department can be interchanged, allowing for flexibility in its layout configuration. It is found that taking orientation into consideration results gives better solutions. Based on hypervolume evaluation, one-point and two-point crossover in the referred NSGA-II shows no significant difference for both performance and solution produced.

With the growing need for more efficient, affordable, and sustainable shipping, this paper creates a model that addresses the periodic vehicle routing problem. Considerations are made for a mixed heterogeneous fleet, consisting of various models of electric and diesel vehicles. Longer service windows are also considered to account for deliveries that can be made over multiple days. With that customers of overlapping service windows can possibly be serviced using the same route. The formulated model will create a route that will satisfy all demands with the objective of maximizing profit, while assigning vehicles to specific routes, in consideration of their capacities. The results of the model showed that the considerations of a hybrid fleet and multiple day service windows would result in decreased fuel cost and distance traveled. Further scenario analysis also provided insights on the price fluctuations of energy and diesel and how it may play a role in fleet mix deployment.

In this paper, we address the bin packing problem while minimizing the total loading cost of used bins. We focus on two different quantity discount schemes: the all-unit discount and the incremental discount. For both schemes, we take into consideration the time compatibility between items so that items sharing the same time window are assigned to the same bin while satisfying the bin capacity constraint. We propose then a mixed-integer programming (MIP) formulation. We prove that the problem is NP-hard for both discount schemes. To assess the model’s performance, we conduct numerical experiments. The results show that the proposed approach proves its effectiveness by providing optimal solutions whose computation time increases with the number of items.

This study conducts a computational analysis to assess the feasibility of electric regional air networks in Northern Scandinavia, leveraging an Integrated Flight Scheduling and Fleet Assignment Network Optimization Model for integrating electric-powered aircraft within Public Service Obligation (PSO) frameworks. Utilizing Matlab for model development and Gurobi for optimization, the research focuses on minimizing operating costs in compliance with PSO requirements. It thoroughly examines operational needs, cost implications, and the effects of electric aircraft on regional connectivity and emissions. Special attention is given to the model's sensitivity regarding electric aircraft charging times and to exploring network restructuring possibilities for improved efficiency. This work contributes significantly to discussions on sustainable regional air transport, offering a novel computational method to evaluate electric aviation's viability and economic potential in areas with sparse populations.

3D printing (3DP) technology is claimed as a potential solution to address challenges in spare part supply chains (SPSCs). Unlike conventional manufacturing, 3DP facilities can be adopted by the original equipment manufacturer (OEM), third-party service providers (TPSPs), or even customers. However, the total cost and customer satisfaction vary in accordance with the location of 3DP in different facilities. Thus, stakeholders should jointly make decisions on the 3DP adoption problem. In this study, we examine the joint decision-making mechanism involving an OEM, a TPSP, and an airline in the aviation industry. Considering the decision-making process, the OEM acts as the leader, and the TPSP and the airline act as two individual followers. Subsequently, we develop a bilevel programming to optimize both the total cost and customer satisfaction level of the OEM and the total profits of the TPSP and the airline. We further propose a deep reinforcement learning-based solution method to solve the bilevel model. We conduct a case study to demonstrate the applicability of our model and solution approach, and we arrive at important managerial implications with sensitivity analysis.

Construction in the unorganized sector is generally done on the basis of past experience of the project managers. Ordering materials for different stages is one such activity that is carried out as per the tentative completion of the predecessor activities. Experience is an important factor in decision-making. However, an approximation can sometimes lead to a deviation from desired results that may substantially increase project costs and extend the project completion time. In this paper, the experience of project managers regarding project completion has been linguistically recorded and used to quantify and formulate an ordering policy model using the Fuzzy Mamdani approach. The proposed model provides the required output promptly when instantaneous inputs are fed into the model. The data set utilized to showcase this approach has been provided by a small construction firm in North Delhi, India and a framework for ordering policy is proposed. It aims to determine the ideal time for ordering a certain quantity of material. This study aims to minimize subjective decision-making in order placement, assisting professionals in reducing inventory storage.

Work-related injuries, particularly musculoskeletal disorders (MSDs), incur significant costs for companies in terms of sick leave and reduced productivity. Maintaining correct ergonomic posture is crucial to prevent these injuries and mitigate the impact of psychosocial factors. Digital technology plays a vital role in creating efficient and flexible work environments that cater to individual needs. Rather than relying solely on specialists, workers can utilize digital applications to prevent workload and strain injuries. This study investigates the effectiveness of a digital AI-based intervention program aimed at preventing work-related injuries and improving the physical work environment by addressing musculoskeletal disorders caused by incorrect postures. Through interviews with tool users in an industry setting, a web-based prototype application was tested to enhance workplace safety and improve physical health. The application employs digital AI tools to provide real-time feedback to workers. The interviews specifically assess how users evaluate and effectively utilize the tool to enhance working postures and the overall work environment. The study seeks to evaluate the efficacy of the digital AI-based intervention program and gather insights on users' perceptions and utilization of the application. This research has the potential to contribute to a safer and healthier workplace by harnessing the power of technology. The study seeks to evaluate the efficacy of the digital AI-based intervention program and gather insights on users' perceptions and utilization of the application.

This study investigates the influence of immigration-related stress on the nursing work performance (NWP) of overseas qualified nurses (OQNs) in Japanese healthcare institutions, in response to the global nursing shortage. It also offers policy recommendations for healthcare administrators. A national questionnaire survey was carried out from September 2023 to January 2024, with 214 valid responses collected. The survey evaluated the demographic profiles of OQNs, immigration-specific stress using the Demands of Immigration (DI) scale, and nursing performance utilizing the Nursing Performance Instrument (NPI) scale. The results revealed that factors such as residential condition and immigration-specific stresses associated with 'Loss', 'Novelty', and 'Language' have a significant impact on NWP. The study suggests that supporting living arrangements, enabling family accompaniment, addressing novel challenges, and providing ongoing language training are crucial for enhancing the NWP of OQNs.

The dynamic pattern of inpatient admissions at any hospital is a critical area of concern as it deals with managing with limited resources. Accurate forecasting is crucial in such situations. This study attempts to predict monthly inpatient hospital admissions using the data of a district hospital from January 2021 to June 2023 using three machine learning models: random forest (RF) regressor, support vector regression (SVR), and extreme gradient boosting (XGBoost); and with two conventional forecasting models: seasonal autoregressive integrated moving-average (SARIMAX) and linear regression (LR). This study aims to discern the most precise model for predicting monthly inpatient admissions. Employing Shapley additive exPlanations (SHAP) scores, the study seeks to identify the most influencing features impacting the prediction of inpatient admissions. The findings reveal that the XGBoost model emerges as the most accurate model, followed by the RF Regressor and SVR. SARIMAX ranks as the least accurate among the models considered. Notably, the rolling average, with a window size of 7, exerts the most significant influence on inpatient admissions, followed by the day of the week, lag factors, and temperature.

This study explores master surgery scheduling at the operating room (OR) tactical level, focusing on managing uncertainty in surgery durations. The aim is to optimize the allocation of surgeons to three types of operating room (OR) time blocks and to determine the number of surgeries scheduled. Given the limited historical data on surgery durations, we employ a distributionally robust optimization (DRO) approach to address the uncertainty in the distribution. To address the needs of different OR managers, we develop a distributionally robust chance-constrained model to manage overtime that extends beyond the designated OR time blocks. Meanwhile, we construct a distributionally robust bi-objective optimization model with the goals of minimizing the expected total duration of overtime and maximizing the number of surgeries performed. These optimization models are reformulated into computationally tractable forms using duality theory. We validate the proposed methods with real hospital data, finding that the DRO approach offers greater stability in scheduling solutions compared to the sample average approximation method.

The aim of the research is to identify the main factors for developing the biopharmaceutical CDMO ecosystem. In addition, the evaluation criteria for biotech CDMOs building digital resilience are also analyzed. The data collection and analysis of this study are divided into two stages. First, the study employed semi-structured, in-depth interviews to collect valuable insights from key industrial KOLs. Then the expert questionnaires were distributed to conduct a survey of experts in Taiwan's biopharmaceutical-related industries. The main targets include a total of 60 experts from CDMO manufacturers, process equipment and solution suppliers, and MAH manufacturers. The conclusion indicates that the strategic development factors for biotechnology latecomers to catch up via regulatory and smart manufacturing paths.

This study is based on real field development background and practical experience in the construction of remote medical systems in rural areas, and analyzes the implementation and evaluation issues in rural areas. The research team includes telemedicine experts and M.D. to conduct empirical analysis. The study adopted a three-stage approach and mixed research methods for analysis. The qualitative interviews and expert questionnaires are employed to data analysis. The results pointed out that in the construction of a telemedicine platform, should consider the maturity of the technology, the operational process, and the familiarity of professional users. The conclusion section puts forward short-term and long-term development strategic suggestions for the construction of a telemedicine platform in rural areas.

The dynamic nature of human behaviour poses challenges for behavioural anomaly detection models that can be impacted by concept drift. This experimental study employs the Aruba real-world dataset obtained from CASAS to examine the effectiveness of using Change Point Detection and Batch Learning in adapting Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Autoencoder models. Results demonstrate that the proposed approach surpasses the baseline of no adaptation, yielding an average improvement of 10.46% for DBSCAN, with a performance of 3.96% points higher than the benchmark regular adaptation. Similarly, Autoencoder achieves an average improvement of 4.01%, with a performance 4.11% points higher than the benchmark. The findings suggest the need for increased attention to address concept drift in behavioural anomaly detection and highlight the potential benefits of enhancing detection capabilities in the presence of concept drift.

The decision to purchase health insurance policies is a common strategy to manage the escalating costs of medical treatment. This study aims to statistically identify the key factors determining health insurance premium prices. A variety of methods were applied, including Ordinary Least Square Regression (OLS), Ridge Regression, Lasso Regression, and Support Vector Regression (SVR), to determine the most suitable model for predicting premium costs. The analysis focused on multiple factors such as age, gender, Body Mass Index (BMI), number of children, smoking status, and region. OSL analysis revealed that age, BMI, number of children, and smoking status positively affect the value of health insurance. Also, it has been shown that the prices vary with respect to regions, while gender is not a significant determinant of charge. Smoking status has the highest impact, while age is the least, and BMI and region are almost the same. Among the methods tested, Support Vector Regression (SVR) demonstrated the lowest Root Mean Square Error (RMSE) of 0.84, indicating it provided the best fit for predicting health insurance costs based on these variables. The findings highlight SVR as an effective tool for estimating health insurance premiums, offering insights into how various personal and demographic factors influence the cost. The results contribute to a deeper understanding of the key drivers that help customers anticipate future costs and allow insurance companies to adopt a more precise tool for pricing more tailored, data-driven premiums.

In 2022, leukemia is projected to be the leading cancer case globally among children and adolescents, as indicated by the age-standardized rate (ASR) incidence and mortality index. This study aims to identify and predict the ASR incidence and mortality of leukemia using statistical and machine learning approaches, including Generalized Linear Model (GLM), Regression Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). Evaluation metrics used were Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetrical Mean Absolute Percentage Error (SMAPE), and Mean Relative Absolute Error (MRAE). GLM and XGBoost emerged as the best-performing models for ASR incidence, achieving the lowest SMAPE (36.273%) and MRAE (35.870%) scores. For ASR mortality, Random Forest was the top performer with the lowest SMAPE (37.733%) and MRAE (28.395%) scores. Further analysis using the Shapley Additive exPlanations (SHAP) method was conducted to determine the impact of each factor on the models. However, the analysis showed unsatisfactory outcomes due to missing values and the limited number of variables.

Public transportation systems play a vital role in urban mobility, but delays pose significant challenges, impacting passenger satisfaction and trust. This study addresses delay prediction using General Transit Feed Specification (GTFS) data comprising static and real-time information. We explore five machine learning (ML) models' effectiveness, including Gradient Boosting, Random Forest, Support Vector Machines (SVM), Neural Networks, and k-Nearest Neighbors (kNN). We discuss issues such as data complexity, limitations, and model interpretability in delay prediction. Our comparative analysis evaluates these models based on predictive accuracy. SVM is consistently accurate with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), while Neural Networks and Gradient Boosting show strong performance. Random Forest and kNN exhibit limitations. This research emphasizes the importance of accurate delay prediction and interpretable models for transportation management. The findings aid stakeholders in selecting suitable methods, contributing to improved service quality and increased public trust in transportation systems.

Electricity demand forecasting is a classical but critical issue owing to the surging wave of artificial intelligence. Today, electrical vehicles and large-language scaled AI servers further boost strong electricity demand. Although numerous studies have been presented, the impacts of feature engineering are not addressed. This research presents a novel framework to achieve the following goals: (1) economic indicators and metrological factors are collected as potential predictors, (2) principal component analysis (PCA), discrete wavelet transform (DWT), and autoencoder-decoder system (ADS) are compared to improve the predictive performances, and (3) the embedded approach is used to reveal managerial insights of capacity planning for energy supply. Energy sectors in Taiwan consist of manufacturing, business, household, and etc. Experimental results show that key performance indicators (KPIs) include amounts of export, seasonal factors, air pollutant, industrial production index, and average temperature. In feature engineering, DWT outperforms other methods while PCA performs the last. Except for PCA, deep learning (RNN, GRU, LSTM) generally outperforms machine learning (RT, RF, XGB) in demand forecasting. Based on the identified KPIs, governments can prepare supply capacity to fit demand forecasting.

Principle Component Analysis (PCA) is an effective approach for reducing data dimensions. However, it faces challenges with outliers and lacks interpretability due to its dense principle components. To address these issues, this work presents a novel PCA technique that improves robustness and sparsity simultaneously. The Huber loss function is applied to protect the analysis from the negative impacts of outliers. And we add a non-convex penalty to create sparse loadings, making the components easier to interpret. This dual strategy not only strengthens the method's robustness against outliers without the requirement for pre-identification of outliers, but also improves the interpretability by reducing bias in the sparse principal components.
To put this strategy into practice, we developed an effective iterative algorithm for solving the difficult optimization problem at hand. Extensive simulations and practical applications demonstrate that our method consistently outperforms current PCA methods. Our method produces more accurate and robust estimates, particularly in cases where data contamination is present. This development in PCA methodology allows for more robust and interpretable high-dimensional data analysis, making it an important addition to the statistical toolset.

Distribution scale is crucial to the profits of movie theaters. The distribution scale decision or seat scheduling problem has been dealt with in many studies, but there is a limitation in that it assumes the audience prediction and distribution strategy independently. In particular, the forecasting models of existing scheduling studies do not sufficiently reflect the competitive environment where multiple movies are screened simultaneously or the word-of-mouth effect among the audience. This study deals with the profit maximization problem based on a forecasting model that considers not only the seat size but also the competition and word-of-mouth effect from the perspective of the movie theater chain. A forecasting model with competitive variables that reflect the relationship between movies and the word-of-mouth effect using social media mentions is established.
Based on this, the expected box office according to the number of seats for each movie at the decision point is derived, and an iterative algorithm for profit maximization is proposed. This study is significant in that it optimizes the limited distribution scale by utilizing the interrelationship between distribution and audience.

The film industry had been severely affected by the COVID-19 pandemic due to restrictions on outdoor activities. This study aims to examine the structural changes in the Korean film market from the distributors' point of view: movie theater chains. A distribution decision-making model is established considering the distribution scale previously determined and the subsequent box office, as well as the competitive and word-of-mouth effects which reflect the audience's theater experience. Competition variables are defined using qualitative and quantitative data on other titles playing during the same period as the target film. For the word-of-mouth effect, social media data about the title and related topics are gathered and evaluated. The results show that the film market experienced distinct structural changes at both the beginning and the end of the pandemic. Specifically, the influence of exogenous variables on decision-making changes. The contribution of this study is the development of a distribution decision-making model with multiple variables that reflect the audience's theater experience. The proposed model and the detected structural changes contribute to a deeper and richer understanding of the film market.

In the current scenario, most of the vehicles are connected with telematics devices and internet-based applications that generate large amounts of data, sparking interest in designing a decision support system using big data analytics. This research work explores the opportunity to design a decision support system for the transportation sector. The model uses the transportation data and makes decisions to solve the transportation problems of cities. The decision support system derived from this research work is suitable for strategic planning for urban areas. Smart transportation systems not only manage the traffic of cities but also have a positive impact on the air quality index of urban areas. Furthermore, by optimizing routes and reducing delivery times, these systems can significantly improve the efficiency of the supply chain. The cases this research covers provide managerial insight into transportation, air quality, and supply chain. Keywords: Big data, Smart transportation, decision support system.

Earthquakes are one of the major causes of railroad infrastructure damage in active tectonic regions. This study investigates the relationship between railway infrastructure accidents and seismic hazards of 26 counties in California, an active tectonic region in the US. Employing spatial analysis and Spearman Correlation methods, the study found that over 15 years,  M4 earthquakes occurring at depths ≤ 40 km strongly correlate with infrastructure accidents. While smaller earthquakes (3 ≤ M < 4) do not strongly correlate with the overall accidents, they exhibit a moderate to strong correlation with specific issues, e.g. defects of rail switch and structure. This indicates that small and shallow earthquakes, which occur repeatedly in the same areas, can cause long-term damage. The study's results lay the groundwork for creating more sophisticated models to depict the relationship between railway accidents and seismic hazards, which could help predict future damage and its causes.

The Philippine Stock Exchange (PSE), established in 1927, has significantly evolved, especially since the 1980s with major reforms and technological advancements. This paper analyzes the performance of portfolios in the Media, Telecommunications, and Information Technology sectors listed on the PSE over a 30-year span. Using mean-variance analysis, it evaluates portfolio optimization by balancing risk and return. Drawing on data from 1993 to 2022, the study examines portfolios across various risk-return frontiers (RRFs) and compares the service sector's performance. The results highlight varying portfolio metrics across RRFs, with some showing outperformance and higher RRFs leading to diminishing returns. The study also identifies key companies with significant stock allocations, providing strategic insights for portfolio construction. These findings offer investors guidance on optimizing portfolio strategies within the PSE for better decision-making and investment outcomes.

This research, of significant importance, aims to understand and validate the barriers to digital technology adoption by 128 SMEs in the textile sector, which Bank Indonesia assists.  These SMEs are involved in producing batik, woven, and other garment products. The research employs content validation methods to validate the proposed barriers from the literature review. Six experts representing Bank Indonesia, the UKM association, academics, the industry service, the cooperative and SME service, and the communications and information technology service Filled out the validation questionnaire. The results of the content validation method reveal that 14 out of 21 barriers are indeed relevant as barriers to the adoption of digital technology in textile SMEs.

Portfolio selection is an important part of investment management that aims to select the right combination of assets to achieve an optimal risk-return ratio. This study integrates the Mean-Variance Theory utilizing the 30-year historical returns of the Philippine Stock Exchange. Three subsectors of the Industrial Sector were used as the investment pool. The back-test results showed that the Mean-Variance portfolios of selected subsectors can outperform the industrial sector and not the market. Stakeholders must be cautious when investing in these three subsectors, considering the values obtained from the simulations. There are recommended portfolio selections wherein companies have a specific allocation that investors and financial managers can use whenever they decide to invest in these subsectors. Overall, this study offers a framework for investors to use as a guide in diversifying their stakes in the Philippine Stock Exchange.

An educational institution's ability to keep motivated and satisfied instructors depends on its ability to manage salaries quickly and accurately. However, there are difficulties in handling salary processing due to its complexity, especially in a provincial state institution. This study applies Social Life Cycle Assessment (S-LCA) to analyze the problem, which is the delayed salary payments of part-time instructors of a provincial state university, and system dynamics (SD) method to improve the efficiency of the instructor wage processing system to mitigate the social impact of the problem. Finding bottlenecks, delays, and difficulties, as well as suggesting solutions, is possible when one understands the underlying dynamics of the system. Using standardized procedures, task automation, and improved departmental coordination and communication are the methods by which the study seeks to optimize the process. The study's conclusions support the university's goal of providing high-quality education by refining pay processing, allocating resources more effectively, and increasing instructor’s satisfaction. The SD analysis approach, significant influencing factors, and techniques for expediting the provincial state university's salary processing procedure are all covered in this study.

Product management perspective, and product-centric approach to data analytics can be valuable during Industry 5.0 (I5.0). This study explores the impact of I5.0 on data analytics by taking a product management perspective. A scoping review is carried out to synthesize a conceptual model for data analytics. The findings indicate specific characteristics of Industry 5.0 for data analytics. The developed concept forms a logical whole, consisting of flexible business processes, scalable data management, and advanced analytics. Productization, master data, dynamic data models, and holistic end to end product lifecycle analytics are at the core for predictive capabilities and deeper analytic insights.

This study examines the efficiency of modular design methodologies by comparing traditional methods with advanced approaches, specifically Axiomatic Design and DSM Cladistics Analysis. Utilizing a case study of a workpiece flipping machine, the research evaluates the modularity index of various clustering techniques. The traditional methods, Design Structure Matrix (DSM) and Hierarchical Clustering are assessed against the newer methodologies to determine their effectiveness in grouping components modularly. Results indicate that Axiomatic Design and DSM Cladistics Analysis significantly enhance modular design, offering superior performance and control ease. This comparative analysis highlights the potential of advanced methodologies to improve machinery design, suggesting a paradigm shift towards these innovative approaches for enhanced modularization in engineering practices.

The Cyber-Physical Production System (CPPS) integrates information technology with physical manufacturing processes to enhance production control flexibility. However, the prevalent issue in manufacturing systems is the heterogeneity of data from multiple sources. CPPS fails to acquire comprehensive information from the current manufacturing system, making it difficult to promptly formulate accurate production schedules. In response to this challenge, the article presents an ontology-based semantic integration and dynamic scheduling framework, achieving semantic interoperability within CPPS. This framework proposes methodologies for constructing ontology models, data models, and rule models of workshop scheduling, thereby providing semantic context, data integration guidance, and scheduling rule description for CPPS construction. Sequentially, under the guidance of the data model, a real-time data integration platform is established to achieve semantic data integration of data in industrial software, which is the key to supporting the dynamic scheduling of CPPS. Finally, employing a sub-assembly construction workshop as an illustrative case, a CPPS prototype is constructed to verify the efficacy of the proposed methodology.

Assessing digital maturity is critical to successfully implementing Industry 4.0 in companies. This study evaluates the digital maturity of Portuguese companies across different regions and sectors using the Shift2Future tool, a self-assessment model adapted to the Portuguese context. The model assesses six dimensions: Strategy and Organization, Smart Infrastructure, Smart Operations, Smart Products, Data-Driven Services, and Human Resources, on a Likert scale from 0 to 5. Data was gathered from 610 companies across sectors like automotive, ceramics, and metalworking through a questionnaire conducted between 2022 and 2023. Using STATA 18.0 software, the analysis included Pearson correlation and Exploratory Factor Analysis (EFA). The results indicate that companies in the North and Lisbon & Tagus Valley regions exhibit higher digital maturity. Traditional sectors like ceramics and glass show lower maturity due to technological and cultural challenges. This study evaluates the digital maturity of Portuguese businesses and suggests ways to improve their Industry 4.0 competitiveness.

Driven by circular economy philosophy and sustainability requirement, end-of-life vehicle (ELV) recycling management as one of sustainable practices has been widely performed by practitioners and academies in automobile sector. However, the single material recycling operation practice in industrial plants ignores the heterogeneous collection quality of ELVs, leading to the inefficient re-utilization and resources waste. This paper shifts our eyes to multiple recycling channel design based on the discrepant recycling quality of collected ELVs. Besides, the multiple recycling channel based 4R recycling operations (re-use, recovery, remanufacturing, and material recycling) is proposed to assist achieve lean recycling management. Facing with the integrated massive uncertainty data, the collection quality is measured and evaluated by developing an improved fuzzy matter-element modeling approach, contributing to the precise recycling and efficiency improvement by multiple recycling channel design. Finally, the experiment study of a numerical case is conducted, and results show that the designed decision-making framework could help manufacturers to design the corresponding recycling channel for better achieve lean ELV recycling management.

Modern industrial data is commonly collected under various working conditions, which are usually nonlinear and multi-modal. This poses great challenges to the accurate isolation of fault variables in abnormal situations. Despite the wide exploration of fault detection methods, studies on fault variable isolation are limited due to the complex correlations between process variables. To cope with the nonlinear and multimodal characteristics of process data, an adaptive Gaussian Mixture Model-based variational autoencoder network is proposed for process fault isolation in industrial processes. Gaussian mixture distribution is introduced into variational autoencoder to fit the complex data distribution in multi-mode industrial processes. An adaptive mechanism is developed to dynamically adjust the number of Gaussian components according to the input data. A combined monitoring index is designed for fault detection via the learned features and residual space. Once the fault is detected, key variables related to the fault are identified by measuring the contribution of each variable to the reconstruction discrepancy. The benchmark Tennessee Eastman (TE) process is utilized to demonstrate the effectiveness of the proposed method.

Heat treatment technology is a fundamental technology in the production of components, and as such it is indispensable and must be considered in the sustainable transformation to a CO2-neutral economy and society in the coming decades. For this reason, the sintering process is analyzed in more detail in this paper as a representative example of heat treatment processes. An unsupervised anomaly detection model is proposed that identifies data anomalies based on the parameters of the sintering process. To provide a holistic view of the sintering process, over 100 parameters from the pre-heating zone to the cooling zone of the sintering oven are analyzed. When an anomaly is detected, this approach allows to determine in which sub-process the anomaly has occurred to intervene specifically in this sintering zone. By preemptively identifying anomalies and intervening accordingly, the potential production of substandard components is prevented, thereby enhancing the sustainability and reducing CO2 emissions in the sintering process.

Dog is one of most common pets around the world. Due to increasing population of pet dog, the owners are willing o pay more money to purchase safe and health products to their pet dogs. This paper is firstly summarizing the common types of hazards of pet toys, then suggesting the design consideration for pet toys, reviewing the current safety testing and evaluation for the human toys and limitations of such testing and standards applied to pet toys, then discussing training of owners in order to make sure that the owners understood what the safe pet toy is, Finally the further research direction is suggested in the conclusion

The prediction of complex product quality has been extensively studied in the last decades. However, due to the high dimensionality and diversity of quality features, the control optimization of feature parameters remains a significant challenge. For complex products, fault detection techniques are required to accurately predict product quality, and significant influencing quality features must also be identified for their control optimization. In this paper, we propose a novel approach combining the Least Absolute Shrinkage and Selection Operator (LASSO) method with Bayesian Networks (BN) for the detection, identification and control of product quality metrics. Specifically, for complex products with high-dimensional features, the identification of key quality features is achieved initially by the LASSO method to obtain more accurate quality prediction. Subsequently, the optimal production range is determined through the utilization of a Bayesian network to achieve the optimization of product quality. The experimental results demonstrate that processing fewer, but critical, features not only achieves satisfactory prediction accuracy, but also saves computational time. Furthermore, this method offers practical operational guidance for product quality prediction and control in complex product industries.

Due to the increasing demand for green hydrogen, driven by global efforts to decarbonize various sectors, the industry is dedicated to scaling up the production of hydrogen electrolyzers. In response to this, current research projects aim to establish fully automated assembly plants for hydrogen electrolyzers. An essential step in this process is the focus on solutions for quality testing and error identification. In the following work, by synthesizing existing literature and insights from domain experts through interviews, potential errors and influences associated with electrolyzer stacks and their components are investigated. Furthermore, recommendations for effective quality testing are outlined. Through this research, the groundwork for decision-making and the development of robust quality assurance within the context of automated electrolyzer stack production is provided.

Fuel cells play a crucial role in future energy systems, and their key components, metal bipolar plates, are prone to various anomalies during the production process, which can seriously affect their overall efficiency and lifetime. In this paper, prior knowledge-based SimpleNet model is proposed to detect the BPPs, but the significant scale difference between different defect types makes the original architecture difficult to achieve high-precision anomaly detection. For this reason, this paper constructs anomaly feature synthesis related to the a prior knowledge of defects to adapt to the detection of different defect types. The results show that the detection accuracy is significantly improved by 2-11% after constructing the prior knowledge. This study highlights the potential of unsupervised learning and representation for anomaly detection in metal BPPs with significant scale differences.

Healthcare-associated infections (HAIs) pose a significant challenge to the quality of care, leading to patient complications, extended hospital stays, and high consumable costs. Cloud-based technologies have become essential for overcoming barriers to circularity in healthcare, offering transformative opportunities for healthcare systems. Measuring the impact of linear consumption is crucial for circularity, however the healthcare industry lacks focus on measuring the impact of HAIs, particularly related to hand hygiene. Integrating cloud-based hand sanitizer and group technology offers potential to prevent the spread of HAIs and minimize linear consumption, aligning with Circular Economy Goals (CEGs). This study examines the potential of integrating cloud-based HST with group technology (GT) to minimize the impact of HAIs caused by poor hand hygiene. Using the Opitz Code classification system, a coding system is created to measure the impact of poor hand hygiene, serving as a control and improvement tool aligned with CEGs. This article presents an illustrative case to demonstrate the HST coding classification to facilitate predictive analysis for HAIs prevention and control. These findings contribute to reducing HAIs by improving hand hygiene performance in healthcare facilities. Furthermore, by aligning technological advances with CEGs, this study offers a proactive strategy to mitigate environmental degradation by reducing carbon dioxide (CO₂) emissions and strengthening the resilience of healthcare systems.

In this paper we identify technologies that are slated to be used in Industry 5.0 and were not there in Industry 4.0. These are collaborative robots (also called as COBOTS), digital twin technology and use of wireless 6G technology. We relate these new technologies of Industry 5.0 (in particular the use of COBOTS and Digital Twin Technology) to the strategy of the firm. We note in particular that in organizations with cost leadership strategy (with very low level of environmental / internal uncertainty) there may be less pressing need for use of COBOTS than in firms with differentiation / innovation strategy (where there is much higher level of environmental / internal uncertainty). By using a similar reasoning we also note that in organizations with differentiation / innovation strategy, implementation of Digital Twin technology may take more efforts than the efforts required in implementation of Digital Twin technology in organizations with cost leader strategy.

In developing economies, the construction industry is crucial, contributing 4-8% of GDP and 45-65% of gross fixed capital investment. HVAC systems in the residential sector consume around 39% of energy, and by 2050, global energy demand is projected to grow by 50%, with buildings becoming a significant emission source. To address these challenges, it's essential to improve methods, skills, and systems. This study aims to evaluate the efficiency of duct manufacturing through automation and provide solutions for future improvements. Duct fabrication involves stages like cutting, bending, and assembly. The Pro Model Simulation was used to assess system efficiency, revealing utilization percentages of equipment and average effectiveness. The simulation results help determine the optimization of process equipment, identify upgrade needs, and enhance the efficiency of duct fabrication stages. Improving duct manufacturing processes is vital to meet future energy demands and environmental goals. By focusing on automation and efficient production methods, the construction industry can reduce energy consumption and emissions, supporting sustainable development in growing economies.

The boom in Artificial Intelligence (AI) technologies is opening up new opportunities in engineering. A variety of novel tools are flooding the market every day. However, the integration into the industry processes is happening at a slow pace. This paper represents a market survey conducted with 163 engineers on the use of AI in product development. The questionnaire specifically focuses on the early phase of product development and investigates the current state, challenges and potentials. The results show a high level of interest in the use of AI, but integration into everyday working processes has been low so far. Among the few who incorporate AI into their concept development processes, a link to shorter concept development times was observed. The automation of routine tasks and a conflicting requirements detection are seen as particularly promising AI applications. Main challenges and barriers lie in the expertise of employees, the costs of implementation and the usability of data. Nevertheless, more than two thirds state that further AI integration is planned. The focus here is particularly on generative AI.

In designing and implementing ethical Artificial Intelligence (AI) for industry, differing perspectives on developing trustworthy AI are evident. This study highlights these variances and offers recommendations to bridge these gaps, moving beyond the trolley problem to address complex challenges in trustworthy and ethical AI. We define three pillars of trustworthy AI: transparency, reliability, and safety. Transparency involves clear, open communication about AI decision-making processes, which fosters trust among stakeholders. Reliability ensures consistent, dependable performance under various conditions, essential for critical operations. Safety focuses on preventing harm to humans, the environment, and infrastructure, requiring robust safeguards and adherence to safety standards. By prioritizing these pillars, the research provides practical recommendations for developing AI systems that balance technological advancement with ethical principles, enhancing user trust and ensuring responsible AI integration across industries.

Maritime transportation has an important role in global trade because most world trade uses sea transportation. One of the biggest contributors to world trade across the sea is container trade. After the pandemic, the container trade experienced a significant increase. This increase has resulted in several ports and container terminals facing operational problems. To deal with these problems, some operations at container terminals have been carried out automatically. One requirement for automated operations at container ports is the ability to automatically identify objects inside the terminal's environment. One aspect of computer vision, image detection, has been widely applied in security and health. With image detection, the process of identifying and detecting an object can be done in real-time and accurately. This paper aims to review previous studies discussing the topic of object detection in container terminals. The main focus of previous research on object detection based on one of the widely used approaches, namely deep learning, is systematically presented in this study. The review process is systematically presented. Challenges, limitations, and suggestions for further research are discussed in this study.

Sectional anomaly detection in urban traffic is challenging due to weather or driving behavior uncertainties. Identifying these anomalies is crucial for maintaining traffic safety and effective administration. This paper proposes a framework for optimization to uncover anomalous patterns in traffic flow time series. The proposed methodology involves the analysis of potential anomalies through the utilization of clustering techniques. An optimization framework is employed to identify particular sections of traffic data that deviate significantly from standard patterns by assuming that smaller clusters are likely to be abnormal. The results were validated through a comparison with established methodologies. The experimental results indicate that the conventional optimization methods can effectively estimate the sectional irregularities with high accuracy. The paper also examines the consequences of detecting anomalies in urban traffic management.

The introduction of smart technologies for managing large herds with a small workforce is gaining attention. For example, methods that involve attaching activity monitors or IC tags to livestock and using the information obtained from these devices for livestock management are already in use. Some of these methods have issues such as high costs because each animal requires an IC tag. In this study, we propose a model that identifies a specific individual cow and estimates their position in the barn using only images obtained from cameras installed in the dairy farm's barn. The proposed methodology does not require   equipment other than cameras to obtain information about the cows and captures images over a wide area of the barn with a single rotating PTZ camera, allowing for a low-cost setup. Using YOLOv8, we built an individual identification model. As a result of the individual identification, the model showed an overall accuracy of 96.6% and a recall rate of 95%, demonstrating that it is possible to practically identify and estimate the position of a specific individual.

This study evaluates the satisfaction and usability of two trading platforms through a survey instrument and qualitative analysis to identify implications for usability improvement. First, the platforms were evaluated using the QUIS questionnaire. A paired t-test revealed significant differences in usage satisfaction across five categories: overall reaction to the software, screen, terminology and system information, learning, and system functionality. Subsequently, qualitative analysis, including think-aloud protocols and debriefing sessions, was conducted to explore specific factors for usability improvement in these categories. The study identified critical areas for enhancement, such as improving the information architecture, optimizing screen layouts, using user-friendly terminology, reducing login barriers, and providing customized interfaces for different user levels. These findings provide insights for future research and development efforts aimed at improving the usability and satisfaction of various trading web platforms.

Collaboration, rather than competition, is becoming increasingly well-known among organizations of similar size. This paper addresses the modeling strategies in purchasing collaboration or pooled purchasing using Common Replenishment Epoch (CRE) for multi-suppliers and multi-buyers. The approach involves integrating five buyers through a third party, utilizing purchasing volume and information sharing. The numerical study revealed that collaboration resulted in a 14% reduction in total operational costs compared to prior collaboration. The potential contribution of this study is to present an overview of horizontal collaboration in pooled purchasing using quantitative methods to minimize the purchasing cost and optimize replenishment time. However, while the benefits of pooled purchasing are obvious, there are also challenges and limitations to negotiate

The research commenced with a focus on reverse logistics analysis, employing the fuzzy Delphi method (FDM) to identify barriers, enhancing decision-making and forecasting trends. A modified FDM incorporating Z-numbers was introduced, providing precision in uncertain contexts. Questionnaire development integrated literature review and expert input, ensuring comprehensive coverage. Utilizing Triangular Fuzzy Numbers and Defuzzification facilitated item prioritization, establishing a structured hierarchy for sustainable remanufacturing strategies. Expert consensus thresholds guided iterative Delphi rounds, ensuring robustness. Likert Scale data transitioned into Fuzzy Scale, with conditions dictating item acceptance. Triangular Fuzzy Numbers' role in acceptance/rejection underscored its utility. Defuzzification finalized item ranking, ensuring methodical decision-making. The process's structured application in Microsoft Excel ensured accuracy and reliability in data analysis.

Correlation-based storage assignment in warehouses involves placing frequently co-ordered products in close proximity to enhance order picking efficiency. This study investigates various approaches towards achieving correlation-based storage assignment in the extant warehousing literature. More specifically, we analyze and classify different storage systems considered, the methods suggested, and the performance evaluation of these methods. A comprehensive search using SCOPUS and Google Scholar is conducted to select 77 relevant studies. The review reveals that most studies focus on picker-to-parts systems, with the  recent trends focusing on automated parts-to-picker systems. We present a classification framework for the methods used in the selected studies to achieve correlation-based storage and to evaluate the performance of the storage assignment. The paper concludes with recommendations for future research, emphasizing the need for investigations into parts-to-picker systems, scattered storage assignment, and the joint solving of multiple warehousing decision problems.

Under pressures from fierce market competition, many enterprises establish flexible multi-echelon supply chain networks to shorten the delivery time for realizing quick responses to customers. Rational inventory management strategies are essential to manage inventory levels correctly to serve customers on time, reduce costs, and improve the overall efficiency of the supply chain. Considering a complex supply chain network with multiple echelons and multiple nodes in each echelon can order from different higher nodes, this paper proposes a simulation optimization method for inventory control policy optimization based on a genetic algorithm. This method encodes the parameters of inventory strategies of multiple warehouses as a chromosome. It evaluates the fitness through simulation, thereby achieving joint optimization of inventory strategies across supply chain networks. Finally, this paper conducts numerical experiments using real data from a home appliance enterprise and performs sensitivity analysis on some parameters to validate the effectiveness of the proposed method. The experimental results demonstrate that the simulation optimization method can reduce the total cost of the supply chain by optimizing the inventory control policies.

To accelerate the electrification of the transportation sector in developing countries, particularly Indonesia, electric motorcycle conversion has been introduced. This study explores the opportunities for applying the circular economy to electric motorcycle conversions to enhance sustainability. A case study research method was used, involving observations and interviews at an official electric motorcycle workshop. The results show that circularity efforts have been implemented through reuse and repurposing. Reused components include body parts and throttle bodies, while repurposed components include fuel tanks and CVT engines. However, further consideration is needed for components reused as spare parts for conventional motorcycles, as these may eventually be replaced by electric vehicles.

Supply chain performance (SCP) is an essential activity of supply chain management. It enables the company and stakeholders to evaluate and enhance SCP to remain competitive. This research aimed to measure the company's SCP which is then modeled in a dashboard of supply chain performance monitoring and evaluation system. This research employs hybrid of Supply Chain Operation Reference (SCOR) Model Type 12 and Multi Criteria Decision Making Methods, specifically Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).  The SCP average of 45 Key Performance Indicators (KPIs) mapped by SCOR Model is 0.487, categorized sufficient. Sensitivity analysis was conducted to see the consistency of the weighting results. The SCP dashboard displays the visualization of each criterion and indicator performances. A number of recommendations are purposed for further research such as extending the study to other industries like salt, fish, and coffee processing to evaluate supply chain performance, determining superior commodities and evaluating suppliers. Furthermore, taking into account other MCDM pairings might improve our comprehensive understanding of the best alternative decision making.

Throughout the years, management theories have evolved through the analysis of empirical evidence of company practices and experiences. Concept of the Living Company drew parallels between organizational behavior and living organisms, emphasizing growth, learning, and longevity in business. While attention has also been given to the behaviors of social animals in relation to management principles, recent research has unveiled the remarkably intelligent strategies employed by plants throughout their life cycles. Drawing on prior sources, this overview explores intriguing behavioral aspects of plant life that share similarities with managerial practices, drawing analogies between the two and paving the way for valuable insights for management theories to be gleaned from the lessons of the natural world. Paper is cultivating management success through plant analogies.

This study examines the relationship between High-Performance Work Systems (HPWS) and employee work engagement in an ICT company in Estonia. Using standard questionnaires, we analyzed 112 valid responses. The results show a strong positive correlation between participation in decision-making and overall employee work engagement. Respondents group with over 10 years of experience reported significantly higher levels of vigor compared to those with less than one year of experience. These findings underscore the importance of fostering employee participation and effective onboarding processes to enhance engagement and organizational performance, offering valuable insights for HR practitioners in highly competitive industries like ICT, globally. Additionally, managers scored higher in work engagement, emphasizing their crucial role in HR practice implementation.

This study is to verify wearable health technology (WHT)’s long-term effects on older adults’ health and lifestyle management, and to propose the strategies for future usage of mHealth to meet older adults’ needs. This is a two-year follow-up study for the previous 12-week WHT trial. Twenty-four older adults in the previous trial agreed to participant in this semi-structured interview study. Three patterns of older adult users’ health awareness changes were identified: no change-consistent low (n=2), no change-consistent high (n=7), and increased (n=15). Owe to the increased awareness because of the 12-week WHT trial, long-term behavior changes were identified for their health and lifestyle improvements in the following two years. To achieve a healthier lifestyle, their attentions were paid not only on physical activity, but also on well-balanced diet and sleep rhythm. Therefore, the long-term effects of WHT were proved. To support the self-health and lifestyle management, 10 participants used various technologies along with own needs and convenience, such as mobile apps and home medical devices. Regarding the future mHealth usage, older adults emphasized the recognition of mHealth technologies’ benefits. In addition, connecting mHealth technology like mobile apps with home medical devices were also expected.

With societal and technological advancements, human-computer interaction design now emphasizes ease of use and user experience. This paper addresses issues in single-channel eye-control interactions by integrating eye and hand control. We propose two dual-channel strategies: "eye control lock-hand control trigger" and "gaze browsing positioning-hand control state switching-eye control trigger".  Using Unity and Tobii eye tracker, we developed an interaction system and conducted ergonomic experiments and subjective evaluations. Results show that for hand-eye dual-channel systems, the "eye control lock-hand control trigger" strategy ensures high interaction efficiency and usability, with interface layout having no significant impact. This study enhances dual-channel interaction systems' efficiency and usability, promoting broader application of eye-control interactions.

Visualization technologies like AR/MR have transformed traditional design collaboration, significantly impacting power dynamics and conflicting interests among stakeholders. However, there is currently no comprehensive mapping of how AR/MR technologies intersect with power dynamics. Therefore, we propose a framework to examine power structures and influences in AR/MR-aided design collaboration, providing insights into how control and decision-making processes unfold. This framework helps researchers understand how AR/MR technologies affect the negotiation of design outcomes and interpersonal relationships in collaborative settings.

The decision-making process in banking involves considering logical analysis and psychological factors. The way information and data are presented can impact cognitive limitations and behavioral biases when making loan decisions. The study examines how the presentation style of loan proposals affects the risk perception and confidence level of SME bankers when making loan decisions. An experiment was conducted where participants reviewed a modified loan proposal and rating report in an online meeting, analysed the information, discussed it, and decided to approve the loan. The results indicate that the way the proposal is presented can introduce biases and errors in the decision-making process. To improve accuracy, the study suggests presenting the proposal clearly, namely, providing comprehensive explanations for the rating, ensuring the presenter's mastery of the material, confirming the data's suitability, and using an easily understandable format. This research contributes to understanding decision-making in SME banking loan approvals.

This study explores the integration of Analytical Hierarchy Process (AHP) with Mean-Variance Theory and the Safety-First Model for optimal portfolio selection in the Philippine Stock Exchange (PSE). AHP, a multi-criteria decision-making tool, aids in prioritizing assets based on risk and return. Mean-Variance Theory, emphasizing diversification and the risk-return trade-off, and the Safety-First Model, focusing on downside risk management, offer comprehensive frameworks for decision-making in volatile markets. The research evaluates the top 30 companies in PSE, addressing the challenges of emerging markets such as high volatility, political instability, and limited liquidity. By combining qualitative and quantitative factors, the study aims to assist investors in maximizing returns while minimizing risks. Back-testing from 2018 to 2022 shows that the Mean-Variance portfolios yielded higher returns but higher risks compared to Safety-First portfolios. The findings suggest that integrating AHP with these models provides a robust method for portfolio selection, balancing risk and return, and offers a significant contribution to financial analysis and investment decision-making in emerging markets.

The city of Bandung is famous for its various culinary delights, nature, historical places and other entertainment venues, making tourists interested in spending time on vacation or just visiting. The large choice of activities or tourist locations in the city of Bandung means that tourists need to determine which activities or locations to visit. This selection process is difficult to carry out because the time available to be in Bandung City is often limited and there is travel time that must be made. This route determination problem can be modeled as an orienteering problem. The goal of this problem is to maximize tourist satisfaction. There are constraints in designing a route, namely time constraints. Solving this problem can be done after collecting satisfaction data from each location and travel time from one location to another. The results obtained from this research are the selection of travel routes and locations in Bandung City with time limits that must not be violated to maximize satisfaction. The result showed that with 100 minutes, eight destinations visited with total satisfaction score is 57.

Order picker routing in a rectangular single-block (conventional) warehouse is a classical and fundamental problem. Exact algorithm with linear computational complexity exists, and it has also been frequently extended to non-conventional warehouses. This paper proposes a new and more concise formulation of the order picker routing in the conventional warehouse. It is easier to present and understand, based on which the algorithm can be implemented in a more concise way and the computation is more efficient. Viewed as an improvement of existing methods for the order picker routing problem in conventional warehouses, the new formulation and corresponding algorithm have potential to be adopted in non-conventional warehouses.

Stage configuration and ticket sales methods are crucial elements that influence the delivery of live entertainment to customers. This study uses social simulations considering both stage configuration and ticket sales methods for live entertainment and assesses their impact on customer utility. Five stage configurations are examined (rectangular, convex, two-stage, three-stage, and circular configurations), with each seat’s value defined and calculated as the seat utility value. Using these values, simulations are conducted for three ticket sales methods (uniform pricing, multi-seat pricing, and auction sales). The results show that the circular stage configuration is the optimal layout for maximizing customer utility across all three sales methods, while the frequently used rectangular stage configuration has the lowest provided value. Additionally, the combination of the convex stage configuration with ticket sales considering agents’ winning history and the two-stage configuration with auction sales are both effective and suitable.

Since the conventional data envelopment analysis (DEA) models are unsuitable for handling ratio data, we develop a novel type of mathematical model for efficiency evaluation and analysis that combines DEA and ratio analysis (termed DEA-R model). DEA is a popular and powerful approach for evaluating the relative efficiency of decision-making units with multiple inputs and outputs. However, owing to the increasing prevalence of ratio data (e.g., operating profit per person) in practical applications, integrating DEA with ratio analysis has become necessary. Thus, we develop a novel DEA-R model that integrates the well-defined range-adjusted measure (RAM) DEA model with ratio analysis. The developed model can handle ratio data and allows the incorporation of expert opinions in the selection of output-to-input pairs. These advantages over the conventional DEA model make the developed model a more flexible and effective approach. To demonstrate the validity and superiority of the developed model, we revisit a case study using a dataset of Japanese banks. The results of this application are discussed and several future research directions are provided.

Revenue management has evolved into an essential framework methodology over several decades, as it pertains to actively managing demand and can increase a company's profits. One way to achieve this is by applying a combination carrier. This paper presents a structured literature review analyzing studies on combination carriers in airline revenue management to discover emerging research trends and topics, using cluster analysis to determine the direction of future research. The study deploys a comprehensive bibliometric analysis of 468 papers from 2008 to 2023. Using comprehensive tools from bibliometric analysis, we identify emerging research clusters, conduct topological analysis, explore key research topics, and network collaboration. Systematic graphical mapping helps evaluate research publications over the period explored and directions for future research. The findings of this paper also guide the layout of a strategic plan for future research studies in the field.

One of the main challenges employers face today is the growing skill gap, resulting from a mismatch between business transformation and the skills needed by employees. Since the demographics show a declining trend in Europe, China, and the US, recruiting new skilled talent will become an even bigger challenge in the future. The growing skill gap has reached a point where almost half of employees’ skills will change in the next years. For the individual employee, this implies a need to take on an upskilling journey to still deliver value to their company and society. However, there is a need to understand the individual’s skill gap and identify suitable actions to bridge it. This paper presents the implementation of a tool for guiding employees in finding their skill gaps and matching them to relevant training and learning modules. This includes implementing a skill-matching solution in a nationwide Swedish upskilling programme, highlighting the challenges of creating efficient individualized skill gap assessment, and recommending learning paths.

This paper aims to highlight effective change management practices in AI-related change initiatives. Through 15 expert interviews, we conducted an in-depth case study on the incorporation of an AI coding assistant tool into a global telecommunications company’s software testing process. The tool is intended to aid with test automation development. The study highlights how the ADKAR model was utilized for developing a change management plan tailored to a technology context. Our findings suggest that while the ADKAR model provides a flexible framework that addresses key aspects of AI-related change, its emphasis on a bottom-up approach may limit its applicability for large-scale transformations.

This paper investigates the innovative capability of technology startups in Kazakhstan, identifying critical success factors within an evolving digital economy. By integrating a Delphi method with quantitative analysis, the study offers a new perspective on how diverse internal factors, including leadership type, cooperation, employee satisfaction, emotional intelligence, and diversity contribute to startups' innovation. Findings of this stufy suggest a positive correlation between transformational leadership style, employee job satisfaction level, emotional intelligence, involvement in multiple cooperation types, gender diversity and startups’ innovative capabilities. These insights are significant for stakeholders aiming to build a resilient startup ecosystem in Kazakhstan and similar developing economies.

Digital transformation dramatically changes firms' competition, and many are devoting considerable efforts to developing competitive advantages. Facing the challenge of digital transformation, firms' response shows their resilience and adaptation to environmental volatility. Drawing on the behavioral theory of the firm (BTOF), this study explores how low- and high-performing firms differ in their behavior when firms face the challenge of digital transformation and how the effects of performance feedback are conditioned by the intensity of industrial competition. This study develops a set of hypotheses and empirical tests using data from Taiwan-listed firms in traditional industries from 2016 to 2022. Empirical results show a higher possibility of investing in digital transformation if a firm's performance is below its aspiration level and the intensity of industrial competition intensifies a firm's digital transformation in response to negative performance feedback. The findings of this study theoretically and practically contribute to the research on digital transformation and behavioral theory of the firm by highlighting the importance of performance feedback on firms' digital transformation in the digital era.

We posit that the dynamics of forerunners’ socio-technical landscape and radical socio-technical regime changes lead to abrupt cross-border mobility of engineers from forerunners to latecomers, which serves as the window of opportunity for latecomers’ catch-up. Specifically, we assert that the economic crisis of forerunners and corresponding radical changes in political and employment regimes serve as a trigger of the cross-border mobility of engineers to latecomers more favorable for them. In addition, we emphasize the cross-border mobility of engineers as the endogenization of windows of opportunity depending on latecomer firms' recruiting efforts. We support our theoretical assertions by analyzing the successive leadership changes of Japan, Korea, Taiwan, and China in the display sector during the mid-2000s and late-2010s. Combining a multi-level perspective and innovation systems approach, we advance our understanding of the catch-up and industrial leadership changes by shedding light on the radical changes in forerunner’s socio-technical regimes and mobility of engineers toward latecomer firms as the endogenous windows of opportunity.

Despite the importance of technology management for knowledge spillover, research has yet to identify the mechanisms through which technology management influences knowledge spillover within an organization. Drawing on the resource-based view and technology management literature, this research developed a model and tested that the effect of technology management on knowledge spillover is mediated by technology road-mapping, while the relationship between technology road-mapping and knowledge spillover is moderated by perceived organizational support. This research conducted an empirical study in the manufacturing industry and found support for the research model, in which the indirect effect of technology management on knowledge spillover through technology road-mapping was conditional on the level of perceived organizational support. This study’s novel findings provide theoretical and practical implications for a nuanced understanding of the relationship between technology management and knowledge spillover.

Live streaming commerce is an immersive shopping format with intriguing features. We explore a scenario where seller and celebrity negotiate revenue-sharing, while consumers decide whether to follow the celebrity. It reflects key aspects of live streaming commerce: the celebrity’s bargaining power tied to her follower count, making both parties’ bargaining power endogenous. Our analysis uncovers the central tension between maximizing total profit (“pie effect”) and maximizing shared revenue (“slice effect”). Interestingly, the celebrity’s popularity moderates how the two players balance the two effects. When the popularity high or low, the two effects are well-matched, either the celebrity or the seller can achieve maximum total profit and maximum shared revenue simultaneously. However, when celebrity's popularity is moderate, the slice effect may dominate the pie effect, both players may have the incentive to set the equilibrium price apart from the one maximizing the total profit. This lead to several main insights, e.g. hiring more popular celebrity does not always benefit the seller and may lead to a lower equilibrium price; platform may strategically limit traffic to the celebrity to prevent profit reduction.

Recent studies have highlighted the negative effect of an increase in platform users, primarily focusing on factors that may cause problems in using the service. However, there may be factors beyond the use of the service. This study aims to clarify the effect of 1) the image of other users, and 2) the changes in the image of other users on the attitude toward the intention to continuous use. Using questionnaire data, we measured each user’s “own degree of interest in fashion trends” and “image of other users’ degree of interest in fashion trends” at the start of their use and at the present time and conducted a structural equation modeling. Our results shows that users with a high degree of interest in fashion trends have negative feelings toward intention to continuous use the platform when their “image of other users’ degree of interest in fashion trends” had decreased. These results provide new insights into negative network effects.

In recent years, the global food industry has undergone a transformative shift driven by heightened consumer demands for transparency. Through a conjoint experiment (n=246), this study investigates the influence of blockchain-enabled information on consumer trust and preferences in online grocery shopping. Our findings demonstrate that blockchain technology enhances transparency and authenticity, empowering consumers with real-time, verifiable insights into food products' provenance, processing, and quality assurance. By providing reliable data, blockchain-enabled information cultivates consumer trust and influences their preferences when buying groceries online. The integration of blockchain capabilities addresses the evolving needs of consumers, offering a technological solution to facilitate informed decision-making and foster confidence in the integrity of food supply chains.

This study examines the relative impact of blockchain-enabled information of fish origin data, tracking, and sustainability on consumer trust, purchase intention, and repeat purchase intentions. In addition, the price was added to strengthen ecological validity. The study used conjoint analysis with 116 participants from Japan, China, and South Korea. The findings revealed that blockchain-enabled information concerning fish origin and tracking exerted the strongest impact on consumer trust, purchase intention, and repeat purchases. However, for the sustainability attribute, MSC-certified seafood products, had the greatest impact on these consumer behaviors. These results highlight the importance of combining blockchain-enabled information with established sustainability certifications to understand their effects on consumer behavior fully.

In domains where customer objectives and preferences are diverse, providing individually personalized experiences has inherent limitations. However, marketing methods utilizing generative AI technology hold significant potential for enhancing customer experiences and supporting marketers’ operations. While the application of personalized AI has progressed, it often focuses on individual tasks and has not yet comprehensively covered all touchpoints of the customer journey. This study explores the potential of a personalized AI that encompasses all touchpoints of the customer journey to provide optimized experiences for customers. We focus on golf, a sport where the diverse objectives and preferences of the customer base make uniform information provision and service support challenging. Based on a case study of Japan’s largest golf portal site, we examine both the enhancement of customer experiences and the support of marketers’ operations through the implementation of personalized AI and business support AI, and discuss the future role of humans and AI in new business contexts.

While online channels are becoming more and more popular, it has been found that consumers tend to have services in the retail channel but place orders online. This phenomenon is called consumers’ free riding. This paper investigated the impact of consumers’ free riding on the centralized dual channel supply chain which both the direct channel and the retail channel are owned by the manufacturer. The results indicates that the retail channel price should be higher than the direct channel price while consumer’s free riding ratio is lower than the threshold value. Once the consumer’s free riding ratio exceeds the threshold value, the manufacturers should set higher price in the direct channel online. For the profit of the manufacturer, free riding of consumers gives negative effects even if both direct channel and retail channel are owned by the manufacturer. The limitation of this study is that decentralized dual channel supply chain, which has an independent retailer to run the retail channel, has not been studied. The gap will be filled in future study.

This study investigates the impact of open innovation and infrastructural amenities on the growth of sustainable ecotourism, employing a system modelling and simulation methodology. Open innovation, achieved through collaboration with many stakeholders, fosters the development of innovative solutions that enhance the appeal of ecotourism destinations. It also promotes greater awareness and engagement of local populations in ecotourism activities. High-quality infrastructure not only improves the overall visitor experience but also plays a significant role in boosting local income. The Partial Least Squares Structural Equation Modelling (PLS-SEM) approach is employed to examine the correlation between these variables. The findings demonstrate the important influence of infrastructure on visitor experience and income as well as the beneficial effects of open innovation on the sustainability of ecotourism. The results bolster the case for ecotourism's adoption of sustainable practices and offer direction to destination managers and policy makers in crafting winning plans. The study's theoretical and managerial ramifications highlight how crucial cooperation and investments in environmentally friendly infrastructure are to achieving sustainable ecotourism objectives.

Industrial robots are commonly used in factories for efficient high-mix low-volume production. It is required to determine the optimal placement and posture of a robot arm to pick up and place a workpiece to minimize the total operation time. In this study, we propose an efficient optimization approach for solving the placement planning and motion planning problems of a six-axis robot arm using a genetic algorithm (GA). To obtain a feasible solution to the problem of minimizing the total operation time, the proposed method conducts placement planning using GA and motion planning using ROS simulation with a Rapidly-exploring Random Tree Star (RRT*) for a six-axis robot arm. The performance of the proposed algorithm is compared with that of a conventional method that uses particle swarm optimization. The computational results show that the proposed algorithm can reduce approximately 26% of the motion planning time compared to the conventional method.

Large steelmaking companies often have many vehicles, warehouses and cranes in the steelworks, and operate the machines to deliver the heavy products continuously for 24 hours. The efficient shipping operations are quite difficult to achieve because the many machines influence each other and are likely to be affected by various kinds of disturbances. In this research, we focus on the complicated shipping operations in steel works and create the new mathematical model which expresses the operational constraints clearly. Furthermore, we propose the new algorithm which develops an efficient shipping schedule in a short time to immediately respond to situation changed by disturbances. The results of numerical experiments show the new algorithm successfully obtained the efficient schedules which reduced the waiting time of ship loading by more than 50% for all ships and spent about one minute to create an efficient schedule for one day operation. The performance of the algorithm enables both efficient shipping operation and frequent rescheduling to adapt to the changes of situation.

Shading devices is one of the major devices in protecting the indoor visual and thermal comforts. Usually, the shading devices are activated by outdoor vertical solar irradiance level on building facades. However, this approach leads two major problems. Firstly, the outdoor vertical irradiance does not account for solar position which cannot help in identifying the indoor daylight distribution. Secondly, based on this approach, the shading devices are switching on more than it needs. Even in summer season, it reduces the electricity consumption of air-conditioning system, however, the lighting electricity consumption increases. For winter season, both heating and lighting energy expenditures increase. This study examines the indoor lighting, and heating, ventilating and and air conditioning system performances of a typical office equipped with automatic shading devices via simulation methods. Energy simulation programme EnergyPlus is used to predict the response of shading system on the building energy and indoor daylighting performance of an urban topography under Hong Kong weather condition.

This research aims to optimize the electric minibus service at the university by developing an improved route that reduces waiting times and serves more passengers efficiently. Real-time data on existing routes were collected and compared with a proposed route (P-route) using a simulation model. The P-route, developed using adapted warehouse location selection strategies, outperformed the existing routes in efficiency and passenger satisfaction. It requires fewer daily trips and buses while accommodating more weekly passengers and offering shorter trip times. The research recommends implementing the P-route and continual monitoring to enhance the electric minibus service. This study demonstrates the potential for optimizing university transportation systems through the application of warehouse location selection strategies and simulation modeling, benefiting the university community. The findings contribute to the field of transportation optimization and provide insights for universities seeking to improve on-campus transportation services.

Food waste in hospitals is a significant issue, producing substantial amounts of food waste daily, including plate waste, serving waste, and kitchen waste. Understanding the complexities of the hospital food service system, from procurement to consumption, is crucial for addressing and minimizing food waste in healthcare facilities. Given this, system dynamics is a tool that was able to uncover the relationship between various variables in the food service system, which are the Serving Losses Loop, Procurement Losses Loop, Preparation Losses Loop, Portioning Losses Loop, Plate Waste Loop, Attractiveness of Food, Patient Admission and Discharge, and Food Service System Process Loop. Policies were developed using the causal loop diagram and stock-flow diagrams made, which produced the policies (1) Improved Food Portioning Policy, (2) Encouraging Attractiveness of Food for Patient Discharge, and (3) Combination of Policies 1 and 2, with the third policy having found to be most effective. Hence, the study was able to provide a model that aids hospitals in identifying the variables that come into play with their food service processes and recommended policies for food waste minimization.

This study integrates the Dynamic Job Shop Scheduling Problem (DJSSP) with maintenance planning to optimize manufacturing processes. The model minimizes makespan while accounting for the uncertainty of job arrivals and machine maintenance. Analysis revealed a minimum makespan of 753.31 hours and a tardiness of 1391.42 hours before a new job, which increased to a makespan of 813.31 hours and a tardiness of 1689.44 hours afterward. Compared to the existing estimation makespan of 835.81 hours, the proposed model shows significant improvement up to 9.8%. Optimal preventive maintenance intervals, based on minimizing Total Expected Maintenance Cost (TEMC), varied for each component due to their non-identical component. This approach enhances machine reliability and extends equipment life, delaying the need for replacements. The results demonstrate significant improvements over existing methods that use unplanned maintenance schedule.

This paper investigates the comprehensive scheduling problem in Flexible Assembly Job Shop (FAJSP), aiming to simultaneously manage both the processing and assembly activities of workpieces to minimize tardiness and machine energy consumption. To achieve this, a mathematical model for the FAJSP is established, and an improved adaptive NSGA-II algorithm (IA-NSGA-II) is introduced. This algorithm utilizes a process constraint matrix encoding to satisfy assembly constraints and adjusts crossover and mutation operations to ensure chromosome validity. Additionally, variable neighborhood search method (VNS) is employed to expand the search space and generate higher-quality solutions. Simulation experiments confirm the effectiveness of the proposed algorithm in addressing FAJSP.

Due to uncertainties in global supply chains, manufacturing companies are facing increasing resource unavailability. Especially for companies in special machine construction this leads to delays in the assembly process. An agile planning of the assembly sequence has the potential to reduce delays by prioritizing assembly tasks according to resource availability. In order to mitigate these delays, this paper presents a modularization concept for agile assembly sequencing. Therefore, the assembly process is divided into process modules first. According to resource availability, a feasible next process module is recommended. Finally, the effectiveness of the concept is demonstrated on an industrial use case.

Lens manufacturing companies face challenges in performing their planning and scheduling activities due to the complexity of matching the demand with the available capacity, due to the operational constraints in the production lines, including the formation change to minimize product changeover, fulfillment of high and low demand Stock Keeping Units, configuration of negative/positive diopters to lines, planned and unplanned downtime etc., making manual planning & scheduling very challenging. Most of the current planning and production scheduling practices are manual and Excel-based and it involves heavy planning efforts with high human error rates. In this paper, Advanced Distribution Requirements Planning & Scheduling (DRPS) is unveiled to address the challenges plaguing manual planning and scheduling within a lens manufacturing company. Unlike traditional approaches, DRPS meticulously dissects planning and scheduling activities into unique functions including Demand Management, Requirement Planning, Scheduling Engine, and Reporting. After implementing the DRPS system, there were significant improvements (50% to 88%) in various key performance indicators and the planners were able to perform what-if analysis under different operation scenarios and the entire manual planning & scheduling process was digitalized and automated. 

Human–robot collaboration promises high advantages in material recycling operations regarding automation, precision, comfort as well as flexibility. In this work, we design a human robot system where the humans and robots are placed together to execute the work regarding sorting of materials in a Material Recycling Factory (MRF). First, task identification for a recycling process is done and then each task is assessed based on multiple aspects such as level of automation and human interaction needed. In our work we use an MCDM approach to assess the suitability of a task for a human or a robot or as a team. The vague preferences or assessment on qualitative aspects are taken with the help of fuzzy linguistic approach. These inputs are used to design the process flow wherein tasks can be appropriately assigned to humans and robots. We use a multi-objective approach to optimize the process flow of a material recycling facility considering multiple aspects such as cost efficiency, desired level of automation, human comfort level and robots utilization.

Efficient warehousing operations are essential in raising the cost-effectiveness of manufacturing facilities. This concept paper explores the possibilities of reducing storage occupancy in warehouses within factories. It investigates current practices and opportunities across the value stream within a factory from the start, where raw materials are ordered, to the end, where finished goods are dispatched. Methodologies such as the floating warehouse concept are explored, helping reduce and delay the receipt of raw materials to reduce stock covers in the warehouse. Line balancing principles can also be applied to determine minimum and maximum stock levels for raw materials, reducing storage occupancy while ensuring continuous supply to production lines. Finished products can be loaded immediately into containers after rolling off the lines, reducing storage occupancy for finished products in the warehouse. This also reduces the movement count of finished products within the factory, reducing labour costs. The goods receipt and goods dispatch processes of raw materials and finished products can be streamlined, creating opportunities for cost savings on both ends of the value stream within factories.

Unemployment is one of the components in macroeconomics that must be considered and maintained by the government to maintain the country's welfare. Therefore, a model is needed to forecast the unemployment rate in the future in the hope that the government can prepare policies to reduce the impact of unemployment. This study used the time series analysis method with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) technique. In this study, researchers used the unemployment rate dataset from 1991-2021, and they considered the dataset before and after the COVID-19 outbreak. Based on the research results, the ARIMAX model produces a reasonably accurate model of the dataset before the COVID-19 outbreak. However, the ARIMAX model generated from using the dataset after the COVID-19 outbreak is less precise.

The potential for commercially exploitable wave-energy has been estimated to be 30,000TWh globally. While numerous Wave Energy Converter (WEC) designs have been proposed, only a small number have been successfully trialled at sea and adopted commercially. The primary challenge currently faced by WECs is the need to operate under adverse environmental conditions for more than twenty years as offshore maintenance is challenging. Even though innovative designs can be installed close to the coast, maintainability remains low compared to traditional energy producing systems. Economic considerations are critical for the success of WEC projects. The Levelized Cost of Electricity (LCOE) is currently far less competitive than alternative renewable energy sources, hence, financial risk is significant. The SUNBIO project proposes a radical new approach towards the widespread exploitation of WEC technology through a low-cost design that supports energy-autonomous intelligent observatories for ecological monitoring, as well as the creation of artificial marine habitats promoting the restoration of marine areas adversely impacted by human activities.

This article critically evaluates the current status and future research trends of Digital Twins (DTs) in the manufacturing industry. Despite extensive academic publications and significant interest in practical applications driven by initiatives like Industry 4.0 and 5.0, the actual industrial implementation of DTs remains limited. The gap mainly stems from manufacturing companies being overwhelmed by the complexity of DTs, coupled with insufficient methodological support from academia. This article scrutinises the underlying challenges in developing and implementing DTs, emphasising the need to address not only technical barriers but also organisational, methodological, and human factors. By drawing on extensive research experience and industry insights, the paper highlights critical aspects that need further addressing to enhance the practical use of DTs. It advocates for a redirection of research efforts towards core aspects of DTs, away from the euphoric trend fuelled by the available funding in academia and industry. This reflective approach aims to realign the development and implementation of DTs with the actual needs of the industry, focusing on a realistic perspective on the challenges faced and providing impulses for targeted research and development.

This study explores the drivers and inhibitors of Circular Economy (CE) practices and Industry 4.0 (I4.0) technologies, leveraged by manufacturing organizations to enhance Sustainable Performance (SP). A Systematic Literature Review (SLR) was conducted, covering 79 papers; 8 examined both drivers and barriers, 8 focused on drivers, and 63 on obstacles. Based on this review, a conceptual framework is proposed to support the integration of CE, SP, and I4.0 technologies. This framework highlights how linking these three components (CE, SP, and I4.0 technologies) can provide managers with opportunities to optimize benefits by enhancing drivers and mitigating challenges. The results show that the key drivers frequently repeated in the literature are increasing awareness, a skilled workforce, experienced leaders, competitive advantage, and cost savings. However, the major barriers are management support, governmental directives, digital infrastructure, and data privacy. This study provides a diagnostic tool for managers to evaluate their readiness for implementing CE and I4.0 initiatives, ensuring effective investment in sustainability efforts.

The transition to circular economy presents a significant opportunity for power distribution utilities to enhance sustainability while adhering to ISO 9001 quality management standards. This research paper proposes a comprehensive framework that outlines strategic steps for utilities to integrate circular economy principles into their operations. The framework encompasses commitment & leadership, assessment & planning, circular process design, stakeholder engagement, measurement & continuous improvement, adherence to ISO 9001 standards, innovation & technology adoption, and regulatory compliance & advocacy. Drawing on theoretical insights, performance metrics, with practical examples, this paper provides guidance on how different sizes and types of utilities can effectively navigate this transition, contributing to sustainable development goals and enhancing operational efficiency.

This research evaluates the effectiveness of Mean-Variance (MV) and Safety-First (SF) portfolio selection strategies for high-volume/high-value traded stocks on the Philippine Stock Exchange (PSE). Analyzing data from 288 stocks over 7,670 trading days (30 years), we estimated stock returns, assigned probability weights, and conducted optimization models. The results show that the Mean-Variance (MV0.5) strategy achieved the best balance between risk and return, boasting the highest mean and cumulative returns with moderate volatility. The Safety-First (SF) strategy, however, yielded negative cumulative returns, indicating potential issues. The Market strategy had the lowest mean return but also the least risk, appealing to risk-averse investors. The Portfolio Mean-Variance (PMV) strategy provided a reasonable balance of returns and risk. Statistical analysis revealed that only the MV0.5 strategy outperformed market returns. These findings offer crucial insights into risk assessment and portfolio management for investors evaluating these strategies against market performance.

We study an optimization problem faced by a construction material leasing company that aims to optimize the transportation of shoring materials between warehouses and customers within a given planning period. In addition to providing forward transportation from the warehouse to the customer and backward transportation from the customer to the warehouse, the company allows for transferring some shoring materials from one customer who has completed their rental to another. Unlike traditional logistics planning, shoring materials such as shore posts and steel beams usually have irregular shapes, making loading decisions significantly more complex. To tackle this challenge, we propose a modular system to facilitate loading shoring materials on trucks. The modular system resorts to a collection of loading patterns associated with high vehicle capacity utilization from the company’s historical operating data. By incorporating these candidate loading patterns, we develop an integer linear programming formulation that integrates forward, backward, and transfer transportation, enabling us to obtain a cost-efficient transportation solution. A case study using realistic data is conducted to demonstrate the effectiveness of our solution approach.

This study investigates the barriers inhibiting the adoption of Artificial Intelligence (AI) in the air cargo industry. An extensive literature review initially identified 48 barriers, which 12 industry experts validated and refined to produce 16 critical barriers for additional analysis. The Fuzzy DEMATEL (FDEMATEL) approach was applied which quantified the interrelationships among these barriers, highlighting their prominence and cause/effect dynamics. Key findings reveal that high investment costs, lack of top management commitment, data security issues, and data quality management challenges are significant barriers. The study categorizes these barriers into cause-and-effect groups, with emphasis on improving data quality and security to mitigate their impact on investment and management commitment issues. The research concludes with managerial implications, suggests strategies to enhance AI adoption in air cargo, and identifies areas for future research. This comprehensive analysis provides insights into overcoming the obstacles to AI integration, aiming to facilitate the advancement of digital technologies in the air cargo sector.

Causal reinforcement learning (causal RL) represents an innovative approach that combines the principles of causality with reinforcement learning (RL), facilitating more effective and interpretable decision-making in dynamic environments. This systematic literature review investigates the potential application of causal RL in the domain of collaborative urban logistics, a critical component of modern urban infrastructure involving the efficient transportation and delivery of goods within cities. The study aims to identify current applications, methodologies, and frameworks of RL in urban logistics and evaluate its impact on collaboration among logistics agents. By synthesizing existing research, the review highlights key findings, trends, and proposes future research directions to advance the integration of causal RL in urban logistics. This study is essential for understanding the importance of causal RL in solving the complexity of urban logistics challenges by enhancing the explainability for better solutions quality.

This study addresses supplier selection and order allocation (SS&OA) strategy for multi-component assembly products considering demand uncertainties and disruption risks. We propose a novel multi-stage stochastic bi-objective mixed-integer linear programming (MILP) model leveraging the augmented-ε-constraint 2 method (AUGMECON2) to optimize total cost and purchasing value. The model incorporates disruptions, allows flexible order fulfilment, and integrates a comprehensive supplier evaluation process considering qualitative and quantitative criteria. Furthermore, an optimisation-based simulation procedure, based on a rolling planning horizon framework, is used to approximate the solution. The findings reveal diminishing returns on purchasing value with increasing costs. The optimal sourcing strategy depends on minimum order quantity (MOQ). Multiple sourcing excels under MOQ due to demand responsiveness, while dual sourcing offers superior cost efficiency without MOQ. Additionally, multiple sourcing outperforms in managing inventory levels. Sensitivity analysis confirms these trends and the impact of Bill-of-Materials complexity, supplier count, and demand variability on cost and service level. Finally, this research provides valuable insights for manufacturers in dynamic environments, enabling them to optimize SS&OA decisions and achieve improved supply chain performance.

This research investigates data-driven inventory optimization under a finite horizon fill rate agreement. We propose a novel multi-period inventory model that considers the realized fill rate, allowing the supplier to strategically adjust its replenishment and implement a dynamic base-stock system. We use data-driven optimization with the concept of "predict+optimize" to solve this model and compare the results to other policies including the standard base stock policy. The findings demonstrate how the supplier can adjust stocking decisions to meet the agreed fill rate in each performance review period. This research provides a decision support tool for both suppliers and buyers in designing the parameters of a service-level agreement. The results indicate that a longer performance review period would benefit both the buyer and supplier in a data-driven inventory system.

To address persistent poverty among smallholder farmers, rigorous policy analysis is required to develop tailored interventions aimed at improving rural communities while ensuring food security. This research proposes a policy analysis framework to create sustainable value in smallholder Agri-Food Supply Chains (AFSCs) focusing on developing economies. As the overall approach in this paper, we used the case study research method along with inductive reasoning. The study finds 7 major policy categories (PCs) and 21 policy subcategories (PSs) that are crucial for smallholder AFSCs. Agricultural policies, land use policies, rural development policies, trade and market policies, environmental policies, social policies, and policies addressing climate change and resilience are all important policy categories. Each policy category is divided into subcategories, such as agricultural input incentives, land redistribution, infrastructure development, tariff policies, sustainable agriculture practices, social protection, and climate-smart efforts. To assess policy success, the research defines Key Performance Indicators (KPIs) in four dimensions: social equality, economic viability, governance, and environmental sustainability. This research provides a foundation for future studies and policymaking aimed at enhancing the sustainability of smallholder AFSCs.

In recent years, the number of R&D partners in complex products (Cops) innovation has grown, thanks to the extensive knowledge and skills they offer. However, the current environment is becoming increasingly volatile, adversely affecting the innovation performance of Cops firms. Innovation is the primary source of survival and growth for firms; therefore, this paper aims to establish and empirically analyze a conceptual model for maintaining the innovation performance of Cops firms in a changing environment. Combining dynamic capabilities theory with the resource-based view, this study employs Smart-PLS 4 to test Cops firms. Results based on a sample of 270 Cops firms from China indicate that dynamic network capability (DNC) has a positive impact on innovation performance, with dynamic positioning characterized by increased centrality and resource orchestration playing dual and chain mediation roles between DNC and innovation performance. This paper contributes to the literature on dynamic capabilities, network dynamics, resource mechanisms, and innovation. For practitioners, this paper suggests developing DNC to sustain innovation in turbulent and changing environments. 

This study presents a novel method utilizing wearable technology for ergonomic risk assessment, specifically targeting the mitigation of work-related musculoskeletal disorders (WMSDs) within industrial settings. It employs Inertial Measurement Units (IMUs) to capture and analyze joint angles in real time, with a detailed focus on the shoulder, elbow, and wrist during repetitive tasks. A significant advancement involves converting IMU data into a comprehensive 3D orientation model using quaternions, significantly aiding in visualizing limb movements and detecting potential ergonomic risks. A case study from the automotive industry highlights the importance of closely monitoring wrist movements to manage ergonomic risks effectively. By advancing the understanding of ergonomic risks and facilitating targeted interventions, this study contributes to integrating innovative solutions for improving productivity and worker well-being, in line with Industry 5.0 principles. Additionally, we propose future directions for integrating AI and machine learning to enhance predictive ergonomic risk assessment.

In project management, the resource allocation problem consists of determining a schedule for the set of project activities that are related to each other by prescribed precedence relations and that require some time and some scarce resources to complete. In general, the goal is to minimize the project duration or time-to-market. In many cases, each resource represents a team of people with specific skills, such as engineering or marketing specialists. To promote team productivity and cohesion, it is often desirable to balance the workload of each resource unit. We analyze two alternative approaches to formulate appropriate workload-balancing constraints in a mixed-binary linear optimization problem. In the first approach, the maximum deviation of each unit's workload from the average workload is bounded, and in the second approach, the maximum workload difference between any pair of units is bounded. Our computational results for a standard test set from the literature show that balanced workloads can generally be achieved without increasing project duration; moreover, the second approach provides more flexibility, resulting in fewer instances for which no feasible solution exists.

The R&D network consisting of multiple innovators has become an important way for development activities of complex products (Cops). Considering the current insufficient innovation capacity of Cops R&D network in China, and ineffectiveness of the existing evaluation indicator system in assessing the innovation capacity of Cops R&D network, this paper aims to construct evaluation indicator system and evaluation model for innovation capacity of Cops R&D network from project perspective. First, we analyze the formation of Cops R&D network from project perspective. Based on this, we use literature review, expert interview, membership analysis, and questionnaire survey to construct evaluation indicator system. Then, combined with the characteristics of Cops, the AHP-entropy weight method and fuzzy matter-element theory were used to establish evaluation model. Finally, an empirical analysis is carried out with a Ground-based Signal Gateway Station Development Project. This paper suggests that the evaluation indicator system and evaluation model are effective, which enriches relevant research and provides theoretical basis for policy formulation to improve the innovation capacity of Cops R&D networks.

Influence of partners’ cooperation on firm’s innovation is not consistent and varies in direction and significance. Nevertheless, few studies have assessed how formal and informal cooperation can affect research and development(R&D) firms’ innovation performance differently. Accordingly, this empirical research on a sample of 286 Chinese R&D firms evaluates whether formal and informal collaboration modes affect innovation performance differently. Moreover, it assesses the moderating role of task interdependence. The study observes that formal cooperation and task interdependence positively affect innovation performance; whereas, informal cooperation has adverse effect. In addition, the negative effect of informal cooperation is curtailed at higher degrees of task interdependence. The results contribute to resource dependence theory and innovation management literature indicating that formal collaboration and task interdependence are important to innovation performance, while informal collaboration does not fulfil the same role. Practically, R&D firms should carefully weigh cooperative modes to optimize their advantages and disadvantages. Besides, given that informal cooperation is inevitable, we suggest high task interdependence as a useful measure to protect firms’ innovation performance from the hazards of informal cooperation. 

In the context of the frequent occurrence of disasters in the world, disaster response mechanism operations to maximize the safety of citizens and minimize damage to life and property have been a constant concern. Thus, this study will explore the framework of disaster response systems in seven countries. Using research methods of qualitative analysis and keyword searches across academic databases, the disaster response framework of seven countries and the technical means involved in various fields are sorted out, analyzed, and summarized. Preliminary findings indicate that based on the complexity of disaster response systems, more complex systems can instill a way of thinking to help improve disaster response. Furthermore, from a system of systems (SoS) perspective, multiple factors can be simultaneously considered in future research to establish a complex disaster response system that would minimize the impact of disasters on society.

This study aims to examine the factors influencing household intentions in Indonesia to adopt solar power systems through a systematic literature review (SLR) using the PRISMA framework. Based on the analysis of 17 relevant articles, it was found that economic, regulatory, social, environmental, and technical factors are the primary determinants of RSPV adoption intentions. Economic and regulatory factors are the most dominant. High initial investment costs and the dynamic evolution of government regulations significantly influence household decisions to adopt solar power systems. Social, environmental, and technical factors also affect adoption intentions, but these are consistently related to and influenced by economic and regulatory factors. The findings provide insights for policymakers in devising strategies to enhance the utilization of RSPV in order to achieve the national energy mix target from renewable sources in Indonesia.

In facing the project economy paradigm, the significance of project management skills has emerged. The complexities of managing multiple interrelated projects and stakeholders, especially in the context of Higher Education Institutions (HEIs), underscore the challenges faced by program management. This research aims to conduct a maturity assessment in the IT department of a private HEI in Indonesia, focusing on identifying the strengths and weaknesses in program management. The maturity self-assessment instruments used the model project management maturity model by Krezner with some adaptations. From the assessment result, the success of program management within HEIs, particularly in IT departments, relies significantly on the commitment and role of leadership at all levels.

We study a project with stochastic activity durations and cash flows; we model the uncertainty using discrete scenarios. The project entails precedence-related activities, each of which incurs a cash flow that may be positive (inflow) or negative (outflow). The problem is to find a scheduling policy that maximizes the expected net present value of the project. A scheduling policy decides the starting time of each activity under every possible scenario. Ideally, one wants to expedite the inflows, while delaying the outflows as much as possible, without violating the project deadline. In this work, we devise an exact and a heuristic method to define policies within two new classes of scheduling policies. The first policy class generalizes all existing static policies in the literature and further illustrates the importance of intentional activity delays from both a theoretical as well as an empirical point of view. Whereas the literature on project scheduling has mainly focused on static policies, we also propose a second class of dynamic policies. We show that dynamic policies outperform static policies by means of extensive computational experiments.

The Hong Kong construction industry faces severe problems of labour shortages, ageing workforces, escalating costs, and environmental burdens. Modular integrated construction (MiC) is an advanced construction with proven advantages for addressing challenges faced by the industry, which has been strongly advocated in recent years in Hong Kong. However, the adoption of MiC remains low after its promotion, mainly owing to its high initial cost and procurement cost, which heavily rely on the government’s financial support. The carbon regulatory system, as a mechanism to guide private investment to low-carbon choices through market means, effectively offsets the negative impact of financial subsidies. MiC adoption results in carbon emission reduction, and carbon trading income can offset its high procurement cost, thus eliminating dependence on the government’s financial subsidies. As this proposal will not only benefit the construction industry but also contribute to Hong Kong’s pursuit of carbon neutrality and sustainable development in the long term, this study will conduct an in-depth investigation into the key influencing factors of utilizing the carbon regulatory mechanism to achieve the promotion of MIC application in Hong Kong.

To allow for a more comprehensive analysis of the financial aspects of a project, we study a project scheduling problem with general temporal constraints, where in addition to cash flows associated with the completion of each activity, per time unit earliness penalties apply if activities are completed before their due date. The objective is to maximize the project net present value including the discounted earliness penalties. To solve this problem, we first investigate the structural properties of the extended objective function. Based on these results, we propose an adaptation of an existing steepest ascent approach for net present value project scheduling problems.

The problem of scheduling conflicting jobs on parallel machines consists in assigning a set of jobs to a set of machines so that no two conflicting jobs are allocated to the same machine, and the maximum processing time among all machines is minimized. We propose a new compact mixed-integer linear formulation based on the representatives model for the vertex coloring problem, which overcomes a number of issues inherent in the natural assignment model. We present a polyhedral study of the associated polytope, and describe classes of valid inequalities inherited from the stable set polytope. We describe branch-and-cut algorithms for the problem, and report on computational experiments with benchmark instances, including comparisons with the currently best-performing algorithms.

This study addresses a real-world issue where orchard owners need routing plans for automated spraying vehicles under constraints of efficiency, safety, and reliability, necessitating fully autonomous systems. We have modelled the problem under mixed integer linear programming as a Split Delivery Capacitated Arc Routing Problem (SDCARP). The study introduce a novel approach to address this SDCARP by approximating real-world irregular agricultural plots of lands as regular grid graphs. In particular, we leverage methods that exploit properties of the regularity of these grid graphs where each robot’s path plan follows adjacent demand edges, making it straightforward to generate better solutions. Additionally we provide a heuristic method to generate the solution and explore potential avenues for future research. The contributions of the present research are twofold. First, we provide a set of realistic datasets for future testing and establish a connection between agricultural applications and the SDCARP model. Secondly, we apply newly developed SDCARP solution methods to the above real-world problem, transforming irregular physical graphs to graphs that are more amenable to the application our developed techniques.

We study a drone scheduling problem that arises in the surveillance of a continuous area utilizing a fleet of drones over a specified planning horizon. Given the finite coverage capabilities of drone sensors, we partition the area into discrete hexagonal grids and conceptualize the area monitoring within a time-expanded network framework. Through mapping the monitoring requirements into revenues of individual grid-time nodes on the network, the drone scheduling problem is to find the optimal flight tours for the drones, maximizing the revenues obtained from the visited grid-time nodes. For this problem, we develop an arc-based integer programming formulation. A Lagrangian relaxation-based algorithm is developed to solve the problem on larger scales. Numerical experiments demonstrate that our proposed algorithm is very effective and efficient in obtaining high-quality solutions for a practically sized problem.

This paper presents a two-stage stochastic optimization model for the assignment problem of aircraft to gate and charging stations in urban air mobility (UAM). We develop a rigorous mathematical model for the ground resource assignment with electric vertical takeoff and landing (eVTOL) aircraft in which the uncertain charging times and constraints of required infrastructure are captured. We employ scenario-based approaches to capture the uncertain charging times of aircraft in terms of a set of scenarios, thus reformulating the problem as a large-scale mixed integer linear program using the Python-based Pyomo modeling framework. The obtained problem, which is called deterministic equivalent form, can then be solved using the branch and cut algorithm embedded in available MILP solvers. Performance measuring criteria are also implemented to see the effectiveness of incorporating uncertainties in the ground resource assignment and indicate future improvements.

We examine the three-dimensional loading and vehicle routing problem (3LVRP), which has broad applications in logistics distribution operations. Due to the complexity of the 3LVRP, previous studies primarily focus on heuristic methods for its solution. These heuristic methods usually involve verifying loading solutions with a fast and simple constructive approach and exploring routing solutions using neighborhood search-based approach. In this study, we propose an improved solution framework for the 3LVRP. Instead of relying on a specific loading method, we utilize machine learning techniques to predict the feasibility of loading solutions and assess their compatibility with routing decisions. Our numerical tests demonstrate that machine learning has potential in predicting the loading feasibility across various scenarios and reducing the loading cost.

We consider a single machine scheduling problem consisting of n clients and q consecutive operational periods (e.g., days). Each client submits a single job to processing on each of the days and wants his jobs to be completed as early as possible. A solution is defined by a set of q schedules (one per day), and it is classified as a K-fair solution if the total completion time of any of the clients on the entire set of q days is not greater than K. The scheduler's objective is to obtain a K-fair solution with the minimum possible K value. The problem is known to be strongly NP-hard, but no practical techniques were developed for solving it. Our main goal is to close this gap in the literature by providing a set of tools to maximize the system's fairness. To do so, we design a mixed linear integer programming formulation, two greedy algorithms and a metaheuristic. We intend to compute the entire set of algorithms and to test the quality of the different algorithms by applying an extensive experimental study.

The railway manufacturing industry is paradigm-shifting due to advancements in Industry 4.0 (4IR). This technological revolution, marked by the integration of digital technologies, is transforming production processes and organisational structures. Employee empowerment, which involves equipping workers with the necessary tools, resources, and decision-making authority, is crucial in this context. This research examines the link between employee empowerment and 4IR digital technologies in railway manufacturing. It focuses on how 4IR technologies impact employee empowerment. The objectives are to (1) evaluate the adoption and use of 4IR technologies in railway manufacturing and (2) assess their impact on employee empowerment. Additionally, it identifies factors that facilitate or hinder empowerment in 4IR-driven transformations. A quantitative methodology using a questionnaire surveyed 179 railway manufacturing employees. Data analysis involved descriptive and inferential statistics, with Cronbach's alpha for reliability and Pearson correlation for variable relationships. The findings indicate that integrating 4IR technologies into railway production can enhance employee empowerment by promoting autonomy, skill development, and decision-making participation. However, challenges include implementation accuracy, infrastructure availability, and skills deficiencies related to 4IR technologies.

Morphological analysis has been considered as a prominent tool for generating new and creative ideas. However, it has mostly been relied on experts’ judgment, which has a risk of subjective and biased idea generation. Despite some previous work on integrating data into the morphological matrix, the synergistic effects of using multiple databases have been overlooked due to the reliance on a single data source. In response, this study proposes a morphological analysis using five data sources, each with different characteristics. The new concepts of WordNet Reinforcement and Wikipedia Reinforcement are developed for morphology building. We also suggest a detailed process for data-driven morphological analysis, with a proper customization framework. The proposed data-driven morphological analysis can help managers accelerate creative idea generation in practice.

Japanese capacitor companies have a high share of the global market. High reliability of electronic components plays an important role in ensuring high safety and performance of the entire system. This study focuses on corporate R&D activities related to electrolytic capacitors. This paper proposes a method for analyzing the number of patents that are the result of technological development and for deriving advancedness through textual analysis of patent information. The method was also used to investigate the advanced nature of technological development. The results show that although the number of patents is at a high level, there are issues with the advancedness of the technology.

Understanding cross-company innovation processes is essential for creating effective data spaces that foster collaborative activities. This paper presents a methodology for visualizing all forms of communication throughout the innovation process across organizational boundaries by utilizing social network analysis tools. By tracking and analyzing communication patterns—such as email exchanges, meeting protocols, and file transfers—valuable insights into organizational behavior, dependencies, and project dynamics are obtained. These visualizations reveal critical patterns and bottlenecks, guiding the design of data spaces specifically tailored to support cross-company innovation. Emphasizing the importance of understanding communication behaviors, the paper highlights the development of technologies that build trust and enhance collaborative innovation.

Digitalization, resilience, and agility are considered as essential aspects needed in turbulent environment.  However, despite the fact that these three concepts have become widely used among researchers and practitioners, the nature of the relationship between these notions has not been adequately established. Thus, this study aimed to develop a conceptual model that capture the relationship of digital transformation on agility and resiliency in business organization by adopting the systematic literature review method. Following the PRISMA framework that consists of identification, screening, eligibility assessment, and article included selection, this study identified 23 articles for thorough review. This study found that digital dynamic capabilities that consist of digital sensing, digital seizing, and digital reconfiguring have an impact on digital transformation. Digital transformation enables business to form agile business characterized by flexibility and timely response. Digital transformation also facilitate organization to build resilience capability that consists of capability to anticipate, response, and adapt towards any disruption and changing in environment. Finally, this study offers a set of hypotheses and a conceptual model that can be empirically validated in future study.

This study presents an approach to identifying emerging technology opportunities by extracting patent vacancies and concretizing their meaning in textual form. Patent abstracts are mapped into a high-dimensional vector space using a text embedding model, then reduced to a two-dimensional map using an autoencoder. Density estimation is applied to these coordinates to identify hotspots and define vacant cells as patent vacancies. The two-dimensional coordinates of these patent vacancies are then converted back into high-dimensional embedding vectors using the decoder of a trained autoencoder. Finally, the embedding inversion model converts the embedding vectors into text describing the technology overview. For validation, 7,413 patents related to solar cell technology registered in the last ten years as of 2023 were collected. The first eight years of patent data were used to extract vacancies and generate technical text. Consequently, patents exhibiting a resemblance to the generated text were observed to emerge in the subsequent two years, thereby substantiating the innovative potential of our approach.

Due to the emergence of aging population and family-work conflict, numerous home technologies are developed to help improve family routines. A domestic social robot is getting more attention, but the influence of personal characteristics and cognitive perceptions from home members’ perspective is seldom discussed. Drawn upon the theory of technology acceptance model and personal innovativeness, this study develop an integrated model to explore the factors related to home members’ intentional acceptance of domestic social robots. The results reveal that individuals’ perception of usefulness, ease of use, and enjoyment are significant factors influencing their attitudes toward the use of social robots at home. Personal innovativeness is also identified as a critical factor affecting appraisals of robot assistance in household routines and companionship.

In the era of knowledge economy, enterprises begin to pay more attention to the role of intellectual property rights in internationalization strategy. The relationship between intellectual property internationalization and corporate performance becomes an important concern for most industries. However, through a systematic literature review we find that the existing researches are often limited to patent internationalization and there are few studies on trademarks internationalization. Based on this, we use the manufacturing enterprises listed in China’s stock markets from 2010 to 2021 as samples to analyze the relationship between trademark internationalization and corporate performance. Our findings show that and add the moderating effect of patent internationalization. We find that trademark internationalization promotes corporate performance, but patent internationalization inhibits this effect.

Sustainability integration in Project Portfolio Management (PPM) within the extractive sector (mining, oil, and gas) remains underexplored despite its importance in achieving corporate sustainability goals. This study investigates the challenges and enablers influencing the integration of sustainability into PPM in these industries. Using qualitative research methods, we identify multi-level factors at strategic, portfolio, and project levels. The findings emphasize the need for strategic alignment, fostering a sustainability culture, and holistic integration strategies. Key challenges include a profit-centric approach, organizational culture resistance, incomplete integration, inadequate training, and ineffective knowledge management. Additionally, short-termism, market volatility, and fragmented knowledge sharing pose significant barriers. Conversely, enablers such as stakeholder expectations, reputation management, regulatory adherence, proactive sustainability reporting, comprehensive training programs, and robust knowledge dissemination processes offer pathways to enhance sustainability practices. Theoretical contributions include expanding the understanding of multi-level factors and providing a comprehensive framework for future research. Practically, the study offers actionable insights for managers to develop targeted strategies, comprehensive training programs, and proactive stakeholder engagement to achieve sustainable development goals in the extractive industries.

The Online food delivery services (OFDS) have been extensively utilized, especially in developing nations like Indonesia on new normal Covid-19 situation. The present research aimed to utilize the “Unified Theory of Acceptance and Use of Technology-2” (UTAUT2) in affecting the customer satisfaction and loyalty (CSL) in OFDS in Indonesia post-COVID-19 pandemic. The 253 data was taken to answer the 65 indicators. Structural equation modeling (SEM) indicates that hedonic motivation exerts the most significant influence on CSL, succeeded by price. This study unexpectedly found that usability factors, including performance expectation habit, did not influence customer happiness and CSL in OFDS. This research strongly provides a theoretical framework for OFDS practitioners, IT developers, and scholars. The present research can be modified and expanded to different nations to examine the determinants of customer CSL in OFDS.

It is said that many companies have not achieved sufficient results from digital transformation (DX); therefore, companies must gain the ability to efficiently promote DX. The purpose of this study was to propose improvements to promote efficient DX by clarifying the differences in the factors of organizational culture that influence the promotion of DX based on differences in company size. By applying multigroup structural equation modeling using the results of previous research, the differences in factors between small and medium-sized enterprises (SMEs) and large enterprises (LEs) were identified. Based on these results, it was proposed that SMEs should focus on improving the characteristics of members of DX promotion organizations, and that LEs should focus on improving the management and capabilities of DX promotion organizations. It is expected that companies will efficiently promote DX by considering these results.

Commercial digital platforms are being developed today to address the global food waste problem and increase the amount of food rescued. This paper develops a research model to help map out and investigate potential reasons underlying consumers’ intention to buy surplus food on a mobile application (app). Previous studies have found a variety of potential variables, but no study has fully captured them in a model. Furthermore, and importantly, the model suggests that consumer attitude towards ecological aspects of food consumption is positively affected as a result of buying surplus food. Scales to measure each variable in the model are also developed and presented. Next steps for testing the model are also discussed.

As users migrate from traditional e-commerce platforms to streaming platforms, showing interest-based recommendations and interactions, understanding consumer purchase decisions in live streaming e-commerce is crucial for businesses. This study constructs a theoretical behavioral model of consumer purchase intention based on the SOR theory, examining the mediating roles of trust and interest. Empirical research reveals that the authenticity and professionalism of recommendations, as well as the timeliness and effectiveness of interactions, influence consumer purchase intentions through trust and interest, with trust having a chain mediation effect on interest.

This study aims to investigate users’ preferences for future smart city transport ticketing services. A conjoint experiment was arranged and conducted whereby participants (n=126) indicated preferences based on a simulated ticketing purchasing scenario. Our main results show that users prefer a ticketing service that allows them to purchase a ticket digitally anytime and anywhere. They want the ticket to take them from where they are to where they want to go, regardless of the transportation service provider. In addition, users prefer a fixed price ticket rather than a dynamic one, and they want to have ownership of the ticket, meaning they can use any ID to prove that they have a valid ticket.

Retailers are also beginning to explore gamification strategies to provide users with a better experience through gamification elements. Gamified platforms not only bring a large number of active users but also provide users with social interaction, shopping vouchers obtained through playing games, charity events, and participation in promotion activities. These platforms’ games attract customers play games even without a purchase need. If customers can receive better rewards, customers could be encouraged to use the same platform more frequently, and then increase their dependency of the mobile e-commerce platform. Customer can regularly engage with the mobile e-commerce platform for playing games and then cultivate their attachment to the platform. This study develops the research model based on the MDE framework. This study conducted an online survey for data collection. Results derived from the 788 valid returned data support all hypotheses. Gamification increase perceived value, social interaction and achievement, and in turn enhance brand attachment. Personality traits moderates the relationship of gamification and users’ perception of using mobile e-commerce platforms. Theoretical and managerial implications are also listed.

This study uses a conjoint analysis approach to determine the key factors influencing subscriber preferences for subscribing to Netflix plan. In a rapidly evolving industry, understanding the drivers of subscription choices is critical for market success. Through a designed survey, respondents are presented with different hypothetical Netflix subscription plans characterized by attributes such as price, resolution, same time watch device, downloadable device and device specs. By analyzing respondents' answers from these attributes, researchers aim to find out the relatedness of each attribute and levels with the subscription preference.  The study's findings can help Netflix make more strategic decisions about plan options and pricing policies, so that it can better serve the wide range of demands and tastes of customers in the cutthroat streaming market.

Individual preferences change over time, requiring recommendation systems that adapt and provide personalized suggestions. This paper introduces a novel approach called Preference Tracing, inspired by knowledge tracing from the educational domain. Knowledge tracing estimates a student’s knowledge state from interactions with question-response pairs and knowledge components, which are essential for solving given exercises. Based on the estimated knowledge state, the model predicts the probability of correctly answering subsequent exercises. Similarly, Preference Tracing estimates a user’s preference state from rating histories, including movie-rating pairs and a movie component. Movie plots were crawled from Wikipedia, IMDb, and Letterboxd, and then latent Dirichlet allocation (LDA) was applied to define each film’s top-weighted topic as a movie component. Based on that, Preference Tracing can track users’ changing preferences and predict whether a user would like a given movie. Our main contribution demonstrates that Preference Tracing delivers hyper-personalized recommendations by adapting to changing individual preferences. Experimental results on MovieLens 1M show that Preference Tracing outperforms traditional baseline models and effectively captures dynamic changes.

Airlines generate significant revenues from ancillary services which has emerged as a pivotal source enhancing the travel experience with ancillary fares supplementing the basic travel fare. However, existing literature has largely overlooked traveller’s willingness to pay for ancillary services offered by low cost carrier (LCC) and full-service carrier (FSC). Our study utilized choice-based conjoint analysis to assess the traveller’s utility and further estimating the relative importance and willingness to pay for their travel. FSC travellers are willing to pay more than LCC for the same travel destination with same travel time. Seat properties has been the most important for both the aircrafts and the next important attribute are check-in luggage for LCC and priority services for FSC.

The shipping industry is an essential sector of a country’s economic activities, requiring adaptation to current technological advancements. However, there is a need to understand which technologies require priority, as these advancements cannot occur instantaneously. In this context, Sultanate of Oman located in Middle East area is expected to experience improvement and diversification in economy through significant development in shipping industry. The adoption of digitalization and embracing Industry 4.0 should be prioritized by Oman’s government to improve various aspects, including efficiency, productivity, safety, and security, ensuring compliance with sustainability issues and competitive advantage. Therefore, this study aimed to explore the requirements of Oman’s shipping industry regarding the adoption of Industry 4.0 technologies using mixed methods. Thematic analysis was used to identify the essential technologies, while fuzzy multicriteria decision-making was applied for prioritization. A total of eight expert opinions were collected to rank qualitative attributes and technologies for Industry 4.0 implementation. The fuzzy AHP was used to measure the weight of qualitative attributes, while fuzzy TOPSIS determined the values of each attribute for selected technologies and calculated prioritization for adoption. The result showed that among Industry 4.0 technologies, Big Data Analysis, Artificial Intelligence, and Blockchain required prioritization. Meanwhile, challenges such as IT infrastructure, employee readiness, and stakeholder coordination must be addressed to ensure the successful implementation of Industry 4.0 technologies.

This study constructs a ‘sustainable raw material supplier selection framework with imprecise information for tire production (SSSIITP)’ in the context of Extended Producer Responsibility (EPR) based on the existing “Z-Number Slacks-Based Measure (ZN-SBM) DEA model-based framework.” The SSSIITP framework selects sustainable raw material suppliers in the presence of imprecision in the available information by capturing its reliability degree by using Z-numbers. The suppliers are evaluated based on seven different raw materials required for tire production, namely “synthetic rubber,” “natural rubber,” “carbon black,” “steel,” “textile,” “zinc oxide” and “sulfur.” The SSSIITP framework is applied in a case study of sustainable raw material supplier selection for new tire production in Indian scenario. The findings indicate that the results of the SSSIITP framework are highly correlated with most of the state-of-the-art Z-number-based ‘‘multi-criteria decision making’’ methods. However, unlike these existing methods, the SSSIITP framework is free from the influence of the implicit biases of the decision-makers. This study offers a more reliable decision support tool for the tire producers to select sustainable raw material suppliers with imprecise information in the context of EPR.

We consider a multicriteria optimization problem where each criterion can depend on the realization of an ex-ante unknown state and where the weights of an additive scalarization are unspecified. This type of decision problem arises, for example, when assessing the lifecycle impact of different car types in terms of their respective effects on human health, ecosystems, world climate, and natural resources. Indeed, the measurement of the individual effects may be subject to state ambiguity, since the marginal damage costs may be unknown. We determine robust weights which maximize a relative performance index. Our approach also yields a robust state estimate, as well as an associated set of optimal robust decisions. Thus, in addition to producing a performance guarantee relative to all feasible decision criteria in the ambiguity set, the approach endogenously identifies the decision criterion with respect to which the optimal robust decision can be rationalized. The main results assume that the underlying action and parameter sets are compact and that the criteria are continuous. In the absence of functional forms, the approach becomes entirely data-driven, dropping the preceding assumptions.

Smart park shuttle service systems (SPSSS) play a crucial role in the future smart city, but there is currently no comprehensive development approach that considers the user at every stage. This study proposes a user-centric development approach for SPSSS, which includes: (1) creating user journeys to uncover user requirements and design opportunities; (2) analyzing elements such as human factors, machinery, and the environment to clarify the service system model; (3) identifying user touchpoints to define system functions; and (4) involving users in evaluations to facilitate decision-making. This approach constructs an SPSSS with shuttle vehicles, an online booking platform, and park navigation stations as the front-end, and a cloud-based system as the back-end. Furthermore, user involvement in evaluations helps refine shuttle vehicle design. This study offers significant value by promoting a user-centric perspective for the development of service systems.

The main impediment to the conversion from conventional to organic farming is the financial difficulties that farmers experience during the transition period in terms of decrease in yield and increase in farming costs owing to transitional practices. Furthermore, uncertainty in crop price and yield may aggravate the adverse effects of transitional practices. This article presents a multi-period optimization model for the allocation of farmland among crops and agricultural practices which allows farmers to plan a transition to organic farming while incurring a bounded shortfall of income. We calibrate our model to represent a grower of corn and soybean in Iowa and, using a seemingly unrelated regression model, crops revenues are simulated and utilized in the numerical experiments. The results show that i) our optimized crop rotation pattern outperforms other policies in the agriculture industry, including monoculture and systematic crop rotation, and that ii) our gradual conversion plan mitigates the chance of profit shortfalls.

Multi-objective optimization seeks to arrive at a diverse set of Pareto-optimal solutions facilitating a posteriori decision-making. However, this becomes challenging for high-dimensional problems with limited compute, imposing a compromise between convergence and diversity of the final solutions. To address this curse of dimensionality, we introduce the concept of Pareto set representation learning, reducing the problem to its smallest possible dimensions while accurately capturing the Pareto-optima. A denoising autoencoder is invoked to discover a compressed latent representation of a sparsely populated Pareto set by leveraging its unique bottleneck architecture. This representation then serves as a means to create compact inverse models, mapping points from the Pareto front in objective space to the (dimensionally reduced) Pareto set in decision space. The method is empirically tested on benchmark problems and an industrial multi-site order planning problem showcasing its effectiveness in reducing the dimensionality of the Pareto set (~99.6%) while achieving significant gains (>200%) in Pareto approximation capacity. With such compact yet accurate inverse models, decision makers can readily generate high-dimensional solutions corresponding to any preferred, unexplored subregions of the objective space.

This research paper examines the topic of secure data exchange in a supply chain within the manufacturing sector. The objective is the development of a data platform that optimizes operational efficiency and promotes cross-company collaboration. To achieve this, helpful tools are utilized and suitable standards are followed to create a secure system. Security measures are determined by conducting a risk analysis to identify, evaluate, and compensate for potential threats. Furthermore, the utilization of non-transparent federated learning models in combination with a method of security design of components contributes to the information sovereignty of data owners. In conclusion, secure data sharing practices play a pivotal role in supporting collaboration and operational effectiveness in the manufacturing industry.

The analysis of near miss events in industrial companies has been widely recognized as an effective tool to improve the effectiveness of the safety management process at workplaces. The use of near miss management systems (NMSs) is mainly diffused in sectors with major accident hazards (e.g. chemical, nuclear, etc.), while it is slowly spreading in other sectors, like manufacturing and construction. One cause is the lack of resources, outlined especially in micro or small companies. losing an important source of knowledge about safety in industry. This work presents a web-based tool for supporting the adoption of NMSs, especially in SMEs The tool has two targets: from one side, it provides companies with a standardized method to collect and analyze near miss data. On the other side, the platform allows implementing a monitoring system for near miss events based on data collected from different companies, with the possibility to elaborate specific analysis for different stakeholders (e.g. single companies, employers’ associations, national surveillance system, etc.).

People are becoming fatigued and helpless with privacy issues in social media. Exploring the influencing factors of privacy fatigue and privacy helplessness and the consequences of disengagement caused by them is of positive significance for individual privacy protection and the healthy development of the industry. Based on the theory of Privacy Calculus Theory and the theory of planned behavior, the research integrates individual perception elements to construct a model, and conducts empirical analysis through a two-stage approach combining structural equation modeling and artificial neural network (SEM-ANN). Through empirical analysis, this study reveals the impact of privacy fatigue and helplessness on social media users' disengagement behavior and its influencing factors. At the same time, the study also demonstrates the differences between SEM and ANN models in measuring the degree of influence of variables, and emphasizes the advantages of ANN models in dealing with complex relationships. These findings have important implications for guiding the protection of personal privacy and the healthy development of the social media industry.

Risk management (RM) is crucial in product development processes in the engineering domain since mitigating risks ensures the satisfactory product performance. Existing RM approaches in engineering require numerical inputs converted from textual data, which are manually collected from risk reports and converted into numerical inputs by human experts via their experiences. The manual process of doing so is laborious. Since natural language processing (NLP) techniques can process textual data in a similar way that humans comprehend textual data, NLP techniques can potentially automate the process of obtaining numerical inputs from textual data. Therefore, we experimented with multiple NLP techniques to automate the process of collecting numerical data from risk reports that serve as the inputs to RM approaches. Our method performed risk identification and analysis, during which textual data from risk reports were converted into numerical data via NLP techniques like generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). Parts of risk identification and analysis were successfully performed, but some results are not accurate due to NLP techniques not being able to understand causal relationships.

This exploratory study uses the occurrence severity in maritime accidents as the main target variable for prediction considering several input variables including vessel types. Historical accident records data of three Nordic countries – Norway, Sweden, and Denmark are collected for the period January 2013- March 2024 from the EMCIP database. Season, day of the week, and month of the year variables were created based on accident date to account for weather-related factors. A total of 41 machine learning models were trained. The models were optimized for highest area under the curve (AUC). The Light Gradient Boosted Trees Classifier with Early Stopping (SoftMax Loss) (64 leaves) is the best performing in terms of accuracy. The top two features, 'CENTROID_X_geometry' and 'CENTROID_Y_geometry', feature engineered by AutoML, have the most significant impact, both exceeding 90% impact. The 'Time (LT) of occurrence' follows with an impact just below 90%.

Many industries understand about data privacy which also affects the maritime industry. Data protection has been widely approached with various of assessment methods for improvement of security and privacy. The maritime industry needs assessment methods to enhance data protection effectiveness to achieve customer confidence, regulation compliance, and improve risk management. This research combines DPIA, an assessment method specified by the GDPR, the EU data protection regulation, with other assessment methods commonly used in the maritime industry including EAST, STRIDE, Taxonomy, and STAMP to assess and mitigate risks. BAS is chosen as the foundation system for this study because it places the highest priority on personal data compared to other existing systems. Each assessment process will undergo rigorous validation through in-depth interviews conducted with relevant domain experts. In addition, the taxonomy validation employs the Delphi method, utilizing two rounds of expert feedback to ensure greater accuracy and consensus. The primary outcome of this research is a generic impact assessment framework, which will serve as a valuable foundation for future studies that seek to apply it to specific maritime businesses or processes.

Offshore floating structures are subjected to random loads with repeating cycles that may occur the fatigue in the mooring lines. That can be one of the important causes of incidents for offshore floating structures while exploiting oil and gas as well as exploiting renewable energy. The paper focuses on the methodology of the fatigue damages assessment problem for mooring system using nonlinear random dynamic tension simulations of mooring lines in time domain under the effect of annual statistical sea states. From there, the T-N fatigue curves for mooring lines are studied and the “Rainflow” method is applied to count the cycles of tension expressions in the mooring lines. Finally, the Palmgren-Miner rule is applied to calculate the accumulated fatigue damages and the fatigue life for mooring systems. In the numerical simulation section, the paper conducts the fatigue analysis to assess the safety for the mooring system of FPS-DH01 semi-submersible platform at Dai Hung field, in the South of Vietnam Sea.

The paper discusses the factors contributing to train derailments from the perspective of South African authorities. The research aims to analyze the patterns of derailment within the South African railway industry and forecast future trends using time series models. The study utilized secondary data from the Railway Safety Regulator (RSR) covering the periods of 2010/11 to 2017/18 and 2018/19 to 2022/23, totaling 13 years of rail safety data. The paper is structured into five sections and uses a mixed-methods approach based on established literature. The research revealed that the frequency of train derailments has shown a consistent decrease over the years, and this trend is anticipated to continue in the future. The most common causes of derailments include broken rails, faulty welding, and obstructions on the tracks, which have the potential to cause damage to the train, tracks, and overall infrastructure. Human error is also a significant contributing factor to incidents in the yard and sidings. The study highlights that various factors, including the technical condition of rolling stock, track maintenance, human error, and operating conditions, can influence derailments.

With the rise of electric vehicles (EVs), managing end-of-life EV batteries effectively becomes crucial. Repurposing these batteries presents a promising solution to mitigate environmental impact but necessitates rigorous safety and state-of-health assessments. This paper reviews literature and industry interviews to examine the role and challenges of visual inspection in EV battery repurposing. Despite its crucial role alongside electrical testing, comprehensive documentation on visual inspection remains scarce. Industry insights highlight the significance of visual inspection in identifying end-of-life indicators and ensuring safe battery handling. Challenges include lack of standardization, battery safety knowledge gaps, and inefficient processes. This study underscores the need for standardized, efficient, and safe repurposing procedures and proposes a model that illustrates the role of visual inspection throughout the battery life cycle.

Manufacturing companies operate in an increasingly volatile environment. Due to external influences, they frequently have to make changes to their production. These Manufacturing Changes (MCs) occur in a great variety and number. While standardized processes are commonly used to address MCs, their wide range of variants requires tailored approaches. To make such change-specific adjustments and decisions, a systematic characterization of MCs is required. Therefore, a model for the systematic description and characterization of MCs was developed for this contribution. To this end, a three-step approach was applied: a literature review, an online survey, and expert interviews. The MC model supports manufacturing companies in characterizing changes to handle them individually, thus increasing the effectiveness and efficiency of MCs.

Automation and robotics are known as one of the key technological enablers of industry 4.0. In the world of industrial production, automation and robotics are the leaders of digital transformation. While there is abundant research done on the nature of Pick and Place operations, with all manner of robotic specification, vision and sensor solutions, and machine learning, there is a need to further investigate how these elements can be accessed by SMEs in a fast, efficient, and effective way. The intention of this study is to find, through research and application of an automated system, solutions that will allow SMEs to digitally transform their processes.

Amid growing pressures for sustainable operations, the manufacturing industry faces methodological, knowledge-related, and organizational challenges in employing existing Life Cycle Assessment (LCA) tools effectively. Addressing LCA’s limitations related to static data and complex system boundaries, this paper presents an information modeling framework designed to enhance LCA applications. The study adopts a systematic approach using Unified Modeling Language (UML) to organize and visualize LCA data efficiently, based on the ecoinvent database. This framework is prototypically implemented and tested in an industrial use case involving the assembly of video surveillance cameras, demonstrating its capability to support dynamical assessments of sustainability performance. Aiming at bridging LCA with advanced digital technologies that are based on information models and interfaces, this framework proposes a concept for more accurate and adaptive sustainability evaluations in manufacturing, offering a pathway towards more informed and responsive environmental management.

The single-point incremental forming procedure (SPIF) has the benefit of exceptional formability. Nevertheless, the intricate geometry of the workpiece remains a challenge for achieving desired shape changes using the SPIF process. Thus, this study aims to analyze the outcomes of the asymmetric shaping of cone-shaped workpieces. The characteristics that were examined were the tool's stepdown and movement direction. An analysis is conducted to examine the alterations in the wall thickness, surface roughness, and microstructural damage of the workpiece following the forming process, using SEM. The findings indicate that the process of step down has a notable influence on the level of surface roughness. The phenomenon of a wider cone angle leads to a reduction in the thickness of the workpiece, which in turn leads to the formation of small internal cracks and eventual failure during the forming process. As the thickness diminishes, the quantity of microcracks augments, rendering the material incapable of withstanding the force exerted during the forming process.

This study relates to a novel synthetic protocol in preparing diversified entities of benzimidazolyl phosphine ligands via simple “One-pot assembly” and “cross-matching” approaches from benzimidazoles, acid chlorides and chlorophosphines. Combining these starting materials enables a significant diversification of the ligand structure.  Several strategic points are considered: 1) the synthetic pathway should be direct and efficient; 2) the starting materials should be easily accessible and cost-effective; 3) the diversity and tuning of the ligand should be readily achievable; 4) the ligand synthetic steps should adhere to the principle of atom economy; and 5) the ligand framework and the substituted groups should have potential hemilabile properties for transition metal-catalyzed cross-coupling reactions. This protocol even allowed scale-up to a sub-kilogram level and potentially to an industrial manufacturing level. Acknowledgment: This work is supported by UGC/FDS16/P02/23 from the Research Grants Council of Hong Kong.

This paper presents a method for determining the gripping point of a freely hanging cable using a 2D camera and a single image. For this purpose, an algorithm was defined, which calculates the gripping point based on the cable end. Subsequently, tests were conducted to assess how effectively the developed method can be applied to the domain of automated control cabinet wiring. The implemented distance calculation between the camera and the cable was the biggest hurdle for the system’s process reliability. In this context, it was found that the coordination between the defined computer vision algorithm and the gripper design is very promising, because longer gripper jaws can compensate for an incorrectly performed distance calculation. In this work, the current state of development is presented, and the next steps are explained.

Outcome-Based Business Models (OBBMs) shift the focus from selling the products to selling the performance outcomes of the products by using advanced digital technologies for decision-making and shifting the logic from “creating value for” to “creating value with” the customer. As a result, they become dependent on complex socio-technical value co-creation systems required for service innovation. Service innovation in OBBMs requires continuous collaboration between the parties, which is neither simple nor always successful. However, to our knowledge, no model or framework exists to assess readiness for service innovation in OBBMs. Hence, we used the Organizational Readiness for Digital Innovation model to address the gap by reviewing it in the context of service innovation in OBBMs. It revealed that a co-innovation approach and consideration of the customers’ resources, capabilities, and readiness are required to develop the model further in this context. This creates a firm foundation for further customization and contextualization of the model by contributing to the conceptual knowledge of organizational readiness for digital innovation and providing insights for collaborative service innovations in OBBMs.

Polyethylene Terephthalate (PET) is a type of municipal waste that contains a large amount of PET. The nature of PET waste that can pollute the environment has encouraged researchers to conduct research starting from selecting raw materials that are more environmentally friendly to encouraging PET waste from customers for recycling. This system is referred to as a reverse supply chain network or reverse logistics. The players in the reverse supply chain include end users, waste collectors, collectors, and remanufacturers. The purposes of this research are to identify the flow of PET waste from end customers until it can be processed by a recycler to produce economically valuable derivative products. The research method uses literature, empirical studies, and benchmarking. According to the results of this research, the integration of the reverse logistics model into PET waste management involves several entities:  waste generators, waste pickers, public and private collection centers, the government, and recycling industries.Keywords – Collaboration, Conceptual Model, PET, Reverse Logistics, Stakeholders, Waste Management.

Uncertainty in the global market and on the shop floor develops risk and eradicates sustainability in the food supply chain (SC). Resulting, the disturbed SC is creating high cost, poor quality and burglary in the system. Machine learning (ML) and Blockchain (BC) are the two new disruptive cutting-edge technologies with multiple characteristics. Therefore, the integrated concept of ML and Blockchain technology (BCT) comes out as the best solution for achieving sustainability, transparency and resilience in disturbed SC and transforming it into viable SC. Therefore, for viable SC, the authors have developed a stochastic MINLP mathematical model to optimise the supplier selection model in real-time. Later on, the proposed stochastic MINLP model is validated using 5 different types of randomly generated datasets. Concludingly, this stochastic MINLP model gives better results than the traditional MILP model in real-time to counter high cost, fraud, poor quality, and mutability in data and burglary in the system.

We examine a basestock inventory model with service level agreements (SLAs) expressed in terms of short-term fill rates. We present exact formulas to compute the basestock level for the gamma-distributed demand and devise a procedure for non-parametric demand. We show that increasing the length of the performance review horizon implies an increase in both the estimated basestock levels and the associated estimation errors. Moreover, we consider the scenario when the supplier aims to design an inventory system to meet the target fill rate with a given probability. The supplier's `risk' is defined as the probability of not reaching the target fill rate. We explore utilizing Hoeffding’s inequality and a simulation approach to manage such defined risk. The decision-maker can use the presented framework to assess the required basestock more precisely under the specified level of risk.

This paper investigates the widely adopted single-supplier multi-retailer cold chain in the food industry. The goal is to design and manage a cold chain for fresh produce with deterministic demand by minimizing the total cost, including cooling, loss of value, and carbon emission costs. The study integrates the global stability index (GSI) method and the non-Arrhenius model to describe food quality degradation. The power-of-two (PoT) policy is used to determine coordinated replenishment policies for the supplier and retailers and an appropriate wholesale price structure for chain coordination. The numerical examples investigate different scenarios and show how cold chain parameters influence optimal decisions. Additionally, the paper compares uncoordinated and coordinated cold chains and highlights the importance of a coordinated wholesale price scheme instead of a constant price.

This paper analyzes lean manufacturing approaches to improve operational efficiency and minimize waste in a South African sugar packaging company. Facing challenges in waste reduction and efficiency, the study applied the DMAIC technique, using tools like the Ishikawa diagrams to delve into waste reduction. Actions were prioritized with matrix prioritization, leading to a strategy that significantly reduced waste, especially in the 500g SKU. Despite obstacles with 1 kg SKUs, the DMAIC framework improved efficiency from 70% to 90%. Recommendations include improving supervisor handovers and supplier performance reviews. The study highlights lean methodologies' effectiveness in enhancing supply chain efficiency and profitability. The paper advocates for continuous improvement and the adoption of DMAIC and associated tools to systematically address inefficiencies and enhance overall performance in the supply chain.

The aim of this is work to propose improvements to reduce the time for handling logistic complaints in an electronic components company, tier 1 supplier to the automotive industry. Six Sigma methodology was used, allowing to identify the variables that influence the logistics quality complaint process since it provides an organized structure for the definition of defects, analysis and problem solving. This resulted in a 65% reduction in complaints handling time and a 79% reduction in variability, leading to a more efficient and robust process. It was observed a greater commitment and motivation from the team in handling the logistic complaints.

The well-being of operators plays a key role in enhancing efficiency, productivity, and demand fulfillment in manufacturing systems. Improving both ergonomic conditions and operational effectiveness is essential for cost optimization, taking in account inventory cost, while meeting demand. This paper explores the integration of ergonomic considerations with production strategies. Recognizing the interconnectedness of these factors, we propose practical solutions to enhance system performance while safeguarding operator health. A model is developed incorporating inputs from ergonomic metrics, production process and production demand. Thus, the total production cost was optimized under the predefined constraints to balance productivity andworker well-being while meeting production targets within service level agreements. The results of our model demonstrate the effectiveness of our ergonomic strategy in optimizing production plans, which enhances production output and reduces costs while balancing the workload for operators.

Environmental and energy independence concerns lead to government subsidies for electric vehicles (EVs). We model the interactions between the government and the charging supplier as a Stackelberg game and study the optimal structure of subsidies by incorporating charging inconvenience. We prove that this inconvenience is decreasing convex in the number of stations. In the expenditure minimization case, the optimal policy depends on the government adoption target and the charging station construction cost. If the adoption target is below a threshold that depends on the construction cost, the government provides pure consumer subsidy or no subsidy; otherwise, a combination of consumer and station subsidies is optimal. As the construction cost increases, the charger builds fewer stations, regardless of the subsidy type. In a real-life case, we find numerically that a station subsidy alone is optimal if the construction cost is not low but the adoption target is low. Besides, a long driving range reduces the need for subsidies significantly if the construction cost is high, whereas a long charging time necessitates high expenditure allocated mostly to a station subsidy.

Human-automation symbiosis (HAS) is a key aspect of Industry 5.0, marking the collaborative relationship between humans and automation systems. Nudging, a behavioral economics concept that indirectly encourages individuals to act in a certain way through subtle interventions, can be applied in the manufacturing context to improve the collaboration efficiency between human and automation agents. While manufacturing nudges facilitates the symbiotic relationship, they may also impact certain aspects of manufacturing system performance. Therefore, the objective of nudging personalization is to achieve HAS while maintaining system performance for a group of people, which suggests a multi-objective optimization problem. In this paper, a behavioral economics model for nudge evaluation using the conjoint prospect theory is proposed, and the formulation of manufacturing nudging personalization optimization is proposed, which is defined as the prospect value of the selected nudges relative to their engineering cost. A 2D genetic algorithm (GA) is employed to solve the optimization problem. Its feasibility and effectiveness are validated through a jet engine assembly case study.

This study explores the optimization of combinations based on single intentional blinking actions, aiming to enhance the efficiency of eye-controlled interaction systems. The experiment selected three basic actions: simultaneous bilateral blinking (SB), single right-eye blink (SBR), and single left-eye blink (SBL), which were paired to form six combinations: SB+SBR, SBR+SB, SB+SBL, SBL+SB, SBR+SBL, and SBL+SBR. The results indicated that the SB+SBR combination had the highest recognition success rate (93.75%), while the SBL+SB combination had the lowest success rate (90.25%). Analysis of overall completion time, single action completion time, and inter-action interval time revealed that the order of blinking actions significantly affected the completion time of SBL, whereas the durations of SB and SBR were relatively stable. Subjective evaluations indicated that combinations containing SB had a lower subjective load. SB+SBR and SB+SBL are recommended as blink control commands. This study provides theoretical support for designing efficient intentional blink interaction systems.

This study explored the work-related feelings generated by some common job conditions and comparing results from two different countries to investigate differences. Two sample were collected separately from middle and high school teachers in Jordan and The United States. Data were analyzed using partial least squares structural equation modeling (PLS-SEM). The final results indicated differences in teachers reaction to harsh job conditions between Jordan and The United States suggesting that Significant differences in the direction of tested relationships were found between the samples of both countries. Knowing these relationships would raise organizational awareness, help teachers manage their feelings and mitigate potential stress or adverse health conditions.

Work-related musculoskeletal disorders (WMSDs) are prevalent among construction workers, negatively impacting their occupational health, safety, and working performance. Though various ergonomic risk assessment methods have been developed, limited of them have integrated different indicators to provide a more comprehensive assessment scheme based on diverse data sources. Thus, this study proposes a framework that considers both postural and physiological perspectives to narrow this research gap. It integrates the computer vision-based postural assessment and the cumulative muscle fatigue analysis using electromyography (EMG) sensors. Then, the fused results can be obtained through a knowledge-based risk matrix. The proposed method has been applied in a realistic case study to demonstrate its effectiveness and feasibility. This study contributes to enriching the ergonomic risk assessment methods based on data fusion and the adoption of different digital technologies. It has the potential to facilitate effective ergonomic risk management, thereby promoting OHS in the construction industry.

Autonomous driving technology has the potential to significantly reduce the number of traffic accidents. However, until full automation is achieved, drivers will still need to take over the vehicle in complex and varied scenarios that the autonomous driving system cannot handle. Therefore, optimal takeover time budget is required to enhance takeover performance and driving safety. This paper designs takeover tasks in different hazard visibility scenarios (obvious hazard scenarios, hidden hazard scenarios) and investigates the effect of different takeover time budgets (5s, 7s, and 9s) on takeover performance through ergonomics experiments. Performance analysis and subjective evaluation methods are further used to analyze the data on driver takeover performance under different hazard scenarios and takeover time budgets.

This study examines the impact of workplace design on technostress and physical discomfort among BPO employees in the Philippines. The objective was to develop a model showing how workplace design affects physical comfort, mediated by technostress. A quantitative, descriptive research design was used, surveying 383 call center agents from CALABARZON. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLSSEM). Results indicate that poor workplace design significantly increases technostress, leading to greater physical discomfort. Technostress mediates the relationship between workplace design and physical discomfort, highlighting its role in employee wellbeing. The study emphasizes the need for ergonomic improvements and supportive organizational practices to reduce technostress and physical discomfort, enhancing productivity and satisfaction in the BPO industry.

E-mobility has emerged as a substantial catalyst with the potential to significantly influence a nation's advancement towards the attainment of the United Nations' Sustainable Development Goals (SDGs). In order to enhance the e-mobility infrastructure, policy makers consider various aspects such as cost of setting up the operations, consumers’ convenience and coverage provided by the enhanced infrastructure. In order to reap true benefits of e-mobility initiatives, it is important that it is available and accessible to all sections of a country including economically and (or) socially disadvantaged groups. In this work, the factors that are important to assess the equity dimension of e-mobility infrastructure are determined. In this work, an MCDM method is developed to quantify the equity aspect of a location with respect to the need of e-charging infrastructure. This score helps us to assess the relative importance of a location in term of its e-infrastructure needs and these values can be used for other downstream decision-making models.

This study assesses the efficacy of various cognitive workload metrics in human-robot collaborative assembly tasks using a systematic review and meta-analysis of literature from Scopus and Web of Science. Key metrics evaluated include physiological (EEG, GSR, HRV), subjective (NASA-TLX), and behavioral measures. Findings reveal that physiological measures, notably EEG and GSR (e.g., EEG with p < 0.01 and GSR with p < 0.01), are highly sensitive to changes in cognitive workload but are constrained by technical challenges. Subjective assessments, particularly NASA-TLX, provide valuable perceptual insights (p < 0.05), while behavioral metrics reflect task performance impacts. Integrating these metrics is essential for accurate cognitive workload assessments in industrial settings, enhancing both the understanding and management of cognitive demands.

Tourism is a sector with significant potential for economic growth, provides educational and experiential benefits. Adequately preparing the local community with the right knowledge and understanding of the tourism industry is crucial. The decline in tourism performance can be attributed to the limitations in the knowledge and skills of human re-sources in the tourism sector, as well as suboptimal quality of service and tourism development. This research aims to elucidate the influence of human resources, quality service, and tourism development on tour-ism performance. Data is analyzed using Partial Least Square-Structural Equation Modeling to examine the relationships between the variables under investigation. The findings of this research indicate that human resources significantly and positively influence quality service. Furthermore, it is evident that quality service affects tourism performance in the industry, and tourism development also exerts a significant influence on tourism performance in the industry. This study contributes to the understanding of the intricate dynamics of human resources, quality service, and tourism development in shaping tourism performance in Lasem.

With the rapid adoption of electric vehicles (EVs) in Indonesia, managing the end-of-life (EOL) phase of these vehicles has become a critical concern, especially regarding the retired EV batteries (REVB), which contain rare earth materials, as well as hazardous materials. Estimating the waste streams and flows of this REVB is an important step in designing an effective and efficient waste management system. Several approaches and methods are available in estimating waste generation, such as disposal-related analysis, time series analysis, input-output analysis, and the population balance model. This study aims to review previous studies on the approach and method of estimating waste generation of retired EV batteries and then propose a conceptual model for estimating waste for Indonesia.

Machine learning (ML)-based surrogate models offer a promising alternative for Multibody Dynamics (MBD) Simulation of railway vehicle-track dynamics systems. A well-built ML model can accurately and quickly predict the dynamic responses to various track irregularities, significantly reducing computation time. However, training effective surrogate models is a complex process, influenced by the specific needs of the analysis. Different algorithms and training sets might be required for different surrogate models, making it essential to research the impact factors for building these models. This paper presents a comparative analysis of ML-based surrogate modeling approaches tailored for MBD Model within a railway digital twin platform. The primary focus is evaluating the performance of various ML-based surrogate modeling approaches, and also the influence of neural networks, training parameters, and data sources. By leveraging extensive simulation and measurement data, we assess the ability of these surrogate models to predict key performance indicators under varying operational conditions. This analysis provides valuable insights for railway engineers in selecting appropriate surrogate modeling approaches, ultimately contributing to the advancement of predictive maintenance and optimization in railway operations.

This study explores resilience strategies for Indonesian startups through system dynamics modeling. Startups in developing nations face considerable risks, which results in a need to be resilient in mitigating disruptions. We define resilience as returning an organization to pre-disruption performance levels and developing a model focusing on innovation, marketing, valuation, and cost optimization. Using system dynamics, this work assesses these strategies' efficacy in managing risks and ensuring sustainability by simulating these strategies under different scenarios. Our recommendations might support business owners, governments, and academics in promoting Indonesia's more resilient startup ecosystem, which is crucial for long-term growth and stability despite uncertainties.

This research aims to analyze how the government, university and business aspects of the Triple Helix framework influence innovation, and how these innovations affect the capabilities and performance of craft MSMEs. The problem identified was the challenge of sustainability and inclusivity of craft MSMEs, especially for businesses with disabilities that require better access to education and training. The research method used a descriptive quantitative approach with purposive sampling techniques, involving 201 respondents from craft MSMEs that employ workers with disabilities. Model testing was conducted using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The results show that Triple Helix collaboration significantly enhances innovation, which in turn improves the capabilities and performance of craft MSMEs. This research provides deep insights into the importance of synergy between government, universities and industry in supporting innovation and sustainable development of craft MSMEs.

Multiple automated guided vehicles are used for automatic transportation. It is necessary to generate a conflict-free routing and task assignment that can avoid collisions and interference among AGVs without congestion. This paper proposes a simultaneous optimization method to determine the task assignment and the conflict-free route that can minimize the total transportation time. We propose the following two task allocation methods: one is an empirical method based on the Manhattan distance between AGV and the starting and ending nodes of tasks. The second one is a simultaneous optimization method for task allocation and path planning using a genetic algorithm. Computational experiments are conducted to show the effectiveness of the proposed method in terms of the reduction of the total traveling distance and the reduction of computation time with less computational effort.

Reverse supply chain in the recyclable waste management sector is gaining its momentum through the optimization of routing and emissions. However, the application of reverse supply chain in achieving green objectives is through green vehicle routing problems (GVRP) mainly considering green objectives or constraints. This shows the lack of integrating features in GVRP models in terms of its supply chain network and optimizing decisions in demand and functionality of a location node while considering their relationship. The study developed a multi-objective GVRP model for reverse supply chain in the recyclable waste setting considering location-dependent demand and node functionality decisions with green and economic objectives, where it aims to analyze the routing selection behavior once these features are integrated in the assignment of functionality of nodes. Through validation in DICOPT solver in GAMS and insertion heuristic, it is found that the model prefers to route least distances and to open dual functionality nodes for less emissions and routing costs. Further research is to consider multiple periods and level of impact to demand based on different factors.

Decarbonization of the transport and logistics industries is of essential importance to address the challenges of climate change. Recently, Electric trucks have drawn global attention due to their potential to minimize greenhouse gas emissions in freight transportation. However, inadequate charging infrastructure is among one of the most significant challenges that hinder the widespread adoption of electric trucks. Research has been done to plan a nationwide charging network in the EU and the US. Taking a different perspective, we propose a novel optimization model for joint logistics-and-charging-network design under an industry-led infrastructure development strategy. This approach ensures adequate accessibility to charging stations while minimizing the total costs. Moreover, we performed an analysis of both the economic and environmental performances of electric truck adoption through a case study of Nepal. The computational results reveal potential savings of up to 33.3% in costs and 55.9% in CO₂ emissions related to transportation, highlighting both the feasibility and benefits of the industry-led infrastructure development strategy in facilitating truck electrification in green logistics and transportation.

Acute ischemic stroke (AIS) requires rapid treatment to minimize neurological damage. Mobile Stroke Units (MSUs) provide timely interventions, and this study optimizes the rendezvous strategy (MSU-RS), where ambulances meet MSUs at predetermined points (rendezvous points). This research proposes a novel method to select optimal rendezvous points, reducing travel times, and enhancing MSU efficiency. Using a modified p-median problem and Geographic Information System (GIS) data, the research calculates precise travel times with the Open-Source Routing Machine (OSRM). A Genetic Algorithm (GA) is employed to solve this NP-hard problem. The research investigates the case study in central Bangkok incorporates telecommunication parameters critical for telemedicine. The result shows significant travel time reductions with the proposed GA configurations, especially the Mutation Dominant (MD) configuration. This research highlights the potential of GA in improving MSU rendezvous strategies, enhancing patient outcomes, and operational efficiency.

The aim of the article is to present the adopted concept for the construction of an unmanned aircraft delivering a defibrillator to save people with sudden cardiac arrest. The concept is based on an unmanned aircraft in a VTOL flying wing system, achieving a cruising speed of at least 160 km/h. Previous scientific work and commercial solutions in this application are mainly based on classic multi-rotor or VTOL designs but moving at lower speeds. The aim of the work is to build a new structure adapted to the mission of providing a defibrillator for people suffering from cardiac arrest.

In hospital settings, Emergency Departments (EDs) often face overcrowding and inefficiencies due to unpredictable patient demand. This study addresses these challenges by integrating design thinking principles with experimental design methodologies. We introduce a novel approach combining design thinking, regression modeling, and discrete event simulation (DES) to enhance resource allocation and reduce patient Length of Stay (LOS) in EDs. Design thinking involves phases such as understanding, abstraction, ideation, testing, and implementation, with each phase incorporating DES or regression modeling to evaluate system performance.
Multiple regression models examine relationships between nurses, beds, physicians, and patient stays, offering insights into key performance indicators and resource allocation. The research aims to develop an optimal resource allocation strategy by exploring interactions between resource management and patient waiting times. Findings highlight critical resource factors influencing patient flow. A case study demonstrates significant reductions in patient LOS, promising an improved patient experience. Our framework equips healthcare institutions with knowledge for informed decision-making and strategic changes, aligning with the study's overall objectives.

High-pressure and high-load work environment can significantly increase the risk of nurse fatigue, which in turn has potential impacts on patient safety, quality of care services, and the occupational health of nurses. To address this challenge, this study focuses on the nurse scheduling problem considering both fairness preferences and fatigue risks under the hierarchical management system. By combining a fatigue quantification model based on circadian rhythms and risk assessment of service process failures caused by violations of soft constraints, this paper explores a new scheduling strategy. This strategy first ensures fairness in scheduling under a hierarchical management system, and then employs a metaheuristic algorithm to search for high-quality scheduling solutions. Case studies show that this strategy not only contributes to achieving scheduling fairness among nurses at different levels but also effectively reduces nursing fatigue and its associated risks. It provides a strong reference for hospitals in preventing and managing nursing fatigue risks. This research has important practical significance for improving nursing service quality, ensuring patient safety, and safeguarding the occupational health of nurses.

This study introduces a novel collaborative operating room scheduling problem across multiple hospitals with consideration of patient preferences. To tackle this challenge, we developed an effective selection hyper-heuristic algorithm that creates a diverse set of low-level heuristics by integrating various tabu parameters with move operators and designing multiple perturbation heuristics. The proposed algorithm is mainly operated through a series of search phases. During each phase, low-level heuristics are evaluated to form an elite subset based on their objective improvement and solution times. From this elite subset, heuristics with fewer executions but a higher frequency of finding optimal solutions are then employed for the search. Experimental results across 160 instances demonstrate that our algorithm significantly outperforms the general solver Gurobi in terms of solution quality and exhibits robust performance across instances with different surgical duration distributions.

This study examines the effects of research and development (R&D) on the market value of Japanese pharmaceutical companies. Based on 1,345 news articles, our event study analysis of 74 Japanese pharmaceutical companies from 1 January 2010 to 31 December 2021 revealed three key findings. First, the stock prices of the aforementioned companies generally respond positively to the business news, and more specifically, R&D-related news. Second, the impact on the share price is more pronounced as the R&D progress to more advanced stages. Lastly, the stock price responses to R&D news tend to diminish as the size of pharmaceutical companies increases.

In the healthcare, Blood Bank operates with the highest standards of quality, efficiency, and compassion, the Blood Bank not only serves as a vital resource during emergencies but also plays a crucial role in supporting various medical procedures, surgeries, and treatments. Facility planning in blood banks is a critical aspect of ensuring efficient and effective operation while maintaining the integrity and safety of the blood supply. This project aimed to investigate the facility layout of the Al BASHIR HOSPITAL’s, Jordan, Amman, specifically focusing on its blood bank directorate. After identifying the issues significantly impacting the efficiency of services and patient satisfaction, we utilized industrial engineering tools to modify and enhance the facility’s design, making it more compatible with the production process requirements. Upon careful observation, significant crowding throughout the department, especially at the entrances and corridors. Large amount of data was collected to explain the reasons for this crowding. This problem has been classified multiple categories including, Crowding, waste, and layout design.  Hence mix methods approach was utilized to analyze the facility layout and address the problem.

Breastmilk cooler bags are essential for mothers who need to store milk for later use. These bags are designed to keep milk sterile and safe, ensuring it remains uncontaminated and retains its nutritional value. They are flexible and can be neatly stacked in the freezer, optimizing storage space and enabling long-term milk preservation. However, despite their benefits and convenience, several issues arise, including concerns about their properties and cost. This study aimed to identify factors influencing purchasing decisions regarding breastmilk cooler bags. Kano's model was applied to product development among mothers to achieve this.  A total of 33 nursing mothers participated in the study. The model was employed to design a questionnaire exploring consumer satisfaction with various product features. Features that yielded high satisfaction were incorporated into the product design. The analysis using the Kano model indicated that breastmilk cooler bag products effectively met consumer needs for portability, temperature maintenance, packing size, and temperature status indication. This research provides insights into consumer preferences, guiding the development of more effective breastmilk cooler bag products.

With the rising awareness of climate change and GHG emissions, there has been a push towards alternative fuels in the maritime industry. In this paper, we use linear and adaptive lasso regression on a novel dataset of transactions in the second-hand market for gas carriers to investigate the effect of fuel choice on vessel value. On the one hand, alternative fuels are more expensive and thus should be reduced in value. On the other, there is evidence of a willingness to pay to reduce GHG emissions in the supply chain and there could be some regulatory risk associated with not reducing emissions. We find evidence that choosing alternative fuels makes a vessel more valuable.

This paper proposes a unmanned aerial vehicle (UAV) flight attitude prediction method utilizing a convolutional neural network (CNN) and long short-term memory (LSTM) network and Attention mechanism. It has become particularly important to predict flight attitude accurately with the wide application of UAVs in many fields such as, aerial photography, logistics, and surveillance. The proposed method uses CNN to extract spatial patterns of UAV flight data, and LSTM to learn the temporal dependencies of these features to capture dynamic changes in flight attitude. The model introduces attention mechanism, empowering it to prioritize the parts of the data that are more critical to the prediction results. Experimental results with a certain type of multi-rotor UAV flight parameters show that the proposed model predicts the pitch angle with high prediction accuracy and keeps the error low. Through comparative experiments, the CNN-LSTM-Attention Model, compared to simple CNN and simple LSTM models, has improved accuracy, slightly decreased error, stronger generalization ability, and can effectively predict the UAV flight attitude.

In recent years, Japan has been facing difficulties in balancing electricity supply and demand. In response to the power crunch, research is being conducted on efforts to reduce electricity consumption during peak electricity demand times. An electricity company conducted a field experiment to reduce electricity demand in households. During peak electricity demand times, they distributed coupons to encourage people to go out. In the experiment to promote going out, the attempts to save electricity did not have enough effect because the coupons were distributed randomly. Therefore, this study proposes a method to perform occupancy detection in households using SVM and then estimate how much electricity can be reduced in each household (saving electricity potential). In this experiment, we estimated the saving electricity potential when using occupancy detection and when distributing coupons randomly and compared the results. The saving electricity potential was found to be higher when using occupancy detection compared to random distribution. By estimating the saving electricity potential, we confirmed the possibility of assessing the saving electricity effects of demand reduction strategies, such as distributing coupons.

In recent years, the automation of dairy management using artificial intelligence has been sought after, with mastitis detection being one such application. Mastitis is an inflammatory response that occurs in dairy cows, leading to economic losses such as decreased milk production. Therefore, early detection is desirable. Currently, raw milk analysis devices using lactate dehydrogenase (LDH), a biomarker, are widely used for early mastitis detection. However, the use of sensor systems often results in false positives. It is common practice to refer to detection results from the past few days for the final infection judgment, which relies on the farmer’s experience, leaving room for improvement. This study aims to combine raw milk analysis devices with machine learning techniques to detect mastitis more accurately without relying on the farmer’s experience. We constructed three machine learning detection models, achieving a maximum recall of 0.89, precision of 0.81. Furthermore, the infection prediction approach proposed in this study is widely applicable and can achieve more advanced predictions when combined with related research.

We propose a transfer learning method to estimate the motion time of a 6-axis robot arm obtained by 3D simulation using a machine learning method. The model is constructed from two perspectives: one without obstacles and one with obstacles. The motion planning method is Rapidly exploring Random Trees Star (RRT*), where the initial and target postures of the robot are set randomly within its range of motion, and the resulting motion times are used for learning. The  model in an environment without obstacles can be constructed with high speed and high accuracy. We develop a transfer learning method for a heterogeneous 6-axis robot arm to verify the speed-up of model construction. For the machine learning model with obstacles, the problem set-up was extended to adapt to obstacles based on the former model. Computational results show that both the accuracy and reliability of the model were improved by the proposed model.

Everyone can see that many innovations have spread to the rest of the world via the United States of America (USA). Meanwhile, declining Japan has refused immigration under the decreasing population, making it a social testing ground suitable for exploring the causes of economic decline. Although both countries have geopolitical advantages with few land borders that are prone to dispute, what factors cause this difference between growing and declining? In this research, the gross domestic product (GDP) factors for economic growth were identified using World Bank Open Data by means of quantitative analysis. The merit of this quantitative approach is that it gives clues quickly. As a key result, the growing GDP of the USA is associated with research and development, as proved in the Solow model. In contrast, the problem of the last mile of food in Japan was revealed from undernourishment as an unnoticed issue. The methodology and results will be useful to provide evidence for countries' economic development policies.

Measures that restaurants must adopt to comply with food safety regulations may increase costs, tempting them to violate the regulations. Inspections conducted by regulatory agencies are crucial in monitoring restaurants’ compliance and urging them to take appropriate countermeasures when necessary. In practice, multiple violations may be found during an inspection. Different types of violations may pose different levels of food safety risk to the public. The overall safety risk depends on the co-occurrences of multiple violations. Yet, regulatory agencies are facing challenges in identifying the associations between violations and food safety risks, and areas that inspectors should pay closer attention to during inspections. This study utilizes association rule mining approaches to identify the relationships between violations and food safety risks. Analyzing the restaurant inspection records from the New York City Department of Health and Mental Hygiene shows that maintaining proper food storage temperatures, cleanliness and sanitation practices, and ensuring the vermin-proofing of facilities are critical factors in upholding high food safety standards.

In the era of Industry 5.0, marked by interconnectedness and smart technologies, the dependable functioning of machinery is of utmost importance. Predictive maintenance and data analytics are pivotal in proactively identifying and resolving machinery failures. Despite previous research emphasising their significance, there remains a gap in synthesising current insights. This review aims to fill that gap by examining the current state of predictive maintenance and data analytics. It seeks to uncover their functionalities, tools, and applications in anticipating and mitigating machinery failures. With a systematic approach, including a comprehensive literature search and analysis, this review aims to provide a better understanding of the subject. The outcomes include critical insights into these methodologies, guiding businesses, and researchers. Moreover, the review identifies gaps in existing literature and proposes future research. The study concludes that predictive maintenance and data analytics could enhance machinery reliability.

Electric vehicle technology has experienced rapid, but there are still obstacles on the information technology side, one of which is the separation of information systems that cannot be exchanged easily. This problem arises because each electric vehicle brand makes its own product ecosystem. In contrast, a system that can work efficiently is using data between companies that can be operated together known as interoperability. The most efficient level of information interoperability can be formed from the development of data ontologies. The purpose of this article is to map research development of ontologies on electric vehicle products, especially batteries, by using systematic analysis to examine article documents in Scopus with the context of “ontology” and “battery” from 2014 to 2024. The content analysis of the articles was carried out based on the interoperability layer model classified into domains, zones, and information purposes. It was found that different applications in different articles hindered the ease of data utility, so a proper framework was needed to address the challenges in a highly complex technological landscape.

Effective progress monitoring on construction sites is essential for project success in that it helps mitigate risks associated with construction delays. Unmanned Aerial Vehicles (UAVs) are widely used to collect spatial data from construction sites on a large scale without requiring humans to visit areas. 3D point clouds are then generated using photogrammetry or laser scanning techniques for site modeling while performing field inspection and earth-volume estimation tasks. However, existing 3D reconstruction technologies have disadvantages in depicting amorphous or reflective objects. This paper introduces a recent approach utilizing Neural Radiance Fields (NeRF) for 3D reconstruction of construction sites from UAV-captured data. The reconstructed models were evaluated against ground truth data using nearest neighbor distance, and a quantitative evaluation was performed using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score. The findings demonstrated that NeRF can effectively synthesize photorealistic views of complex scenes, improving the fidelity and utility of 3D models for construction site monitoring. The practical applications can be extended to semantic segmentation and volume estimation, further contributing to its value in automatically quantifying construction progress.

Traffic signals can greatly affect the traffic on the road. Poorly controlled traffic signals may cause heavy traffic congestions, which is the waste of energy and costs. Many studies have conducted to find the optimal traffic control to minimize the traffic on the road. Most studies have considered the flows of vehicles to find the optimal traffic control. However, the traffic signal also affects the pedestrian signals. Thus, this paper considers the flows of pedestrians to find the optimal traffic signals. This research also explores the dynamic situation of the problem. Reinforcement learning is used to find the optimal solution. Simple numerical results provides the optimal strategy of the traffic signals.

Three-dimensional (3D) reconstruction is a pivotal technology for generating digital representations of physical scenes. It plays an essential role in the timely and precise evaluation of site conditions and consequently facilitates construction management. Implicit representation-based methods such as neural radiance fields (NeRF), which have been recently proposed, demonstrate unprecedented accuracy and scene fidelity in 3D reconstruction. However, despite their potential, these implicit methods encounter significant challenges in rapidly updating representation for large-scale and complex scenes like construction sites due to their tremendous computational demands. This latency in reflecting the current state of scenes can hinder their applicability to tasks that require precise and real-time monitoring of site conditions. To overcome these challenges, this paper introduces a novel framework that employs a change detection technique, enabling the rapid identification

Data visualization is essential for turning data into meaningful insights. This is highlighted in the context of experimental data for scientific research purpose due to the complexity and scale of the data involved. In this paper, we investigated the relationship between the visualization effect of experimental data and chart features. We analyzed the effects of chart axes, lines, text display ratio and content display ratio on the visualization. The chart parameter model and chart content model were proposed to capture the effect of different features on the degree of visualization and to explore the cross-influence of different models on the degree of visualization. This study also introduced machine learning models to help build a set of evaluation models for a more readable visualization for experimental data. This work may help authors to set the chart parameters into the paper more easily, as well as improve the efficiency of the readers' reading comprehension of scientific figures.

This qualitative research study examines the impact of digital transformation on organizational strategy performance within an organisation. Through in-depth interviews and thematic analysis, the study aimed to identify the current effects of digital initiatives, determine the role of these initiatives in strategy performance, and offer recommendations for enhancement through a case study analysis. The findings reveal that digital transformation significantly influences employee productivity and efficiency, with a notable shift towards a customer-centric approach. While these changes are promising, challenges such as aligning digital strategies with overall business goals and digitizing customer interactions persist. The research highlights that strategic performance thrives when there is a strong alignment between digital transformation efforts and the company's strategic objectives. Leadership and governance emerge as pivotal elements in driving the digital agenda forward, coupled with the need for continuous learning and adaptability among employees. The study concludes by suggesting avenues for further research, including long-term sustainability assessments of digital transformation.

This study evaluated the readiness of a small to medium-sized enterprise (SME) industrial factory for Industry 4.0 and the feasibility of investing in automation technology. A comprehensive matrix assessed technological and organizational readiness, providing a clear overview of the factory's current capabilities and potential improvements. Financial analysis showed the investment is viable, with a positive net present value (NPV) and an internal rate of return (IRR) exceeding the discount rate. Payback periods fell within the system's lifespan, ensuring a prompt return on investment. Benefits were calculated to be 5.41 times the cost, highlighting significant economic value. The results suggested the SME factory was well-positioned to enhance its Industry 4.0 readiness through smart investments in automation. This readiness boosts competitive advantage and ensures long-term viability in an increasingly digitalized industrial environment. The study confirmed that investing in these technologies is a prudent decision, promising substantial improvements in productivity and operational efficiency.

Quality Management (QM) plays an important role in manufacturing enterprises and thus has been a topic of research for decades. Recently, interest in local production networks of heterarchical, i.e. non-hierarchical, micro and small enterprises (MSE) from the manufacturing sector has increased due to reliability and sustainability concerns in global value chains. This paper thus presents a literature review on QM in that specific type of production. For the literature search, three scientific search engines were prompted with the same search string. The resulting publications were scanned for those providing insights into QM for production networks of MSEs. We show that there is very little literature on QM in this type of network and especially very few recent publications. The identified, relevant literature is analyzed and presented. We further identify a research gap regarding modern QM concepts that are adapted and developed for production networks of MSEs.

This study aims to evaluate the critical failure factors (CFFs) of process improvement (PI) projects, providing an initial understanding of the most impactful factors during PI implementation. Conducting an extensive literature review encompassing five prominent PI methodologies, Kaizen, Lean, Six Sigma, Lean Six Sigma, and Agile, revealed 39 CFFs contributing to PI failures. To manage this extensive set of factors, we utilize the Pareto analysis followed by the Analytical Hierarchy Process (AHP) model to assign weight to each factor and prioritize them accordingly. Five PI experts were involved in the pairwise comparison of the CFFs to examine the most influential factors. The top-ranked CFFs highlighted for PI initiatives were “Lack of top management support”, “Lack of clear vision/strategy for PI and future plans”, “Resistance to change”, “Lack of motivation, encouragement and reward” and “Lack of alignment between strategic objectives and PI project scope”. The outcomes of this work serve as a valuable tool for policymakers and PI professionals, offering a means to diagnose, evaluate and address highly ranked CFFs, ultimately minimizing the risk of PI project failure.

This paper introduces a new multi-aspect Phase-II distribution-free cumulative sum (CUSUM) procedure based on a combination of three orthogonal rank statistics for simultaneously monitoring random processes' location, scale, and skewness aspects. The idea of multi-aspect process monitoring involving three orthogonal aspects using a single combined statistic is new, and previous works on Phase-II monitoring used three non-orthogonal statistics. Although they are very robust, orthogonal statistics have certain advantages in performance and interpretation. A quadratic combination of three component statistics based on the Legendre polynomial is proposed for monitoring the shift in location, scale, and skewness. Implementation design and post-signal follow-up procedure for identifying which parameter is more responsible for the signal are discussed. The In-control robustness of the proposed scheme is studied via simulation. The run-length properties of the proposed scheme display encouraging out-of-control properties and help identify a broad class of shifts involving one or more of the three parameters in an underlying process distribution. We illustrate the implementation of various schemes in process monitoring applications.

Capability analysis of manufacturing processes needs to appropriately model quality characteristics (QCs). The sample data for modeling can be divided into three categories: (a) original measurement data of QCs, (b) transformed or normalized measurement data, and (c) data of a composite QC obtained through aggregating several QCs. Generally, different categories of data require different types of distribution models. The purpose of this paper is to identify potential candidate distributions for the first and second categories of data. This is done through fitting seven real-world datasets to eight candidate distributions, which can be divided into two types: (a) common distributions like the Weibull distribution, and (b) the folded-normal distribution (FND) and its variants such as generalized and power half-normal distributions. Main conclusions are that the normal assumption does not hold for the data considered in this paper, the measurement data can be appropriately modeled by some of the common distributions, and the transformed data can be appropriately modeled by the FND or/and its variants. These are useful for QC modeling in process capability analysis.

This article aims to address this research gap by adopting an "individual approach." It focuses on examining the various components of Industry 4.0 and their potential interaction with the "Define" stage of the Six Sigma practice in this initial research paper. Specifically, this study explores how big data analysis (BDA) and Artificial Intelligence (AI), which are key components of Industry 4.0, can enhance the collection efficiency of the "Voice of Customer." By gaining a better understanding of the "Voice of Customer" and improving the identification of relevant and significant improvement projects during the "Define Stage" of Six Sigma, enterprises can adopt Industry 4.0 components and reap the associated benefits of this individual integration.  Furthermore, this paper also establishes future research directions, guiding subsequent research papers to investigate the interaction between different Industry 4.0 components and the "Measure," "Analyze," "Improve," and "Control" stages of Six Sigma

The defect rate tolerances determine the accuracy of the process hence the lower the defect rate the higher the accuracy of the process. The defects can be caused by a variety of reasons therefore the process should have standards and protocols for process control. Zero defects can result from good adherence of quality management standards, however the accuracy of problem solving could permanently eliminate and prevent the problem from happening again. An effective Corrective Preventive Action system results to zero future defects if it is designed to best suit the nature of the system which can be implemented by integrating Quality Management Systems (QMS). This research seeks to upgrade existing Corrective Preventive Action models using lean techniques to evaluate kaizen projects for businesses to remain competitive in the market by sustaining creativity and uniqueness of the business. A review of existing methods has been performed. This research will add more knowledge to Quality Management Systems by satisfying the requirements of ISO9001 standards. The comparison of existing methods to the proposed technique was presented for a clear picture of the upgrade.

In this investigation, linear models used for quality prediction of a final product are compared and evaluated using data from a real manufacturing process in forming technology (i.e., flexible rolling process). Two alternative methods for simplifying the feature selection method for the quality prediction model of manufactured blanks are presented. This work proposes implementing L1 and L2 regularization techniques in the original regression model. The method is then evaluated based on model complexity and performance metrics using the final predictions. By comparing these indicators, the effectiveness and benefits of the proposed method are confirmed. A simplification in the model-building effort and feature selection process is developed while providing an efficient and comparable accuracy in the predicted quality of the manufactured blanks.

This study aimed to examine the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines, using the latent variables from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and two additional variables derived from relevant literature. A total of 360 medical students participated by completing an online survey comprising 40 questions. The analysis, conducted using stepwise multiple linear regression, revealed that Performance Expectancy (PE), Habit (HB), and Instructor Characteristics (IC) were significant predictors of Actual Use (AU) with an accuracy of 54.22%. Higher levels of PE, HB, and IC were found to positively influence the acceptance and utilization of medical eLearning platforms. The findings from this study can provide valuable insights for the Commission on Higher Education in the Philippines, aiding in the enhancement of eLearning platforms for medical education. Although this research primarily focused on UTAUT2 and two additional variables—Learning Value and Instructor Characteristics—the results offer substantial implications for the broader development of eLearning platforms in the medical field.

The “Engineering Ethics Enigma” (3E) is a digital role-playing game (DRPG) designed to enhance ethics education for pre-university engineering students in the Malaysian Matriculation Programme (MMP). Utilizing the RPG Maker MZ engine, 3E immerses students in interactive scenarios where they navigate ethical dilemmas based on real-world engineering cases. The development framework encompasses five stages: Planning, Pre-Production, Production, Testing, and Post-Production. The game is structured into five phases: Introduction, Learning and Assessment, Transition to the Past, Mission, and Conclusion. Each phase integrates ethical tenets with gameplay, fostering critical thinking and decision-making skills. By incorporating the MUSIC Model of Motivation, 3E aims to engage students deeply, promoting a comprehensive understanding of engineering ethics. This paper details the game’s design, development process, and potential impact on engineering education.

The Testing, Inspection, and Certification (TIC) industry is the key to product safety, quality, and performance of any traditional consumer products and technology devices, any products should be tested and inspected by quality assurance experts, and comply with international product regulations and standards; and inspected by professional quality control auditors. Currently, the TIC industry is facing several challenges of manpower shortage and high demands of productivity, researchers and industries are sourcing solutions for such challenges. Digital Twin (DT) is an emerging technology embedded with Virtual Augmented Reality (VAR) devices to guide the operator in reviewing and following the operation procedures via digital platform in VAR devices like HoloLens, DT has been adopted in the automotive industry for assembling and repairing motor engines and most academic researchers focused on the manufacturing production plants but seldom in TIC training. This paper will review the feasibility of DT training adoption in the TIC industry and demonstrate a practical DT training case to share the insights and research application roadmap to address the aforementioned challenges in the TIC industry.

In the Polish railway sector is a visible increasing dynamics of employee turnover. It is a challenge in the current management of railroad personnel. Due to that, new tools are introduced in the teaching process of the employees, also in the railway sector. Such tools are mainly virtual ones, and specifically the immersive virtual reality is interesting. A disadvantage of these tools is the possibility of simulator sickness (SS) occurrence. The paper shows a briefly introduction into this problem combined with a literature review and ended with questions relevant for the research. Then, the methodology of performed research is presented describing the developed virtual reality tool, the actions to be done in the tool, and questions asked to the participants about symptoms of the SS. Noteworthy is that the analysed tool is a slow moving one. The next paragraphs show results of the experiment as well as the conclusions. One of the interesting results was to find the part of people who can or cannot be trained by the VR tool. Interesting is, that for the used slow-motion tool the part of participants unable to finish the training is only about 4%.

This study highlights the importance of Process Operation Diagrams (POD) in facilitating collaboration between human operators and collaborative robots in Tecnológico de Monterrey's "Smart Factory" for mechatronic engineering students. This collaboration optimizes operational efficiency and enhances workplace safety, with an average manufacturing cycle time of 10 minutes for a FrED device. The process showcases automation and interactions between robots and operators, fostering the development of technical skills and theoretical concepts for 96.5% of the students.

In the dynamic realm of building automation, the reliability and performance of KNX devices are crucial for the uninterrupted functionality of intelligent buildings. This paper presents an essential solution—a dedicated testing kit tailored for the KNX industry. Committed to elevating the quality and efficacy of KNX devices, our project develops a comprehensive testing suite designed to enable manufacturers, system integrators, and industry experts to thoroughly evaluate the reliability, latency, response times, network congestion management, and interoperability of KNX devices. Through an exhaustive analysis of current products’ strengths and limitations, and by addressing the industry’s unique demands, our project resolves prevalent technical challenges within the KNX ecosystem. The testing suite, a synergy of hardware and software advancements, offers an intuitive interface for test configuration and execution. It incorporates standardized test scenarios, data analytics, and reporting capabilities to ensure KNX devices adhere to exacting performance criteria. Moreover, the project underscores scalability, affordability, and versatility, catering to the varied requirements of industry participants. The projected outcomes transcend the creation of a testing apparatus; they include fostering enhanced quality benchmarks, streamlining the manufacturing workflow, diminishing interoperability issues, and ultimately, boosting the overall robustness and efficiency of KNX devices. As the global trend moves towards intelligent, eco-friendly edifices, the “Design and Development of Testing Kit for KNX Devices Reliability and Performance Measurement” initiative aspires to drive innovation and set new standards of excellence in building automation technology.

1) Multiple P/D Points: To avoid congestion for pickers, many warehouses use multiple P/D points. Different warehouses have varying flow policies and infrastructure for using these points. 2)Minimizing Travel Distance: If source and destination P/D points are chosen randomly, minimizing one-way travel is equivalent to minimizing two-way travel. Thus, the objective function is to minimize the one-way pick travel distance from the P/D point to the pick location. 3)Two-Way Travel and Return Routes: Extending the models, we consider minimizing two-way pick travel distance. Unlike one-way travel, the optimal destination P/D point for the return route is chosen deliberately. In many real warehouses, flow rates at P/D points depend on pick positions. A good warehouse management system consolidates orders across multiple P/D points, enhancing the pickers' flexibility in using different P/D points. 4)Research Scope: The study considers warehouses with varying P/D points, from a single-central one to multiple symmetrically placed points. Remember, warehouse efficiency depends not only on mathematical models but also on practical considerations and system flexibility.

The rapid growth of the electric vehicle (EV) industry necessitates robust tracking and management systems for battery lifecycle, prompting significant attention towards GS1 standards. GS1's global standards, widely used for product identification and traceability, offer a promising solution for creating a digital passport for EV batteries. The implementation of GS1 for the digital passport requires a collaborative approach involving industry-wide adoption of standards, technological integration, and data management protocols. In this talk, we present a framework based on a digital product passport approach that can encompass comprehensive data on battery production, usage, and recycling, to enhance transparency and sustainability within the supply chain. Within our proposed EV battery digital passport framework, GS1 can play a crucial role by providing unique identifiers for each battery, enabling seamless data exchange across various stakeholders, including manufacturers, suppliers, and recyclers. By centralizing the database to store and share battery-related information, our integration of GS1 standards ensures that batteries can be tracked through their entire lifecycle, facilitating regulatory compliance, efficient recycling, and second-life applications.

Consider base-stock control of an item with compound Poisson demand, negligible set-up costs, and continuous review. Assume that customer demand, which cannot be satisfied immediately, is lost and causes a penalty cost proportional to the loss, and that lead times do not cross in time. Our Markov model assumes that lead times are Erlang distributed, implying that the long-run average number of units in resupply, the average cost per unit time, and the optimal base stock can be expressed in closed forms when customer demand sizes are geometric. For non-geometric demand sizes, an approximation to the long-run average number of units in resupply provides good specifications of the base stock. For non-crossing lead times specified only by mean and standard deviation (SD), we represent the average cost per unit time as a convex combination of the average costs for two models with Erlang lead times. Our numerical study shows that the average cost is very sensitive to SD, which is in sharp contrast to the complete insensitivity in case of geometric demand sizes and independent lead times.

In this paper, we propose an approximation algorithm for a single source multiple depot pipeline scheduling problem considering demand, tankage limitations, sequencing constraints. The objective is to find the pumping and delivery sequences and associated pumping and dropping batch sizes at the source and demand locations to minimize the sum of cycle stock, interface(set up) and backorder costs. The proposed algorithm consists of bundle of pumping and dropping heuristics which interact using adaptive memory principles to obtain better solutions. Field visits are conducted in an Indian Petroleum firm to collect the relevant real test data such as inventory tankage and pumping and dropping capacities, initial line fill etc. The experiments based on the real data suggest that the algorithm can find high quality solutions quickly compared to the exact approach proposed in the prior literature. Furthermore, the magnitude of the benefit with the use of the algorithm increases as the number of products, the number of the time periods in the planning horizon increases. Thus, the approach demonstrates its capability to solve the real life instances.

The maritime industry faces heightened security challenges in the Red Sea, exacerbated by geopolitical tensions and recent missile and drone attacks. These disruptions impact global trade routes, prompting nations to seek crisis management solutions. This study explores vessel platooning (vessel train) as a strategic approach to enhance maritime security. Within the context of these threats, we develop a mixed-integer linear programming optimization model to analyze the relationship between platoon size, expenses, and safety. The study contrasts the benefits of enhanced safety and potential cost savings with the use of military escorts. By prioritizing maritime safety and operational costs, the model aims to provide shipping companies with a framework to navigate the trade-offs between cost, time, and safety. It evaluates factors such as optimal platoon size, safety costs, and penalties for maximum waiting time, emphasizing proactive decision-making. This exploratory study lays the groundwork for a decision-support system, helping ship operators navigate high-risk situations while balancing safety and financial responsibilities amidst geopolitical challenges.

This study explores the integration of crowdsourcing into last mile logistics. A crowdsourced fleet is characterized by its delivery pricing, and a mathematical model is proposed to identify optimal terminal locations for both dedicated delivery and crowdsourced fleets. The objective is to minimize total last mile delivery costs, accounting for variations in demand and pricing due to the floating population in a given city. Using annual floating population data from Ulsan Metropolitan City in the Republic of Korea, we conduct numerical experiments to determine the optimal terminal sets for an integrated last mile delivery platform. Results indicate significant cost reductions by utilizing crowd workers in highly populated areas rather than solely near final delivery destinations. A sensitivity analysis examines the impact of three key cost parameters on delivery efficiency. Additionally, we analyze the cost and carbon emissions implications of crowdsourcing integration and strategize incentive pay distribution to promote the adoption of electric vehicles for last mile deliveries. The study also evaluates the effects of varying traffic conditions on the optimal solutions.

Artificial Intelligence (AI) is becoming increasingly prevalent in industry, but one major drawback is the "black-box" nature of AI models, which obscures the reasoning behind their decisions. Causal AI offers a promising solution by identifying and quantifying cause-and-effect relationships within a system. This approach involves two main steps: causal discovery, which uncovers potential causal links in a dataset, and causal inference, which quantifies the strength of these relationships. In this research, we explored the application of causal AI in the automotive industry to enhance sustainability. This topic is important given the significant environmental impact of vehicle emissions. We applied various causal AI algorithms to compare them and conducted a case study to demonstrate our methodology. Existing models describe the selected dataset well. By utilizing such a dataset, we validated our results against these established models. Our findings provide a foundation for others who want to make decisions based on cause-and-effect relationships. Future work can extend our case study with complex variables, such as human behavior, where no models exist. Additionally, we made the source code publicly available to facilitate research.

The mobility industry can transform significantly if Artificial Intelligence (AI) is included in Cooperative, Connected, and Automated Mobility (CCAM) systems. The key components and requirements for adopting AI in CCAM systems must be identified to get the maximum benefits. This research develops a conceptual model with a technical architecture containing essential technological components and a technology adoption framework comprising policy and business-related measures. The conceptual model is designed based on the key findings of a Systematic Literature Review (SLR) conducted in this research. After multiple screenings, 23 papers are analyzed based on the formulated research questions. This research gives researchers and practitioners a comprehensive overview of the critical technologies, their requirements, and the socioeconomic framework to adopt AI technologies in CCAM

Condition-based monitoring of gear health is crucial to prevent unexpected machine downtimes. However, the lack of small-scale and cost-effective sensors that can be easily integrated on a system level hinders the implementation of condition monitoring approaches. Damage to gears often occurs due to tooth contact, making it essential to acquire accurate data close to the gear engagement for reliable damage detection. Integrating sensors directly into gears is a promising solution for a space-neutral sensor system, but continuous data acquisition is limited by energy and memory constraints of Microcontrollers (MCUs). This paper proposes and evaluates an operating strategy for detecting gear damage and temperature increases, featuring automated gear state detection to reduce energy consumption. Testing on a Cortex-M0+ MCU demonstrates its suitability for low-power devices. The strategy was evaluated using downsampled pre-recorded acceleration data measured by external, high-performance sensors. An optimal MCU clock frequency is determined by evaluating its impact on energy consumption and execution time. The strategy's energy consumption and execution time for different states are presented, highlighting opportunities for future optimization.

In addition to reducing traffic congestion, urban public transit systems are essential for supporting economic growth. However, delays often compromise their reliability. This study investigates the utility of machine learning (ML) models, enhanced with Explainable AI (XAI) techniques—specifically SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME)—to predict and interpret delays in public transportation. We employed Random Forest and k-Nearest Neighbors (kNN) models to identify and explain key features affecting delay predictions, such as time of day, day of the week, and route characteristics. SHAP provided consistent and robust global insights into feature importance, while LIME offered clear and straightforward local explanations for individual predictions. The combined use of SHAP and LIME enhances model transparency and trustworthiness, making the predictions more actionable for stakeholders. Our findings demonstrate that integrating XAI methods enhances the interpretability and practical application of delay predictions in public transportation.

In recent years, Fully Autonomous Vehicles (FAVs) have attracted considerable attention in the transportation sector, promising enhanced safety and efficiency. However, pressing concerns regarding safety, cybersecurity, liability, infrastructure, and societal impact persist. While much research focuses on technical aspects, this study adopts a comprehensive approach through Fault Tree Analysis, examining both component and infrastructure failures to provide valuable insights for FAV development and deployment. Findings indicate a failure probability of 0.2610 for component failure, while infrastructure failure has a probability of 1.470 × 10−4. The analysis highlights the significance of focusing on FAV components like the LiDAR, camera and communication systems, with manufacturers and governments urged to establish robust regulations and quality standards to ensure FAV safety. Additionally, measures to address risks like reckless driving of conventional vehicle drivers, distractions, and extreme weather conditions are also crucial for promoting FAV safety.

This paper presents the development and validation of a smart failure management system designed for small and medium-sized enterprises (SMEs) in the manufacturing sector. The system integrates the Berlin Problem-Solving Circle, a comprehensive framework for systematically addressing production failures through a knowledge-based expert system. It includes a manual failure entry interface and graphical failure data representation, supported by artificial intelligence (AI) algorithms such as Bayesian Networks for probabilistic inference and Case-Based Reasoning for complex problem-solving. Additionally, an integrated e-learning platform disseminates theoretical and practical method knowledge to employees.The development process spans several critical phases, including knowledge acquisition, system implementation, and validation of user interaction on the shop floors of selected manufacturing companies. This structured approach ensures the system's effectiveness in addressing complex failures and facilitating continuous improvement. Preliminary results indicate significant improvements in failure prediction, diagnosis, and resolution. The findings underscore the system's wide applicability across various manufacturing environments, highlighting its potential to improve production quality, reduce costs, and foster continuous learning within SMEs. This paper demonstrates the system's capacity to promote greater efficiency and resilience in failure management.

The aim of the paper is to provide the opportunity to examine the system degradation process based on the information taken from oil data. In practice, a typical problem to encounter is usually the shortage of diagnostic data on the system under study. If it comes to a fleet of identical objects, however, it is possible to handle such situation. In our case we do experience the shortage of diagnostic data when studying the degradation of combustion engines in medium-weight off-road vehicles. Therefore, we apply the Bayesian approach to model and analyze diagnostic oil data. In spite of i) low mileage, and ii) the low number of diagnostic records, it is still possible to determine the presumed degradation development for a single vehicle based on the results. Owing to the Bayesian approach, such estimation and prediction could rely on the knowledge base which contains diagnostic oil information for all the observed fleet. The achieved results help at a relevant mathematical confidence level during i) the organization of operation and maintenance, and ii) the optimization and rationalization of life cycle cost.

This paper discusses the optimization of maintenance strategy inference by constructing a comprehensive maintenance knowledge graph and applying Relational Graph Convolutional Networks (RGCN). A vast dataset encompassing equipment, their attributes, various fault types, and corresponding maintenance approaches was collected to build the initial maintenance knowledge graph. To further unveil the hidden maitnance link, the RGCN model is introduced. This model significantly enhances the graph's capability by assigning specific matrices to the relationships between different types of equipment and fault categories. Such an approach enables more precise inference of the most suitable maintenance strategies for specific faults. The knowledge graph is implemented in the Neo4j graph database, which enhances the querying process and the intuitive understanding of fault diagnosis and maintenance strategies. Experimental evaluations demonstrate the effectiveness of RGCN in optimizing maintenance recommendations for equipment faults, thus promoting the advancement of intelligent maintenance practices in industrial settings.

Structural reliability analysis is essential for evaluating system failure probabilities under uncertainties, yet it often faces computational efficiency challenges. While surrogate model based techniques, including Kriging, are known for their high accuracy and efficiency, they typically employ a sequential learning strategy, which limits their potential for parallel computation. This paper introduces the Local Penalization Adaptive Learning (LP-AL) method, which facilitates parallel adaptive reliability analysis; LP-AL introduces a penalty function that emulates the process of sequential learning strategies, thereby achieving parallelization. The method also integrates a global error-based stopping criterion and a sample pool reduction strategy to enhance efficiency. We tested LP-AL with five commonly used learning functions across various engineering scenarios. The results demonstrate that LP-AL achieves high accuracy and significantly reduces computational costs, making it a viable approach for diverse structural reliability analysis tasks.

Mines use a wide range of heavy-duty rotating machinery which typically operates at steady state. Such equipment is designed to be robust and require minimal maintenance throughout its lifetime provided that lubrication is kept at nominal level and planned maintenance interventions are carried out as prescribed by the manufacturer. Nonetheless, certain equipment such as the agitators can be exposed to complex loading conditions which can result in gradual degradation of the rotating components. Acoustic Emission (AE) has been proven to be an effective tool for monitoring rotating and reciprocating equipment. AE measurements were carried out on various pieces of equipment at the Olympias plant in Halkidiki, Greece to confirm quantitatively their actual condition and identify any faults that may be present as part of the NETHELIX EU project. Herewith we discuss an accurate quantitative approach based on AE monitoring for predictive maintenance of rotating machinery used in mines. The aim is to enable an overall improvement on the Reliability, Availability, Maintainability and Safety (RAMS) of the rotating equipment of the plant with minimal additional intervention and cost.

A logistics company in South Africa is facing a challenge, where approximately 25% of its business assets have been out of service for more than 55 to 1000 days. To address this, a study was conducted to predict the number of assets that will be parked in the future and to identify the primary factors associated with the overall number of parked assets. The study used autoregressive models to forecast asset failures and collected data by developing a template that included the asset number, manufacturer, date of stoppage, reasons for stoppage, and reasons for not repairing or returning the asset to service. The results showed that the largest number of assets were out of service due to unscheduled maintenance, followed by those affected by vandalism and collisions. The results show that the company is more likely to experience an average of eight failures per month, with the upper limit falling between 34 and 68 failures. The study concluded that the use of autoregressive models can effectively forecast asset failures and facilitate proactive maintenance and management approaches.

Hydrocarbon refrigerant is a class of refrigerants with a low global warming potential and zero ozone depletion potential, because of that it is considered as an environmentally friendly group of refrigerants. Nevertheless, the use of hydrocarbon refrigerants is still rare due to its characteristic of being highly flammable. This research identifies and assess risk aspects related to fire risk from hydrocarbon refrigerant application in air conditioner and chillers. The assessment comes from practices that have been carried out in Indonesia with the goal of knowing which component of the risk aspect is the riskiest and how to mitigate it. The research was conducted using a semi-quantitative method using questionnaires filled by design engineer manufacturer, installer, operator and service personnel. The risk scoring was based on questionnaires result from 38 respondents for chiller questionnaire and 44 respondents for split type air conditioner. The average experience of the personnel is more than 7 years for both equipment. The study results in ranking and assessment for four categories of risk aspects namely: leakage points, ignition sources, causes of leakage, and non-compliances to standards.

Food security remains a critical global challenge for human health, economic stability, and social development. Addressing this issue necessitates a comprehensive understanding of the complex and interrelated factors influencing food systems. Systems Dynamics (SD) offers a robust framework for analyzing these complexities, allowing for modeling feedback loops, causal relationships, and time delays. This study employs a detailed bibliometric analysis to examine the scholarly output on food security using SD, spanning publications from 1982 to 2024. Utilizing the Scopus database, the analysis identifies vital trends, influential studies, and collaborative networks within this field. Findings highlight a significant increase in research interest, particularly in recent years, with notable contributions from environmental science, agricultural sciences, and mathematics. The insights gained from this analysis inform future research directions and support the development of more effective food security policies.