This paper presents an inbound production routing problem to manage just-in-time deliveries of different types of input components at a high-volume manufacturer served by a supplier cluster. Each supplier makes a single component. Multiple vehicle types are considered for pick-up and delivery from suppliers to manufacturers. A mixed-integer linear programming model is presented with sub-tour elimination constraints to solve the inbound PRP. As a solution approach a branch and cut algorithm with the subtour elimination constraints added as lazy cuts in the branching process are presented. Computational analysis with the model suggests improvements due to the proposed integrated delivery planning in comparison to the sequential planning process under practice.

The agricultural sector plays a crucial role to promote food security, especially in the dimensions of food availability and accessibility for developing economies. However, the agri-food supply chain entails various risks alongside other local and global risks in obtaining food security. This study aimed to examine risks that are faced in the agri-food supply chain to guarantee food supply since there limited studies that assess risks in the agri-food supply chain in relation to food security. Poultry supply chain in Indonesia is chosen as the case study due to its developing market economies and the commodity’s importance as a national protein source. A risk matrix diagram was used as the underlying approach to identify, assess, and measure food security-related risks in the agri-food supply chain. Findings of this study are expected to illustrate the risk related to food security in developing countries. Additionally, the research methodology is expected to contribute as a future guide to a systematic risk assessment in the context of the agri-food supply chain and food security.

Supply chain (SC) risks significantly impact manufacturers, affecting their operations, finances, and business activities. The lead time to arrive of raw material is a critical measure in the inbound SC.  This study aims to develop a risk matrix to assess inbound SC risks, thereby enabling predictions of potential for failures and taking proactive measures. Using a case study in an automotive manufacturer and data from a logistics expert interview, the Mean Time to Arrival (MTTA) is identified as a measure for the probability of failure (PoF). The consequences of potential failure (CoFs) consider ‘undesirable MTTA’ and are classified into quantitative (e.g., production stoppages and man-hour losses) and qualitative (e.g., client loss and goodwill) aspects.  The study developed a risk matrix to show the interrelationship among PoFs, CoFs, and the corresponding risk levels. It also identified recommended actions for various risk levels and presented an illustrative SC risk assessment. This paper provides practical guidelines for effective inbound SC risk management in manufacturing, aiding decision makers in informed decision-making and enhancing their operations.

With increasing consumer awareness of sustainability, remanufacturing has become a popular approach to achieving a circular economy. One of the challenges in remanufacturing is the uncertainty surrounding product returns. To address this issue, this study proposes a business process that uses IoT-enabled device operation data to predict remaining lifespan and offers replacement purchase financial incentives to adjust product returns. The proposal was evaluated through simulation, and we confirmed a reduction in uncertainty in product returns. We also identified the conditions under which this business process is most effective. The proposal is expected to help manufacturing companies establish efficient remanufacturing processes.

This research focuses on hospitals’ inventory management and ordering policy with consideration of effective substitution decisions for RBC units. Hospitals are mainly responsible for managing inventory and ordering decisions efficiently and cost-effectively. This is especially important for blood components such as RBCs which are essential in providing critical care to patients. However, blood components have limited shelf lives, for example, RBCs can only be stored for up to 42 days. Further, hospitals may substitute one type of blood product with another, either due to a patient's specific medical conditions, a supply shortage, or to prevent wastage. To tackle this problem, we used a stochastic optimization technique. Through computational experiments, we found out that our proposed method significantly improves the metrics of the blood supply such as the rate of emergency orders, and costs.

Since the turn of the millennium, sustainable supply chains have attracted significant attention because of increasing awareness and regulations. However, supply chain networks are exposed to disruptions associated with human or natural occurrences such as natural disasters and industrial strikes. Practitioners are concerned with building supply chain networks that are sustainable, yet resilient. This study aims to propose a new all-encompassing methodology for a sustainable and resilient (susilient) two-tier supply chain network design (S-2TSCND). The links and inclusiveness between resilience and sustainability in supply chains were explored to establish a novel taxonomy for susilient. A framework was built, which identified the susilient dimensions, enablers, and criteria. A fuzzy four-objective optimization model (FFOOM) was built to address the susilient facility location problem; establish best order quantities; and uncover the trade-off between resilience and sustainability. Finally, the trade-off was evaluated using the global criterion method to measure its distance from the ideal solution with the aim of choosing the optimal supply chain network.

The purpose of this study is to identify the importance and value of sustainable e-procurement adoption and practice in COVID-19. An in-depth semi-structured interview with seven practitioners was conducted to understand the impacts and changes of firm's procurement operations during the days of pandemic. It also stated that adoption and practices of e-procurement is vital to the companies to enhance their business competitiveness and sustainability.The study confirmed that e-procurement has its unique features and benefits which can fulfill the requirements of the four most common factors of operational efficiency, cost effectiveness, employee acknowledgements and market environment changes. More government guidance and support (particularly SMEs) are needed to motivate the e-procurement with achieving green value in the corporations.

Despite the positive impact of the halal industry on Indonesia's economy, it is regrettable that its potential has yet to be completely realized. This research aims to develop a model for identifying the relationship between barriers to maximizing the performance of the Halal Industry. This research used a structured literature review and content validation to identify the valid barriers. Then, this research used Interpretative Structural Modeling (ISM) and the Matrice d'Impacts Croises-Multipication Applique (MICMAC) analysis to present the model relationship between barriers and group the barriers. The result indicated thirteen valid barriers. The most critical barrier is a lack of control and coordination supporting a viable halal industry ecosystem from different government agencies.

The adverse sustainability consequences of freight transportation have compelled stakeholders to study the modal transition from unimodal road to the intermodal railroad. This motivates identifying and prioritizing modal shift initiatives (MSIs) adopting modal shift from unimodal road to intermodal railroad (IRR) freight transportation. This research employs a multi-criteria decision-making method utilizing the spherical fuzzy Bayesian best-worst method to evaluate the relative weights of MSIs. Applying the proposed framework to the Indian freight transport sector reveals that ‘government initiatives’, ‘infrastructural and technological initiatives’ and ‘management initiatives’ are the most significant categories of MSIs. Furthermore, ‘managerial support and commitment towards the modal shift to IRR freight transportation’, ‘invest in the expansion of rail network and dedicated freight corridors’ and ‘high budgetary allocation to develop rail infrastructure facilities’ are the most crucial initiatives among the 36 MSIs. Additionally, the results of this study can provide valuable guidance for logistics managers, freight shippers, and railway officials in decision-making about the modal shift to IRR freight transportation.

The circular economy (CE) model has been widely applied in large businesses, but very few research has been conducted on how CE may be applied in small- and medium-sized companies. Although emission of individual SMEs might not be comparable to that of LEs, the total emissions holistically have a significant impact due to large number of SMEs. Hence, the present paper aims at exploring barriers for CE transition in SMEs based on a systematic review of literature. 46 selected articles were screened according to eligibility criteria and then analyzed. According to the findings, the most frequently identified barriers are a inadequate financial resources, inadequate organizational capabilities, and a lack of regulation/standards.  Potential paths for future research are also discussed.

Coordination plays an important role in facilitating effective and efficient humanitarian operations. However, coordination in humanitarian operations is not an easy task due to uncertainty and limited available time to establish coordination. The present paper aims at identifying perceived barriers to coordination among humanitarian organizations. An empirical survey involving 150 experienced practitioners from both governmental and non-governmental humanitarian organizations was conducted. Findings indicate that the highest quartile of the perceived barriers are related to inter-organizational compatibility, i.e., incompatibility on mission and goal, timeline, structure/policies, work culture, lack of sense of togetherness; organizational barriers including risk of dependency, limited resources; and external factors in terms of demand uncertainty. The least quartile of the perceived barriers is dominated by organizational barriers with respect to lack of incentives, lack of identity, and risk against fast response, and inter-organizational barrier in terms of communication/language barrier. The potential strategies dealing with the barriers are explored. Avenues for future research are also discussed.

This research aims to investigate a strategic method for determining suitable locations to build cross-dock facilities for a specific company that distributes traffic safety products to government offices across every province in Thailand. By having a predetermined location of customers and the corresponding demands for each customer, the cross-dock facilities must be placed in locations where the costs of renting, the cost of travelling from the headquarter, and the cost of travelling to and from customers are minimized while satisfying vehicle capacity constraints. The problem was decomposed into two distinct parts. The first part is minimizing data size using a k-mean clustering approach via a GIS platform to group customers based on location. The result from this clustering process is then utilized in the second part, which involves using the optimization method to find the optimal location for the cross-dock facility and the optimal routes from that cross-dock to all customers in each cluster group. By utilizing this approach, the research shall provide the company with a comprehensive solution that will minimize company expenses and, at the same time, maximizes the efficiency of its distribution network. This optimization could positively impact the company’s profitability and improve its competitiveness in the marketplace.

The food supply chain at present is important to the human and there have a lot of transportation modes to distribute food from manufacturer to customer also business to business. The customers there need fresh food and the providers always find the best way in logistics that send their food to customers with the best quality and fresh, that is why the cold chain of logistics is popular and needs to improve more for the customers. However, there is no clear way to improve the performance and assessment of food logistics service, the aim of this study focused on service quality of food logistics and identify a set of service quality dimensions that developed from the SERVQUAL model for measuring their logistics process after that researcher prioritized the dimensions and sub-dimensions by Analytic Hierarchy Process (AHP) and finalize the local weight and global weight from AHP result. Finally, this research has shown the providers and the customers were prioritized for punctuality in food logistics service. It should be concern and improvement about punctuality in a food logistics service at the same time, the worst dimension of the provider's perspective is tangible and the customer perspective is empathy, that they did not pay attention it should be improved later.

India was the world's fourth largest manufacturer of cars and Indian automotive industry is expected to reach Rs.16.16-18.18 trillion by 2026. The domestic Indian auto market is dominated by two wheelers and passenger vehicles. There are many researchers highlighted the importance of prediction of car sales rate and proposed different traditional time series and artificial intelligence models (Chen et al., 2018; Vahabi et al., 2016; Shed Shahabuddin, 2009; Hülsmann et al., 2011; Fantazzini and Toktamysova, 2015; Karaatlı et al., 2012; Akyurt, 2015; Wang et al., 2011; Pai and Liu, 2018; Fleurke, 2017; Aslankaya and Oz, 2018; Xia et al. 2020). All these models considered different economic indicators to predict the car sales rate. Indian auto market is growing and there are various factors affects the car sales rate. As the Indian auto market is dominated by passenger car hence in this study the passenger car sales rate has been predicted considering the various economic indicators such as IIP, crude oil price, employment rate, exchange rate, CPI of transport motor car, landing rate and GDP using ANN model.

Capacitated facility location problem (CFLP) is a classical combinatorial optimization problem widely applied in the domains of distribution, transportation planning, and telecommunication. As a typical NP-hard optimization problem, CFLPs featured by combinatorially high-dimensional decision spaces are not easily solved by most conventional methods. To appropriately handle the hard nature of CFLPs, we propose a deep reinforcement learning (DRL)-based framework to address CFLPs with discrete expansion sizes. Since a solution to the investigated CFLP can be sequentially constructed by partial solutions, we reformulated the CFLP as a Markov decision process with an unfixed and discrete time horizon. A deep Q-network (DQN)-based framework is adopted to learn the policy parameters and location solution. We experimentally demonstrate that our proposed approach can effectively find near-optimal solutions for CFLPs.

The resource-constrained project scheduling problem describes a situation in which the duration of a project must be minimized by choosing a start time for each project activity subject to given precedence constraints and resource capacities. Various mixed-integer programming models exist for this problem. Approaches that extend these models to enhance their performance are often formulationspecific or cannot be easily integrated with the original model, which limits their practical applicability. We suggest a model extension based on auxiliary variables and redundant constraints that describe workload limitations for certain subsets of the planning horizon. We apply our approach to three state-of-the-art models from the literature. A computational evaluation demonstrates that the extension is beneficial to all three models tested.

The stock market is one of the investment options that offer the best potential returns. However, investors are aware of the risk associated with their investments, which is why stock portfolios are established to diversify investments or distribute investments. Managing a portfolio may be challenging because there are so many different stocks. One of the challenges is deciding how much money to invest and how to allocate it for the best results. This paper used the K-means Algorithm for cluster analysis in identifying Decision Making Unit (DMU), the stocks will be grouped according to their characteristics considering their risk and return. Asset allocation per clustered portfolio was performed and the Mean-Variance Model was used to optimize clustered portfolios. This paper aims to use Data Envelopment Analysis (DEA) to evaluate the efficiency of these clustered portfolios/DMUs. In addition, this paper introduces subjective choice in decision-making using the criterion's level of importance to help investors decide which among these efficient DMUs is a better option. The results show 4 Efficient DMUs for 2019 Data, and 3 Efficient DMUs in 2020 Data. All simulations were carried out using the MATLAB environment platform.

Multi-hole drilling is a significant operation in manufacturing industries. There is a class of products that requires a specific pattern of multi-holes. Moreover, the drill-path sequencing generated by commercial CAD/CAM software falls short to achieve optimum tool travel distances. This is due to the combinatorial nature of the multi-hole too drill path sequencing problems. Hence, the researchers have proposed various evolutionary algorithms. In this study a recently proposed Teaching-Learning-Based Optimization (TLBO) algorithm in the discrete space domain is used for optimizing the multi-hole drill toolpath sequencing of concentric circular patterns. The optimization results are compared with the highly successful Ant Colony Optimization (ACO) algorithm. The study highlights the weaknesses and strengths of the canonical form of the TLBO and proposes an approach to remove the weaknesses.

Collaborative planning for aircraft maintenance operations emerges with the transition of aircraft Maintenance, Repair and Overhaul (MRO) outsourcing practice. Airline outsources its fleet’s maintenance operations to an independent MRO service company, involving a cross-organizational aircraft maintenance planning issue with maintenance demand and service supply’s matching along the planning period. A bi-level mixed-integer linear programming mathematical formulation is proposed in this paper, which describes the hierarchical relations between airline and MRO company to facilitate planning decision associated with maintenance operations. In the bi-level maintenance planning, airline leads and initiates the collaborative maintenance through its fleet’s maintenance plan, then service company is the follower reacting to the MRO demands from airline according to its maintenance capability limits. The bi-level framework aims to reconcile the conflicts between MRO demand and supply. The interdependent relations between MRO demand and supply, as well as the bottleneck in causing planning conflicts are modelled and quantified in the bi-level model. We further proposed an algorithmic framework for solving the proposed bi-level problem.

Railway organizations are currently engaged in the pursuit of digitalizing rail freight transportation to optimize resource utilization and increase revenue. The use of IoT technology combined with real-time information enables train management systems to provide accurate GPS data. However, freight transport operators frequently encounter the challenge of rescheduling freight trains in real-time without the aid of a decision-making system. To overcome this obstacle, an IoT-based real-time rake schedular-reschedular heuristic has been developed. Computational studies have demonstrated that this heuristic is both efficient in terms of runtime and produces high-quality solutions. The implementation of this approach would enable freight transport operators to optimize their business operations, increase their monthly revenue, and re-schedule their rakes efficiently.

Increased consumption of natural resources due to a growing population requires solutions for sustainability and conservation. Eco-industrial parks (EIPs) have been built to promote sustainable industrial development but have yet to consider the social impact in terms of maximizing job opportunities and uncertainty in water output quality. In this study, a multi-objective optimization model for wastewater treatment in EIP was developed to minimize economic and environmental waste, maintain plant resiliency, and increase job opportunities that will improve the quality of life in the community that the EIP will be built upon. By adding the social aspect to the model through job opportunities, the model opened more plants and pipes, creating a more costly but more resilient EIP Network. By introducing uncertainty in output water contamination from plants, the achieved EIP Network is found to be not vulnerable to change. Insights from considering both social impact and uncertainty in output water quality help EIP managers to make informed decisions to build more plants and create more connections to design a sustainable EIP.

In this talk, we introduce several approaches to model the uncertainties, such as natural water inflow, wind and solar power output, and electric load demand, and construct the multistage robust optimization models for the operations of the hybrid hydro-thermal-wind-solar systems. For example, a data-driven dynamic ambiguity set is introduced to model the uncertain net load, which exhibits significant spatial-temporal correlation. We explore how the operational flexibility of hydroelectric generation, and the coordination of thermal-hydro power can be utilized to maximize the economic benefits while reducing the carbon emissions. Some cutting plane and decomposition-based algorithms are proposed to solve the constructed models. Numerical experiments will be performed on real practical cases to validate the proposed models and to evaluate the efficiency of the algorithms.

This study explores the potential impact of implementing a sustainability-focused product configurator using a construction company case. Qualitative interviews were conducted with key stakeholders to gain insights into the benefits of such a configurator. The findings reveal that a sustainability-focused configurator has the potential to provide several advantages. Firstly, it can enable earlier sustainability assessments, allowing designers and engineers to make informed decisions during the initial phases of a project. Secondly, the configurator enhances customer communication by providing them with a clear understanding of the environmental impact of different product variants. This transparency empowers customers to make more sustainable choices and supports the overall goal of promoting environmentally friendly products. Additionally, the configurator can reduce process lead times, enabling faster life cycle assessments (LCA) and facilitating the sale of sustainable products with lower environmental impact. These results reinforce the impact identified in previous studies while deepening the understanding of the topic.  While the findings suggest the significant potential of sustainability-focused configurators, further research is needed to expand upon these insights.

Videogames have been used within education, and previous research has widely discussed the benefits students can gain from videogames. However, studies analyzing students' acceptance of videogames as a learning activity are scarce. Educators should realize that students may not gain the full educational benefits that videogames offer if they do not have a genuine interest to accept and play the game. This study aims to determine students’ acceptance of architecture-related content videogames throughout their landscape architecture education. This study adopted the simplified unifier theory of acceptance and use of technology 2 as the theoretical framework, primarily investigating the role of hedonism motivation, habits, and social influence in students' play intention and behaviour. A total of 58 participants’ responses were analyzed using partial least squares structural equation modeling (PLS-SEM). The results revealed that hedonic motivation and habit have significant effects on students’ playing intention, whereas social influence lacks a significant impact. In addition, the students’ play behaviour of architecture-related content videogames is determined by their intentions.

Wearable healthcare technology (WHT) has been developed for a more active and healthier lifestyle. However, after adoption, many users abandon it. Therefore, this study aims to identify important factors contributing to WHT’s continuance usage intention, separating younger and older users. A questionnaire was developed, including ten WHT-related factors: functionality and accuracy, ease of hardware use, ease of software use, effectiveness, privacy protection, social value, epistemic value, emotional value, perceived control, and concentration. The online survey was conducted and collected responses from 378 WHT users, of which 278 were under 50 years old. The regression results showed that the ease of hardware use positively influenced all the users’ continuance usage intention. In addition, perceived social value and epistemic value contributed significantly to younger users’ intention; while perceived control had a significant impact in the older-user group. Therefore, to improve WHT’s continuance usage, companies need to focus on the ease of hardware use; to design WHT that can draw younger users’ curiosity and expand WHT-related social connection; and to support older users about how to use WHT for their personal healthcare management.

Openness for sharing knowledge is a key value mechanism in firms’ R&D network innovation. However, existing studies have ignored the potential variation in the effectiveness of openness across different types of innovation activities. This study fills in the gap by introducing the network pluralism approach and distinguishing between firms’ exploratory R&D networks and exploitative R&D networks, which are distinct in nature. By collecting collaborative patent data in the new energy vehicle industry from 2000 to 2020, we empirically analyzed the two dimensions of openness, namely breadth and depth, and examined their effects in the firms’ exploratory and exploitative networks in their pursuit of technological innovation. The results suggest that a higher level of openness depth within firms’ two kinds of R&D networks leads to improved technological innovation performance. The relationship between openness breadth and technological innovation follows an inverted-U-shape in both kinds of R&D networks. Moreover, both openness breadth and depth conducted in firms’ exploratory R&D networks play a more effective role than those in exploitative R&D networks.

One way to counter the increased competitive pressure in globalised markets is to increase a company’s own innovation capacity. In most cases, innovations are a particular result of the environmental influences on the product. Text mining aids the systematic analysis of the various external influence dimensions with their large amount of data. The developed method developed in the context of this research identifies innovation potentials in the product environment and showcases their influence on the existing product. First, text data is retrieved from external influence dimensions. Through specific preprocessing, this data is transformed into a document term matrix, which is the basis for subsequent topic modelling. Finally, a semantic network shows the interconnection of both, the existing product and the analysis results.

Using the Job Demands-Resources (JD-R) model as the theoretical foundation, we examined the curvilinear association between instant messaging interruptions and task performance. We also studied the potential moderating roles of job autonomy and work mode (traditional and remote). Previous studies revealed mixed findings on the relationship between instant messaging interruptions and task performance. While receiving messages (a type of job demand) can increase individuals’ workload, engaging in message exchanges and responding to them promotes collaboration and communication among coworkers. This, in turn, can enhance individuals’ sense of task completion, especially when working remotely and having job autonomy as a job resource. Using data collected from 443 employees at two different time points in Taiwan, we found a U-shaped relationship between instant messaging interruptions and task performance. Moreover, job autonomy and work mode moderated the above relationship such that the U-shaped relationship was stronger for individuals with high job autonomy and in remote working mode, respectively. In contrast, the relationship shifted to an inverted U-shape under low job autonomy and traditional working mode.

The population of ASEAN is steadily increasing and with rapid urbanization, there is mounting pressure on the issue of food security. Fears are that traditional farming methods alone will not be able to meet the increased demand for food. Fortunately, the emergence of disruptive technologies in fields such as agriculture has brought about efficiencies in the way things work. Smart farming techniques and technologies can increase farm outputs and improve food safety, at the same time reducing harmful effects on the environment. While the adoption of smart farming technologies has achieved success abroad, there have been mixed responses across Southeast Asia. Policymakers must address the reasons why the transformation of agriculture industries in ASEAN may not be smooth. Given the strong interdependence among the 10 member states for the provision of food and agricultural products, it is advisable to adopt a regional strategy to ensure food security. As developments on smart farming in the region are current and an ongoing process, a qualitative approach was applied to address the research problems in question.

This study examines the impact of demographic characteristics and technology adoption on the sales growth of small and medium enterprises (SMEs). The demographic factors include the type of industry and SME age, as well as the gender, age, and educational background of the owner. Technology adoption encompasses the utilization of social media, electronic-commerce (e-commerce), and production-related technologies. The present study employs inferential statistical methods, such as chi-square and Spearman’s rank tests, and is based on data collected from 119 SMEs in Lampung Province, Indonesia. The findings reveal that the type of industry significantly influences sales growth. In contrast, the SME age and the gender, age, and educational background of the owner did not substantially affect sales growth. Notably, a correlation between social media adoption and sales growth was observed within the context of technology adoption. Furthermore, the statistical analysis indicated a significant linkage between industry type and the adoption of social media, e-commerce, and production technology. The study concludes with recommendations for future research directions.

After society encountered the concept of open source, something open has increased, and a lot of open source software (OSS) has been created. Nowadays, it is said that OSS is the best success of open innovation. Many enterprises incorporate OSS into their products and services, and OSS based on new ideas makes technological trends. In order to catch up with the latest trends of OSS via key persons, centralities of networks are available. This research investigates the appropriate ranges of parameters in order to get persuasive results. Consequently, a data set with accounts having 1–9 “following” in repositories created in the last two years is enough to identify the key persons. It is confirmed that key persons are tackling popular OSS and the observations on the accounts of key persons revealed their responsible OSS and new ways of life.

Open source projects with open source software (OSS) have permeated society, and no industry can exist without OSS. At the same time, the risks and life cycle of OSS have severely impacted business and social life. Therefore, organizations utilizing OSS have started to analyze trends of OSS and use the analysis in their own organization's strategy. In an effort to look at OSS from a bird's eye view, OSS classification is important for human recognition. Without the OSS category, important OSS for the organization's business will be missed among too many OSS. At present, as multiple initiatives coexist, each uses its specialized OSS category, even in a single organization. For a long time, OSS stakeholders have manually organized OSS categories, and they have put in a great deal of effort. If manual work continues in the future, much effort will be required in the rapidly changing world of OSS. This research is fundamental and widely available for every analysis related to OSS, providing a universal methodology for OSS categorization with high validation accuracies of 0.98 by means of artificial intelligence technology. During this execution, there was an issue that the accuracy was low due to the small amount of suitable training data for OSS, but it was resolved with data augmentation.

Recently, blockchain technologies seem to have emerged from a period of disillusionment named in the hype cycle, and development has become active again. In this research, substitute and complementary repositories were identified from GitHub records in order to grasp the state of representative blockchain platforms: Bitcoin, Ethereum, Hyperledger, Ripple, and Corda. Within many blockchain platforms, it is common to have complementary relationships. Ethereum and Hyperledger also have a complementary relationship across platforms. The results showed that Ethereum is reactivating Hyperledger, whose development is stabilizing. This research proposes a methodology to find next-step software development from the network based on developers' skills via their movements between repositories.

This paper applies a set of data-driven model specification procedures, including LASSO, Ridge and Elastic Net regressions, to predict vessel values for oceangoing merchant vessels. A large dataset is utilized for over 16,000 vessel transaction records that include vessel technical specifications, details about the transaction, as well as the freight market conditions at the time of sale. The empirical results show that the inclusion of factor variables improves model performance and that more advanced models are required to handle the high dimensionality in such a rich dataset.

The purpose of this research is the need to reduce backlogs and distribution for technician problems in the maintenance process. The distribution of work to technicians for maintenance will be effective in meeting the goals of the maintenance system of the University of the Thai Chamber of Commerce to be more efficient while reducing delays in maintenance by implementing Machine Learning (ML) using the form Support Vector Machine (SVM), while research is developed on the application UTCC-CMMS is able to work more comprehensively by allowing the application to act as a staff. The work related to maintenance can receive maintenance requests on the algorithm VSM to suitable technicians. and evaluate the performance as well. Also, contact purchasing or support external technicians in maintenance, resulting in faster maintenance there is a lot of backlog work reduced than before, allowing the technician and everyone to work together as well. The evaluation of operations in the past found that it received a score of 2.44. After the adoption of ML, it was found that the score was 4.44, representing an increase of 81.96%.

The COVID-19 pandemic has had a significant impact on the global economy, including the stock markets of many Asian countries. Forecasting stock prices is a challenging task, but it is essential for investors to make informed decisions. This study compares the performance of ARMA-GARCH and RNN models in forecasting the stock price index of four Asian countries (China, Indonesia, Japan, and South Korea) during the COVID-19 pandemic. The results show that the RNN model outperforms the ARMA-GARCH model in terms of forecasting accuracy, as measured by R-squared. The findings of this study suggest that RNN models are a more promising approach for forecasting stock prices during times of economic uncertainty. However, the methods used in this research can be further developed to improve model accuracy and precision. For example, future research could explore the use of longer time periods, other RNN variants such as LSTM, different data splitting techniques, and other hyperparameter tuning strategies.

This paper focuses on comparing the performance of two supervised learning methods, SARIMA and Facebook Prophet, in predicting stock prices of Indonesian companies. SARIMA, an extension of the ARIMA model, incorporates seasonal components in the data meanwhile Facebook Prophet is a business forecasting model that incorporates trend, seasonality, and holiday components in its approach. The study analyzes five sets of stock data and measures accuracy using the R-square method. For SARIMA, the data undergoes stationarity testing, model selection with hyperparameter tuning, and prediction of stock prices. Facebook Prophet involves dividing the data into training and testing sets, fitting the model, and making predictions using various components. The comparison is based on the R-square values of the predicted data. The findings reveal that the SARIMA method outperforms Facebook Prophet in predicting stock prices for the selected Indonesian companies, as indicated by higher R-square values obtained from SARIMA.

Although landfills damage the environment due to over-exploitation of land resources, several countries still depend on this waste management strategy. To reduce this dependency, several countries have begun to increase the level of waste recycling due to regulations pressure from regional governments (e.g., European Union). There are also initiatives from individuals or organizations to create a more circular waste management business, which seeks to extend the product life cycle from waste recycling, reuse and reduction, has been developed. This research attempts to map the interrelationship between government policy and circular waste management (CWM) business initiatives from individuals or organizations. Using a System Dynamic (SD) approach, this research produces qualitative and quantitative model to explain the interrelationship. From the SD model simulation, the results show that the decrease in the amount of waste sent to landfills is affected by the population size and the level of waste production per capita. While qualitatively, the model shows that capacity building both in quality and quantity and waste segregation campaigns for the community are important in growing the CWM business in the future.

Fluctuations in the global oil market have been a crucial subject matter where forecasting models have been formulated and analyzed for the global economy. With the rise of oil prices in the last three years, this study aims to formulate a system dynamics model to investigate the global trend of oil prices. The proposed model was developed to provide a framework for understanding the leverage point under the effects of COVID-19 disruption and Russia-Ukraine conflict. Countermeasures were also simulated to investigate their effectivity in modifying the oil price’s dynamic behavior. The results indicate that policies involving the capping of prices can undoubtedly address the inflating price problem. However, it does not address the problem regarding the reliance on the commodity, which may result in a similar record-breaking price shock in the future. Conversely, policies directly targeting the supply-demand gap such as alternative resource generation and behavioral shift can address the reliance on oil, which indirectly deals with the oil price dynamic problem.

Efficient production and manufacturing are vital global industries with far-reaching impacts. Reducing operational errors and waste is crucial in sustaining profits. Process improvement is pivotal across sectors by ensuring consistent results, heightened efficiency, and standardized procedures. This study assesses company productivity, identifies operational flaws, and proposes remedies for enhanced production efficiency. Employing diverse tools and methodologies, our investigation achieves these goals. Results showcase a remarkable 21.42% increase in work efficiency through Observation, Time and Motion Study, ProModel Simulation, and Stopwatch Time Study. This surpasses the targeted 15%-20% enhancement for 2-PC tin can production. The proposed model can potentially reduce manufacturing time from 45.89 to 36.05 minutes. The study highlights operational inconsistencies within workforces of even well-established manufacturers, contributing to rectifying overlooked degrees in larger corporations. Utilizing promodel software, daily production escalates from 25 to 35 pilings, reflecting a significant 40% increase in output. This paper proposed manufacturing improvement and the importance of scrutinize fundamental components to uncover underlying issues.

Although the manufacturing sector now reaps the most benefits from digitization, the oil & gas sector is increasingly embracing digital technology to boost system efficiency, particularly when it comes to modeling and simulation. The oil & gas industry is a complex and multiscale system, making it more challenging to construct a complete and accurate model. This paper presents an algorithm based on the combined use of Fuzzy Cognitive Maps (FCMs) and Gray Wolf Optimization (GWO) to identify the minimal causal model for estimating the level and pressure of a vertical tank in a multiphase liquid-gas plant. Two FCMs were modelled to regress tank level and pressure separately, to analyze the minimal causal relationships among the involved variables. By choosing only simulations concerning the most usual working conditions for the plant as the training dataset, an average accuracy in the training phase of about 85% (with peaks of 99%), and 90% in the testing phase, could be achieved.

The contribution of this research is to improve the efficiency of the production process of seasoned seaweed products at the selected pilot plant. The study found that the current batch process is time-consuming and results in a small output. To address this issue, the study suggests transforming the production line into a continuous process. A simulation program was employed to analyze the redesigned production line, incorporating the substitution of certain steps with studied machines. The findings from the operation indicate that the minimum area needed for the new production line is 216,152 square inches. With this redesigned process, the production output is nearly ten times higher within the same timeframe. The findings from this study can be used by other seaweed producers to improve their own production processes. By switching to a continuous process, seaweed producers can increase their output, reduce their costs, and improve their profitability. Overall, this research makes a significant contribution to the seaweed industry by developing a more efficient and sustainable production process.

This paper presents a comparative analysis between our Discrete Event Simulator (DES) and a real industrial case in the automotive industry. The study’s objective is to evaluate our simulator’s accuracy and reliability in fast predicting assembly line performance in a real-world manufacturing setting. The study includes data from a plant in the automotive industry and compares the estimated results from our simulator with the actual data collected from the plant. The study findings suggest that our simulator is highly accurate and indicate a strong correlation with the actual data. The mean absolute error and root mean square error of the simulated/real cycle times of resources are also found to be low, implying that the simulator’s estimates are highly reliable. These results demonstrate the effectiveness of our simulator in predicting the performance of manufacturing systems in the automotive industry and provide fast and valuable insights that can be used to optimize production processes and improve productivity.

Power distribution systems are complex, posing technical challenges and risks that require substantial resources for maintenance. To optimize predictive maintenance while upholding safety, environmental standards, and organizational prestige, this study introduces a time series analysis method. The proposed methodology involves using Python programming-based time series predictions in power distribution system failures, especially for medium voltage level (33kV,11kV) failures. The application of the proposed methodology is demonstrated by a real case study in a highly dense area of Colombo, Sri Lanka. This study will help relevant officials to make accurate decisions on maintenance investments, utilizing scarce resources through an effective methodology.

The Lifecycle Preventive Maintenance (LPM) scheduling of the machining station with unstable demands is studied. Due to unstable demands of products, the station operates under uncertain working conditions. Then, the LPM scheduling is much challenging because of the unpredictable deterioration of the station. A two-phase Preventive Maintenance (PM) scheduling frame is proposed to incorporate both the lifecycle information and the unstable demands. The numerical examples and the policy comparisons show that the two-phase PM scheduling frame is independent of the demand prediction. Furthermore, the PM trigger based on the cycle proportion is more cost-effective than both the one based on the time interval and the one based on the failure rate. It can provide cost-effective, robust and convenient LPM scheme to the station user.

Safety-related systems get a lot of attention especially in automotive industries along with the widespread of the advanced driver assist systems. Generally, the most of safetyrelated systems are composed by the electrical / electronic / programmable electronic devices, such as E/E/PE safety-related systems, and play an important role for preventing hazardous event occurrence of whole system. For maintaining designed safety level of the E/E/PE safety-related systems, proof-testing is conducted in operation. The proof-testing is known as periodic maintenance activity for detecting and repairing fault which could not be detected by automated diagnostic fault checking system. This paper derives mathematical optimal policy for obtaining economic proof-testing intervals by considering the cost at hazardous event occurrence and proof-testing. Numerical illustrations for our optimal policy are also shown for explaining how to apply our approach in possible practical situations.

Material testing equipment is a significant piece of equipment in the process of material development. Its working condition is extreme and complex. However, there is no mature mode for the operation and maintenance of this kind of equipment. The research designed and developed a platform for the operation and maintenance of material service performance test equipment, the platform was designed for the whole process of material testing, and functional design was carried out from three scenarios: experimental preparation, test process, and data analysis. Based on the whole process, the platform developed operation and maintenance modules: Test Data Monitoring Modules, and Business Function Modules. To ensure the usability of the platform, the interface design was carried out according to the economic principle of information processing during the development process, and the developed platform has been practically applied in a laboratory.

In large-scale power customers, when a ground fault occurs, the circuit breaker operates, and electricity becomes unavailable (power outage). Thus, enormous losses are caused, such as the stoppage of production lines. Currently, ground fault causes in distribution lines for large-scale power customers are identified by human experts who observe the current waveform. It takes much time to identify the cause by human expert. Therefore, identifying the causes as quickly as possible is essential to reduce the outage time and losses. One of the efforts to solve this challenge is the automation of the ground fault causes identification. Related work proposed a threshold judgement method using four values to identify the causes. However, the method couldn’t achieve a sufficient accuracy for practical use. Therefore, in this study, we propose a method that uses machine learning to replicate the identification by human expert and give sufficient accuracy. We confirmed that the proposed method can identify the five causes of the ground fault with an accuracy of over 90%.

With the increase in structural complexity and reliability requirements of modern industrial systems, Standby Repair Units (SRUs) are widely used in phased mission systems to recover system performance immediately. To better analyze the availability of phased series systems under the limited mission interval, cannibalization strategy or replacement strategy is considered. This paper proposes an availability analysis method based on the cannibalization strategy for the series system with equal mission intervals by considering the number of spare parts, the priority of maintenance of faulty parts, and the maintenance waiting time. Experimental results have demonstrated that this method can significantly reduce mission delay costs and effectively improve mission availability. Finally, the average availability improvement and maintenance cost reduction are discussed by considering different mission durations.

Businesses and industries are implementing their business objectives with the assistance of machines. Unfortunately, machines deteriorate with time. The current study investigates global manufacturing industry maintenance strategies and their projected evolution. We used secondary data from Statista databases published between 2016 and 2023 and English as the language of the study. According to the findings of the study, preventive maintenance is the most prevalent strategy for asset care in the manufacturing industry. Trends indicated that predictive maintenance and other maintenance strategies were declining. It was suggested that future research investigate the challenges of implementing predictive and reliability-centred maintenance strategies in order to assist the manufacturing industry in reaping the benefits.

Since the framework conditions of manufacturing companies change dynamically, production control must react to this and be adaptive and dynamically designed. Our article addresses the need of industrial production management systems in the course of enhanced digitization. The aim is to examine the extent to which traditional systems for controlling and optimizing production systems have been supplemented by Industry 4.0 concepts. In the course of the scarcity of resources and the shortage of labor, the human factor is once again coming to the fore. Against this background, the interaction between users / humans and artificial intelligence applications will be the main focus. The result should give an indicator how this connection must be considered in the future and what should artificial intelligence do in the context of production control. The findings will be the basis for future considerations of a smart production management system, which can be used for decision support as well as for auto-control.

Improving the reliability and flexibility of key process assembly schemes is a critical factor in the intelligent management of aircraft production. However, the complexity of aircraft assembly operations, large-scale production, and fierce competition for resources aggravate the difficulty of key process identification in the aircraft assembly manufacturing system. To efficiently recognize the key processes and facilitate the management of the aircraft assembly, this study proposes an adaptive influential node identification framework. The aircraft assembly manufacturing system is transformed into an assembly complex network (ACN) according to the technology. Then, the problem of community detection is addressed by applying the Louvain algorithm to partition ACN into several communities. Moreover, within each community, the structure characteristics and physical information are combined to evaluate the influence of the node. Finally, the experimental results demonstrate the efficiency of the proposed framework in adaptively analyzing ACN and identifying key processes in the aircraft assembly manufacturing system. This study provides an efficient and convenient solution for key process identification in aircraft assembly system management.

The production tasks of aerospace enterprises are increasing and the order demands are variable. The production flexibility, production capacity and efficiency of aerospace products are gradually enhanced. Usually, because of unbalanced process time of works, the operators in aerospace products assembly line are not belonged to a fixed workstation. For dynamic scheduling of limited operators in shift-based assembly lines of this paper, the knowledge of scheduling decisions for multiple dynamic changes in the assembly line is analyzed, and a decision model based on a weighted fuzzy Petri net is proposed, which can reasonably allocate the limited operators to different assembly stations in order to improve the assembly efficiency. A case study is given to shown the effect of the operators dynamic scheduling strategy on the throughput improvement of the assembly line.

Distributed manufacturing can effectively improve production efficiency and shorten delivery cycle, which is one of the current research hotspots. Distributed permutation flow shop scheduling problem is a classical NP-hard problem, which includes two parts: job allocation and job ordering. However, most current researches focus on job ordering, which leads to local optimal solutions. Therefore, This paper proposes a distributed permutation flow shop scheduling method based on efficient job allocation. First, this paper studies the two sub-problems of the job allocation problem respectively, proposes the corresponding fast estimation method and proves it by experiments. Second, according to the characteristics of distributed permutation flow shop, the corresponding specialized efficient scheduling algorithm is proposed. Finally, 90 cases on the famous TA benchmark were selected for experiments. The experimental results showed that the proposed algorithm obtained 79 optimal solutions, and the coverage rate of the optimal solution reached 87.8%, which verified the effectiveness of the algorithm.

Load forecasts are becoming increasingly important in an increasingly digitalized world, even for smaller companies, for energy procurement or operational optimization. At the same time, it is unclear how much historical data is required to calculate a sufficiently good forecast. To answer this question, this work investigates the impact of training set size (historical load, weather, and calendar information) on the predictive performance of a day-ahead load forecast in the industrial context. For this purpose, a use case study on the data of seven companies from the manufacturing sector using six model classes was conducted. The results suggest that a forecast can produce meaningful results on 18 months of data whereas a period of less than six months yields results of high variance. For six out of seven companies, the best model trained on historical data less than or equal to one year was at most 1.5% (MAPE) worse than the overall best model.

Additive manufacturing (AM) has risen as a revolutionary tool in the automotive industry, facilitating the creation of intricate, lightweight, and tailored parts that enhance efficiency and eco-friendliness. This mini-review offers insights into the present landscape and potential directions of AM in the automotive industry. A range of AM techniques, such as Selective Laser Sintering (SLS), Stereolithography (SLA), Binder Jetting (BJ), Fused Filament Fabrication (FFF), and Selective Laser Melting (SLM), are discussed, emphasizing their mechanisms, materials, and particular uses in the automotive domain. Furthermore, the advantages of AM in the automotive industry are discussed, emphasizing material efficiency and lightweighting, design flexibility and customization, rapid prototyping and accelerated product development, as well as applications in electric vehicles (EV). Innovative applications and case studies are presented to showcase the recent advancements in the automotive industry facilitated by AM. By understanding the current state and potential of AM, stakeholders can better strategize and harness the power of AM to drive the future of automotive design and production.

With the increasing global emphasis on sustainability, it is crucial to explore strategies that enable industries to adopt environmentally friendly and resource-efficient manufacturing processes. The study specifically focuses on enhancing the competencies of professionals in additive manufacturing, a multidisciplinary field encompassing mechanical engineering, electronics, and computer science, to contribute to sustainable production practices. Problem-centered and guided expert interviews were conducted with 12 experts from diverse industries to accomplish this. The interviews were transcribed verbatim, and Mayring's content analysis method was employed to evaluate the transcripts. This methodology identified vital competencies, including knowledge of technology and materials, part identification skills, and a comprehensive understanding of the AM process chain. The findings also highlighted the significance of soft or interpersonal skills, such as teamwork, effective communication, adapting to multicultural and diverse environments, and more. Furthermore, the interviews revealed opportunities for sustainability and emphasized the importance of increased collaboration among companies and universities involved in AM. The insights gained from this research will inform the development of training programs and guidelines to foster sustainable production in advanced manufacturing.

The generation of alternative policies is essential in complex decision tasks with multiple interests and stakeholders. Today such settings are common in the mitigation and management of environmental impacts by governments and industries. A diverse set of policies is typically desirable to cover the range of options and objectives. Decision modelling literature has often assumed that clearly defined decision alternatives are readily available. This is not a realistic assumption in practice. We present a structured process model for the generation of policy alternatives in settings that include non-quantifiable elements and where portfolio optimisation approaches are not applicable. Behavioural issues and path dependence as well as heuristics and biases which can occur during the process are discussed. The experiment with the climate change mitigation game compares the results obtained by using two different generation techniques. The results show that the outcome can be process dependent. Modelling support in policy problems needs to be combined with processes for the generation of alternatives paying attention to the related behavioural effects.

Over the past few years, the number of newly registered EVs (electric vehicles), hybrid, and plug-in hybrid EVs has rapidly increased in the markets across the globe^[1]. All these vehicles are categorized as battery-operated EVs, carrying Lithium-ion batteries as their fueling battery technology. Due to the narrow area where lithium-ion batteries operate, the problem of environmental, geostrategic, and economic issues need effective management and control. However, if the rate at which lithium-ion batteries are recycled remains at a low level, it will cause a considerable increase in battery demand, which could reach a shortage level soon, and secondly, it will lead to exponential growth in hazardous waste. Thus, this paper aims to identify roadblocks/barriers to reverse logistics of Lithium-Ion batteries.  Analytical Hierarchy Process (AHP) and Fuzzy AHP give precedence to the barriers of Reverse logistics adoption. The study has identified 24 barriers under five constructs using expert opinion and a literature review. The results of this work indicate that Organizational, Technological, and Strategic factors are the three major roadblocks to RL adoption.

Because of the growth of financial services such as stock market investments, mutual fund investments, home loans, insurance, etc., both academics and banking professionals are paying more attention to online promotions due to their significant influence and better reach to customers. Social media is a great platform for both firms and their customers to exchange information among communities. Sometimes this kind of communication is affecting the customer's trust in financial services. The purpose of this study is to determine and rank the factors that affect customer trust in financial services through social media online reviews. The methodology has been proposed based on a mixed approach. First, the data was extracted from Kaggle databases. Then using NLP to determine the factors and applied the TOPSIS method to prioritize the rank. This study’s results determined that security, fake promises, less ROI, firm credibility, and wealth of the firm’s factors are affecting the trust of the customers with financial services. Additionally, the work seeks to validate a novel methodology that employs social media data to solve multi-criteria decision-making issues.

A class-based storage (CBS) policy in unit-load (UL) warehouses groups items considering their turnovers to form product classes which are allocated to the closest storage locations in order to minimize pick distance. An additional possibility of reduction in storage space is also considered in this model as a result of combining different stock keeping units (SKUs) in a class. Dynamic Programming Algorithms (DPA) are frequently used in warehousing research to solve the combinatorial problem of finding such SKU to class allocations and their respective class boundaries. This paper proposes a new generation/fathoming rule in the conventional DPA and demonstrates it with the help of a numerical example. The new DPA is much faster as compared to a conventional DPA published in literature and is sub-optimal by a very small margin as compared to optimal solutions.

The Capacitated and Time-Constrained Vehicle Routing Problem (CTCVRP) is regarded as a complex but essential, optimization mission in logistics and transportation systems. In this paper, we propose a novel approach to use deep reinforcement learning to solve the CTCVRP in an e-fulfilment center environment. Our approach aims to deal with both capacity and time constraints, ensuring optimal resource allocation and timely deliveries. Deep reinforcement learning algorithms are developed in Python environment to guide the learning agent towards optimal decisions while satisfying constraints. Experimental evaluations on benchmarking instances demonstrate the viability and effectiveness of our approach, surpassing state-of-the-art techniques in terms of solution quality and computational efficiency. The contributions of this work include a reinforcement learning formulation for CTCVRP, a deep reinforcement learning-based approach and experimental analysis. This research provides a scalable and adaptable solution for solving capacitated and time-constrained vehicle routing problems with high practicality in a real-life environment.

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.

The study addresses the Multi-Trip (MT)-Pickup and Delivery Problem (PDP) in a transportation network with one-to-many/many-to-one (1-M/M-1) connections. It focuses on efficiently managing transportation tasks using cargo planes and depots serving as both sources and destinations. The challenge involves optimizing routing decisions, determining node sequences for cargo planes in each trip, and minimizing the number of trips and fleet size to meet all requests promptly. Unlike traditional approaches, this study allows the splitting of loads among cargo planes and considers practical factors like multiple depots, trips, and operational restrictions. The proposed solution is a mixed-integer linear programming model (MILP), demonstrated through a real-world case study for practical insights.

Collision avoidance system’s design and configuration introduce operational delays, particularly in mines where several mobile machines and workers interact. Following the deployment of a radio-frequency identification collision avoidance system on underground loaders at a platinum mine in South Africa, production decreased by 13.28%. This research study focusses on determining the system’s impact on productivity, its constraints, ranging and detection accuracy. By triggering alarms and measuring activation distances for stop, crawl and caution mode the system was assessed on surface and underground in static and dynamic trials. Caution mode was the most accurate and crawl mode the least, rear direction was the safest and front was least safe, the system performed better underground than on surface. Metallic parts of the loader which were in line of measurement, caused tag detection failure in front of the bucket during surface trials, and distortion in distance estimation which influenced productivity.  Utilising Received Signal Strength technology rather than Return Time of Flight may improve the system’s accuracy because of its even magnetic field distribution in the presence of metallic objects.

The paper deals with the business approach for the software engineering domain. Software engineering is a combined with engineering and business perspectives. Although the domain of software engineering is broad, design and managing software development process are the major portion of the software engineering. This research aims to develop a strategy framework that can assist firms in selecting the most suitable software development process for their needs. The paper introduces the concept of the strategic spectrum, which proposes a range of software development options that align with the strategic fit of an organization. This research aims to develop a strategy framework that can assist firms in selecting the most suitable software development process for their needs. The strategic spectrum which is the proposition of the proper software development within the strategic fit has been introduced for help firms to design their own customized software development processes based on their capabilities and market needs.

This study investigates potential determinants for 'project team resilience' and 'project performance' during the pandemic in Indonesia's construction sector. A theoretical framework is developed, which involves four predictors: 'individual resource' (individual-level), 'team state' and 'team resources (team-level), and 'transformational leadership' (contextual). A quantitative, cross-sectional survey is performed, with a response rate of 46.7% (usable team-level datasets=70). The Partial Least Square (PLS) analysis provides compelling evidence of the crucial role of 'team resilience' on 'project team performance' (p-value<0.001) during a pandemic. Further, project' team resilience' is significantly associated with the two team-level variables ('team resource' (p-value=0.007), 'team state' (p-value<0.001)). Limited evidence suggests the role of 'transformative leadership' and 'individual resources' on 'team resilience.' Two cultural dimensions in Indonesia may influence the finding: 'collectivism' and 'high power distance.' The result may motivate Indonesian project practitioners to emphasize their resources for developing team-level capabilities and states which support team resilience to anticipate the next disruptive events.

Although work-related musculoskeletal disorders (WMSDs) have been a major concern in physically demanding industries, ergonomic risk assessment often lacks comprehensiveness in considering activity duration and an effective way to monitor industrial workers’ postures. Furthermore, they are not easily usable due to the cumbersome operational process when using biomechanical analysis software such as OpenSim and the complexity of estimating total lumbar compressive force. To address this issue, we present a method to estimate the total lumbar compressive force only with a monocular camera by applying a state-of-the-art 3D Human Pose Estimation algorithm and simplify the operational process with a ‘parameter a method’ for the estimation of total lumbar compressive force, which can be easily adjusted by a professional ergonomist. Results show that the estimated force and ergonomic injury risk fall within a reasonable range compared to the results obtained from the previous studies, where existing, complex biomechanical analysis was performed. This finding implies an enormous potential for enhancing the prevention of WMSDs by adopting the proposed method, which integrates technologies, simplifies the operational process, and enables comprehensive ergonomic risk assessment.

The space sector is unveiling unprecedented levels of potential value. Satellite services and applications provide benefits to an ever-expanding number of institutional and private users. Nevertheless, there exists a misalignment between the potential value offered by space infrastructures and the enacted value realized by users. The front-end of projects offers prime opportunity to collaboratively shape the project success with primary and secondary stakeholders, thereby bridging the gap between potential and enacted value.  Space project practices have not embraced yet such an evolution towards a wider stakeholder engagement perspective. Therefore, users’ needs are weakly addressed and included in the design of space projects. Through secondary data and the organization of a formal workshop gathering space organizations and users, we examine the factors determining potential and enacted value misalignment through a New Stakeholder Theory (NST) lens. To seek insights for solutions, we delve deep into project management studies to identify how similar challenges are addressed.

In response to the escalating complexity of modern products and services, this paper introduces a novel Smart Project Management System (SPMS) powered by multi-objective optimization techniques. The growing intricacy of these offerings has led to an exponential increase in the number, complexity, and potential solutions to errors, necessitating proactive support for all stakeholders involved in the development process. This research addresses the formidable challenge of managing a burgeoning volume of findings by leveraging clustering methods grounded in multiple criteria. Our proposed methodology integrates quality assurance reports to identify specific activities and employs a robust multicriteria decision-making approach to establish optimal execution sequences. Through the automation of task allocation and the incorporation of diverse criteria, SPMS significantly enhances quality management processes, improves operational efficiency, and provides invaluable support to development stakeholders. By replacing manual prioritization with algorithmic processing, SPMS generates optimal solutions that comprehensively consider all criteria and explicit decision-making factors. The integration of this Smart Project Management System offers a systematic, efficient means of addressing quality deficiencies and optimizing project outcomes, contributing to the advancement of modern project management.

This paper focuses on the management of accessibility requirements in web application development projects. First, it presents a map that integrates the methods for assessing web accessibility, the factors contributing to accessibility barriers, the consequences of accessibility barriers and the possible solutions for enhancing the accessibility of web applications. Second, it provides insights into industry practices related to every theme in that map and the working knowledge that can help improve the accessibility of web applications. Findings show that applying accessibility standards and using effective evaluation methods and tools help better manage accessibility requirements in web application development projects. Implications for practice are discussed.

The development of Web3 based on blockchain networks is advancing at a rapid pace. However, many of the actual Web3 applications are still dependent on the old Web 2 platform. Among them, distributed autonomous organizations (DAOs) have no mechanism in place to execute projects autonomously (i.e., implement them on smart contracts), even though their purpose is to be a decentralized organization that can execute projects without a specific representative.
This research will analyze the structure and challenges of existing organizations called DAOs with respect to the operational aspect in comparison to non-DAO companies. Then, while operating an environment that is similar to the current DAO, an experiment will be conducted using a development management method such as Waterfall, Agile, and Scrum, based on Tokenomics; and propose what kind of project progression can be considered for DAOs in the future. Lastly, this research proposes a new approach for working in DAOs. Contribution confirms the foundations of working in a DAO and suggests future directions for DAO labour force.

Nowadays, various industries are affected by several new trends in technology, market or even by disruptive changes. The automotive industry is affected as well, for example by autonomous driving. Furthermore, VUCA-factors aggravate the situation of occurring challenges and risks. These challenges demand highest flexibility, sustainability, and resilience in system development. This exploration considers state-of-the-art literature as well as results out of an empirical study applied to develop a process orientated method for risk and technical change management. The study is conducted in the automotive industry involving interdisciplinary participants, representing system development experiences, requirements, and status quo. Evaluated subjects are: VUCA, complexity, (re-) action, quality, and module interfaces of Generic Systems Engineering. Concluding, the publication provides a baseline of aspects to be considered, designing an iterative process model for risk and technical change management for interdisciplinary system development projects.

This study addresses the vehicle dispatch problem with the chassis repositioning to operate the container exchange terminal and the chassis pool facility effectively reflecting the situation in Japan. It is assumed that the vehicle move types are consisted of (i) tractor only, (ii) tractor and chassis, (iii) tractor, chassis and container. To find the feasible solutions, this study proposes the solution approach based on Simulated Annealing. To investigate the impact of handling time length by the external tractor spent from gate-in to gate-out. As the results, total travel distance with long handling time is longer than that with the other handling time. More tractors with long handling time are required than that with the other handling time.

France is one of the pioneer countries in the use of electric vehicles (EVs). The French government aims to complete the transition to EVs by 2040. Therefore, modeling related to the adoption of EVs is needed in order to determine the potential policies needed to achieve this goal. This modeling is based on a literature study to identify the factors and the causal relationship between those factors. The systematic literature review (SLR) analysis was performed on 20 journals selected based on the PRISMA framework. Five direct factors and four indirect factors were found through SLR analysis, and all of these factors were used as the basis for modeling. Four balancing (B) loops and three reinforcing (R) loops were obtained based on the model developed. From the analysis, it was found that the advertising factor has a goal seeking structure, while the word of mouth, environmentally friendly image, and total cost of ownership factors have an S-shaped structure.

Businesses must comply with numerous Product Compliance requirements to sell on global markets, and these requirements must be met throughout the entire supply chain. Since their management involves critical suppliers, firms must be able to assess the risks associated with noncompliance to support their supplier selection and segmentation phases. However, literature on Product Compliance Risk Assessment along the buyer-supplier relationship is still scanty and organizations find limited support in improving their Product Compliance Risk Management practices, with potential negative consequences. To address this gap, we propose a hybrid methodology that integrates Product Compliance Risk criteria into Supplier Risk Assessment. We identified relevant Product Compliance criteria via literature review and practitioners’ interviews, to assign weights by means of Analytic Hierarchy Process (AHP), and to assess supplier risk via Fuzzy TOPSIS. The effectiveness of the methodology was tested in a global manufacturing company for construction tools. Results contribute to expanding the existing literature and offer a valuable support tool to practitioners.

In recent years, market competition is gradually changing from interactions of enterprises to that of competing supply chains. And this chain-to-chain competition raises a new problem: how to internally coordinate one chain to cope with the competition of another chain. To address this problem the paper investigates two pricing strategies: the wholesale pricing strategy and the two-part tariff, and compares the performance of the two strategies by employment of Nash bargaining model. The paper obtains new insights as follows: First, previous studies based on single-chain argue that two-part tariff can better coordinate the supply chain compared to wholesale pricing strategy. This is not always the case in chain-to-chain competition: two-part tariff is better only when competition intensity is relatively low. In contrast, wholesale pricing strategy is more adaptive when competition exceeds a certain threshold. Second, the choice of pricing strategy is also related to the power structure of supply chain. As the bargaining power of retailers increases, the incentive to implement a wholesale pricing strategy is increasing and the two-part tariff is decreasing.

This paper studies the inventory control strategy for equipment spare parts with huge shortage losses and rolling demand forecasts. A dynamic  strategy considering ordering costs and the turnover rate is proposed to reduce costs and improve the turnover rate while meeting a certain fill rate. This strategy constructs models under two scenarios where the lead time is shorter or longer than the planning horizon. Improvements by using safety stock and safety time have been introduced based on the models to deal with demand fluctuation. The effect of the strategies is verified by the real data of Wuhan Cigarette Factory which belongs to China Tobacco Hubei. The case proves that compared with the enterprise’s current strategy and  strategy, improved dynamic  strategy has advantages. Numerical results also indicate that improvement by safety time performs better when the prediction error of demand is relatively small, otherwise improvement by safety stock is more stable in terms of the fill rate.

Customers receive services that require a lot of labor from warehouse. High cost and unmet demand from customers could be the outcome of underperformance. In order to handle this, order picking procedures must be streamlined by finding solutions to a variety of planning issues. A bad overall warehouse performance may result from progressively optimizing order picking planning challenges. This literature review is investigating combinations of various order picking planning issues and their impact on picker blocking that affects the length of time it takes for pickers to complete a customer order. To automate the search for items in storage in warehouses, IOT-based technologies like RFID can be used in order picking planning. RFID can improve traceability of products. Application of this technologies can support development of effective order-picking systems and enhance customer service by finding the best technological and policy combinations.

Supply chains are facing disruptions in succession, and recovery remains a challenge. Disruptions challenge supply chain managers to find solutions for a faster recovery. However, building supply chain resiliency may lead to foregoing some globalization cost-benefits. While professionals and academicians research this conundrum, it's evident that reactive approaches do not support sustenance and present a unique challenge with each disruption. Therefore, it becomes significant to predict risk probabilities and severity and act to mitigate the risks strategically. It also calls for timely decision-making. This paper identifies the need for a proactive approach to predicting risks and detecting trigger points for well-timed decision-making. The paper recommends the existing frameworks of Failure Mode and Effects Analysis (FMEA) Risk Priority Number (RPN), Uppsala model, and Multicriteria Decision Making (MDM) for quantifying and reducing the risk and improving resiliency. The FMEA model helps assess and prioritize risks, while the Uppsala model guides commitment based on changes in risk. MDM acknowledges that other criteria may also be important in strategic decision-making beyond just risk.

The Hybrid Plug in is an electrical device that aims to provide a lifestyle that can achieve comfort, affordability, and conservatism at the same time through innovative technology. The developed technology is an energy monitoring that can manage and monitor the amount of electricity being generated in real time, set a restriction on the amount of time spent and can set a limit on the amount of money to be spent. It can be managed through a dedicated application. The device is composed of a current and voltage sensor that allows it to perform the needed functionality. The device's primary operating system is an Arduino Nano, which also acts as the microcontroller for the entire system. The product also goes through the process of receiving legal authorization and validation from the appropriate government agency. The result of the study suggests that it does not bring about any potentially dangerous circumstances and will contribute to the achievement of goals for sustainable development. This technology is expected to be among those that will modify both the world and how people use electricity.

In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.

This paper presents a real-time action recognition system for cleanroom standard operating procedures (SOPs). The objective is to develop a lightweight and efficient system capable of recognizing multiple actions and detecting individuals who deviate from the SOP. The proposed method utilizes a 3D Convolutional Neural Network for action classification. It employs object detection and tracking algorithms to focus on individuals performing the SOP. The proposed method can handle multi-object action recognition by incorporating object detection and tracking. The technique is designed to run in real-time on standard computers without hardware accelerators. Experimental results demonstrate that the proposed method achieves similar accuracy to MoViNets but with faster training and prediction times. Furthermore, the technique effectively handles multi-object action recognition and identifies individuals who skip parts of the SOP. The average prediction time of 0.03 seconds outperforms MoViNets’ average prediction time of 0.05 seconds.

To achieve Digital Transformation, companies are required to create new value and deploy solutions by using multi-field data, not just data from one domain. In recent years, data sharing platforms such as FIWARE and GAIA-X are being developed to integrate and manage multi-field data stored in various locations. However, while these platforms provide everything from data storage to data provision, we need to implement data-to-data association and data linkage processing through programming for building services after extracting data from these platforms. Therefore, in this paper, focusing on building services and applications, we propose a data model that can define data-to-data association and linkage processing. The proposed model can be stored and managed by Graph Database, allowing retrieval and extraction of data across multiple fields. We also developed an application that lists related data on a map using the proposed model.

Usability plays an essential role in user-centric design, ensuring that systems, products, and interfaces meet the needs and expectations of the intended users. The concept of usability includes many dimensions and attributes that must be thoroughly understood and carefully considered during the design and evaluation process of the halal traceability system. This paper presents a comprehensive literature review that aims to identify and define the most relevant usability attributes in contemporary research and practice by synthesizing and analyzing various scientific articles on Scopus. This research found 111 articles published from 2013 to 2023. Then, based on 111 articles, this research can identify and analyze 40 attributes, and nine out of 40 attributes are selected as top usability attributes, namely efficiency, satisfaction, effectiveness, learnability, errors, memorability, consistency, accessibility, and aesthetics. At least 20% of publications used those attributes.

Dialogue translation, typically reliant on sentence-level translation models, often struggles with accurately capturing contextual relationships and cross-sentence semantics. To address this, we took inspiration from document-level translation models and propose a Transformer architecture with a multi-block encoder, equipped with our novel context aggregation method. The applicability and effectiveness of these proposals were tested across three chat translation datasets using automated evaluation metrics. Notably, the integration of the context aggregation method improved the baseline model performance, while the Transformer with Multi-block Encoder demonstrated substantial gains in particular datasets (BLEU, METEOR). Moreover, our model and method displayed versatility, adapting effectively to various chat scenarios. These findings affirm the potential of the Transformer with Multi-block Encoder and the context aggregation method in enhancing dialogue translation by ensuring greater context sensitivity and adaptability.

The preciseness of milling or drilling processes heavily relies on proper fixture system. In recent years, there is a noticeable upward trend towards loading robots in milling operations. For an automated end-to-end process from raw material to finished part, an automated fixture planning process is necessary. The automation on the shopfloor is increasing, whereas there is no known tool for an automated fixture planning. The current lack of tools for planning the fixture system hinders the full potential of utilizing loading robots in milling operations with small batch sizes. To address this challenge, a faster and effortless fixture planning method needs to be established. This research paper aims to evaluate existing methods that could be integrated into the established CAD/CAM/CNC chain. Through a systematic literature review, five research questions were proposed to explore the feasibility of integrating such methods. The findings indicate that the established approaches have limitations in selecting appropriate clamping devices to create an optimal fixture system. Nonetheless, significant progress has been achieved in evaluating and optimize fixture systems through simulation.

The Portuguese industrial culture is going through some challenges and difficulties in the new phase of digitalization, using the development of several technologies that provide digital solutions aligned with Industry 4.0 (I4.0). To evaluate the digital readiness for I4.0 of industries companies, namely small, medium and large companies, a IMPULS model was used, which allowed quantifying and qualifying their level of readiness to implement I4.0 technologies, considering different dimensions and sub-dimensions. It is found that not all companies have the same pace and facility in adopting and implementing these technologies, where business strategies are not integrated with I4.0, resulting in the absence of an accurate self-assessment on the real maturity level achieved. Therefore, it is of great importance to understand how companies are facing different challenges and difficulties in the digital transition. This work aimed to assess the level of digital readiness to I4.0 of manufacturing companies in Portugal, in global and dimensional terms for each region. The overall assessment level is low (beginner) and the Smart Infrastructure and Data-driven Services dimensions are weak (outsider).

Uncertainties in our energy system, such as consumption and renewables, pose a challenge to system-wide planning. Deterministic approaches are thus insufficient for making techno-economically optimal decisions for the type, scale, and operations of energy supply and storage facilities. This work adopts a stochastic approach and develops a general multi-period optimization model of an energy system consisting of renewable and non-renewable energy supply sources, storage facilities, and uncertain parameters such as energy demand. It uses multi-stage stochastic programming to address multiple probabilistic scenarios for the stochastic parameters modeled as scenario trees. The model evaluates all the scenarios including the deterministic case and prescribes minimum cost options for the energy supply and storage capacities, and multi-period supply allocations to meet energy demand over a time horizon. The model enables assessment of the potential impacts of variations in demand on energy supply and storage costs and plans as demonstrated by the results of the numerical cases.

The demand for air travel is gradually returning to pre-pandemic levels. With the sudden influx in passengers, complaints are filed because of the inefficiencies that happen. One of them is the inconvenience in assigned boarding gate assignments. Having mentioned the point of view of the passengers, it is also important to consider the point-of-view of the operator as well. Recuperating from the poor economic performance during from the pandemic, the costs that would be incurred by the operator must be considered. In this study, a passenger-centric and operator-centric multi-objective integer linear programming model was constructed, minimizing total walking distance for the passengers and minimizing total turnaround costs for the operator. In order to optimize the system, goal programming was used for the multi-objective optimization model. The numerical results of the multi-objective optimization model were then compared to the results of both objectives optimized separately.

The curse of dimensionality often poses challenges for combinatorial optimization techniques. Therefore, it is crucial to employ an effective technique that can efficiently reduce the search space and enhance the performance of the optimization algorithm. This study proposes a modified selection heuristic approach for subnetwork selection in the aircraft schedule recovery problem. This algorithm optimizes the allocation of flights to the selected aircraft subset. To evaluate its effectiveness, we compare it with the optimization of the entire network of a major airline company. The performance of the selection heuristic is tested on 39 aircraft, 147 flights, and 42 airports. The results demonstrate a significant improvement in the overall runtime of the optimization algorithm while giving the optimal solutions in most cases.

Developing waste-to-energy (WTE) projects is an important way to achieve sustainable development. Due to the high capital expenditure, WTE projects are jointly constructed by the government agencies and private investors under the formation of public-private partnership. Private investors usually participate in a bidding process, which quote the price for disposing of waste. Government agencies choose the lowest bidder among all the qualified private investors. The bidding price has become a key factor for qualified private investors to obtain project opportunities. Hence, how to determine the optimal bidding price becomes a critical task for private investors. This article adopts option game theory to establish an investment value model to estimate the bidding price for investors. The least squares Monte Carlo simulation has been applied to solve the model. The proposed model is further applied to a WTE project based on incineration technology located in Shaanxi, China. Results show that determining the optimal bidding price requires balancing both risk-hedging capability and winning probability. At the same time, when there are many bidders involved, it can lead to a vicious competition among them. Results provide guidance for private investors and promote the development of WTE projects.

Remanufacturing is widely recognized as an effective strategy to address the negative environmental impacts of product disposal and minimize costs across the entire value chain. Short life cycle product like smartphone manufacturers and their e-commerce partners are offering tempting incentives to exchange obsolete handsets to gain market share. This increase in exchange programs has created issues about returns management, notably remanufacturing and disposal regulations. While we realize new product demand, we cannot guarantee returning item quality or quantity. Due to rapid technology changes that make components obsolete in 2-3 years, anticipating spare part needs is harder. An effective remanufacturing policy should replenish a percentage of spare parts inventory through returns recovery to solve these problems and optimize inventory levels. We propose two steps. First, we use Bayesian Estimation to predict returns and spare parts. This reduces production risk. The return quality function determines the spare part manufacturing curve. This two-step technique reduces production uncertainty and optimizes inventory. This study concludes with a comprehensive approach to smartphone returns management, remanufacturing, and spare parts inventories. Numerical examples show how our approach works.

Multimodal optimization aims to find multiple optimal or near-optimal solutions in solving a single-objective optimization problem. In this paper we propose a metaheuristic framework, which utilizes several niching methods including speciation, crowding, and clearing to keep population diversity and search multiple areas in the solution space in parallel. It also uses an archive to store inferior solutions to refresh the population to explore promising areas. The performance of the proposed framework is verified by comparing it with four existing algorithms using the CEC2013 benchmark. The results confirm the positive effects of the proposed ideas and show that our framework provides competitive search ability.

This paper discusses the development of an optimization model involving four echelons: donor, blood center, blood mobile and hospital. The blood collection can be done at the blood center and the blood mobile. The blood mobile should determine its optimal routing in order to collect the blood from each city to minimize the total relevant cost. All collected blood are pooled in blood center which then deliver the blood to the hospitals in satisfying the demand. To assist the distribution of blood from the blood mobile to the blood center, this article also discusses the usage of shuttle and blood mobile that can be utilized on a daily basis. This research aim to reduce blood supply chain cost by develops a mix integer linier programming. The model also considers the blood shortage of blood in the hospital due to unsatisfied demand.

The digital transformation is fundamentally disrupting established business models and existing value chains at an accelerating pace. Faced with multidimensional and inevitable changes, many incumbent companies lack necessary competences, processes, and structures to actively transform their business with sufficient speed and extent. While successful tech-companies therefore consequently rely on startup acquisitions, investments or cooperations, most incumbent manufacturing companies are not successful in systematically leveraging external corporate venturing as a catalyst for their digital transformation. Several reasons may already be found in the early phases of an investment. This applies especially to the identification of suitable startups, the determination of the potential value contribution of an investment, and the effective initiation of startup investments.Against this background, this paper presents a concept for effectively identifying and initiating external corporate venturing initiatives for the digital transformation of manufacturing companies. Thus, existing approaches in literature are discussed and analyzed to derive the requirements for developing a concept, which enables effectively identifying suitable startups for digital transformation objectives and subsequently initiating an investment. Based on these findings, the methodology and its sub-models are derived.

Digital platforms have made their way to the mainstream state-of-the-art of many disciplines, propelled by their adoption across multiple industries. In the case of digital industrial platforms, the peculiarities of the industrial environments emphasize the iterative dynamics of cooperation and competition with complementors. By adopting a sociotechnical perspective that focuses on the interplay between platform owners and complementors, we explore how boundaries between complementors, and platform owners impact the transformation and evolution of platforms. We further conceptualize how the different phases of a digital industrial platform lifecycle follow recurring novelty cycles and how these are influenced by the alternance of collaborative and competitive boundary work with complementors. Leveraging this conceptualization provides a perspective on ecosystem governance focused on platform evolution. We use this conceptualization to explore how key performance indicators from a boundary object perspective serve to understand the need for new novelty cycles and guide the new functionalities that should be targeted. Finally, future avenues for research based on this conceptualization are suggested.

In this paper, we discuss a situation of enterprise software upgrade that is common in real life. We started with a simplistic model with one software vendor and then multiple software vendors. This model led to an optimal interval time for upgrades that resembles the optimal time in Economic Order Quantity. A more realistic model with discrete time was proposed by adopting MicroStrategy case in releasing their newer software, namely one major upgrade followed by 3 minor upgrades in a year. We proved that the discrete cost function is convex. From an analysis of several numerical examples, we found very interesting and a bit counter intuitive observation.

The word "value" is used a lot. Particularly, Toyota Production System is used to eliminate waste and create valuable products. Among them are value analysis tools such as Value Stream Mapping. Its main approach is the reduction of waste, and the reduction of waste from the whole is regarded as value. In other words, the focus is on the waste, not value. This paper reports on a project in progress. In this project, we are developing "Value Karuta", which is an application of traditional Japanese card game Karuta. The purpose of this article is to find the elements that make this game effective as a tool for understanding value. The research method is a PDCA cycle suitable for improving products. We are planning and implementing this game for two groups, college students and business persons. We also conducted a questionnaire to the participants to confirm and evaluate this game. From the results, we have confirmed three elements of VK that promote understanding of value: (1) nostalgia and design, (2) naming and the type of Japanese, and (3) implementation environment.

Intelligent pipeline network technology holds significant importance for the future development of  energy industry. However, as it is in the early stage of development, the technology trajectory shows very high uncertainty. To effectively deal with that, this paper extracts patents related to intelligent pipeline network technology from the IncoPat patent database and uses methods of patent mapping and main path analysis to analyze the overall trends and evolutionary development of this specific technologic field. The findings are as follows: (1) Intelligent pipeline network technology has entered a stage of rapid development recently, with the high growth in patent volume primarily driven by the Chinese market. (2) Intelligent pipeline network primarily focuses on data processing and utilization, oil and gas extraction and risk monitoring, intelligent decision-making calculations and dynamic simulation of pipeline data,  covering a wide range of technical areas. (3) The knowledge flow between patents is not closely interconnected, and the overall technological structure is relatively loose. The results illustrate the main areas involved in the development of intelligent pipeline network technology, and find that the development trajectory of this technology is not fully clear. All these suggest firms and research institutions should carry out a comprehensive layout according to their own advantages and prepare for the multiple possibilities of the development of intelligent pipeline network technology.

This paper explores the negative consequences that can arise in public organizations due to the misuse of performance measurement, resulting in unintended dysfunctional effects in practice. It highlights the importance of conducting research to assist public organizations in selecting appropriate performance measures that align with the core purpose of the public organization. To address this need, a three-step conceptual model is proposed, emphasizing a system thinking approach over traditional control and command thinking. By adopting this model, public organizations can better navigate the landscape of performance measures to avoid negative of dysfunctional effects that arise from a micro-management focus in practice.

The sustainable agriculture enterprises of the 21st century take into account the interplay between Industry 4.0 (I4.0) technology and the principles of circular economy (CE). While the principles of CE have indeed brought more attention to environmental difficulties than economic ones, its present stage has also given rise to social concerns. At the macro-level, it is essential to establish limitations on the design, operation, and control of forthcoming manufacturing, logistics, and supply chain systems in order to uphold considerations of economic, environmental, and social factors. At the micro-level, people and communities must engage in acts such as refusing and rethinking, rather than relying only on changes in corporate principles, in order to address behaviors, preferences, and related factors. Both levels of responsibility contribute to the advancement of a cohesive industry and society towards a new I4.0-CE mandate that is closely linked with sustainable development considerations. This work discusses how I4.0 technologies may serve as a basis for so-called precision agriculture (PA), and hence how they might be used to further CE efforts in the agricultural sector. The objectives of this study are as follows: (a) to examine the mutually beneficial relationship between PA and CE; (b) to investigate the understanding of the potential benefits of PA technologies in the context of the regenerate, share, optimize, loop, virtualized, and exchange (ReSOLVE) models; (c) to propose an outlook roadmap for future research that integrates principles of PA and CE, drawing on theories of green supply chain management.

Emerging technologies are bringing many capabilities that can transform business and governance. Blockchain is one of the most talked technologies as it comes with unique capabilities like immutability, traceability, and decentralization. Blockchain brings trust and transparency in the system. Blockchain has many applications like cryptocurrencies, smart contracts, NFTs etc. still, the implementation models are not well developed. The deployments of these capabilities in wider application domains such as Governance and Business functions is still nascent. Governance requires trust and transparency as key enablers, especially in developing economies where governance functions are not very efficient. Blockchain-based solutions may help in these scenarios through relevant implementation models. In this work, Blockchain based model is explored to identify the possible use cases for the governance in the context of developing economies. As a part of the work, I will explore the inductive approach as well as the case study research method to come with the implementation model with primary focus on emerging economies. This work may add value in the context of utilizing Blockchain capabilities for e-Governance.

A reliable and cost-effective public transportation system plays an essential role in many cities. Understanding the crowd density for public transport is crucial for smart city and urban planner. The main objective of this paper is to monitor and predict the crowdedness level on the Mass Rapid Transit (MRT) platforms in Singapore. Firstly, we design and implement a scalable big data framework to support this task. Secondly, in order to address the issue of class imbalance, Synthetic Minority Over-Sampling Technique (SMOTE) is integrated with a balanced random forest classifier to predict the crowdedness level of station platforms. Extensive experiments are conducted to evaluate the performance of the proposed approach on real datasets from Singapore Land Transport Authority (LTA) DataMall API. The results demonstrate the accuracy and efficiency of the proposed approach.

With the development of the information age, cities provide a large amount of data that can be analyzed and utilized to facilitate the decision-making process. Urban big data and analytics are particularly valuable in the analysis of business location decisions, providing insight and supporting informed choices. By examining data relating to commercial locations, it becomes possible to analyze various spatial characteristics and derive the feasibility of different locations. This analytical approach contributes to effective decision-making and the formulation of robust location strategies. To illustrate this, the study focuses on Starbucks cafes in the Tianhe District of Guangzhou City, China. Utilizing data visualization maps, the spatial distribution characteristics and influencing factors of Starbucks locations are analyzed. By examining the geographical coordinates of Starbucks, main distribution characteristics are identified. Through this analysis, it explores the factors influencing the spatial layout of commercial store locations, using Starbucks as a case study. The findings offer valuable insights into the management of industrial layout and the location strategies of commercial businesses in urban environments, opening avenues for further research and development in this field.

Tropospheric ozone (O₃) is a secondary pollutant generated from the photochemical reactions of two pollutants: nitrogen oxides and volatile organic compounds. O₃ higher concentration above the earth's surface harms human health and ecosystems, which urges the need to build a robust model that accurately forecasts pollutant concentration to support decision-makers in mitigating its adverse effects. In this study, we compare the performance of four state-of-the-art deep learning models for temporal data to forecast pollutant future concentration using five air pollution stations that exhibit different environmental assessment points. Overall, the LSTM and Transformer-based models outperform other models. The Transformer model reported a lowest RMSE of 0.25 in South Habshan. At the same time, LSTM reported the best performance for the Ruwais station with RMSE 0.47. Incorporating deep learning techniques can significantly enhance the prediction of ozone concentration. We also have observed that the temporal characteristic of the pollutant can impact the model's performance. The Transformer-based model excels when the pollutant sequence has great diversity. In contrast, LSTM stands out with a lower variation sequence.

This study proposes a method to solve the problem of predicting the coating film thickness of the workpiece, which is called the multi-region segmentation model. First, four types of sensing data contribute 125 features, including Clean, Oven, Painting, and Environment. Data preprocessing covers several aspects, such as missing values, outliers, and scales. Then, the key features are extracted and given to the machine learning algorithm to build a model, verify, and test. In addition, the multi-region segmentation model is the main idea and aims to reduce the model from falling into the trap of overfitting when modeling. At the same time, the classifier guides the test data to a more suitable regressor. The experimental results show that the multi-region segmentation model based on the Pearson correlation coefficient has obtained a relatively ideal performance. The RMSE is 15.3724. This is superior to the results submitted for IMBD Competition 2022 and the official standards. In future research work, we will devote ourselves to strengthening the strategy of data pre-processing, and it is expected to improve the model's error.

Automated handling of deformable linear objects (DLO), specifically wires, is challenging due to their physical and geometric properties. Manufacturing companies handling wires mainly rely on manual work with operators to produce wiring systems. To address the research gap for automated wire handling solutions, this article addresses the human-robot co-manipulation of DLO and focuses on robot hearing for process monitoring. The focus is on the handling task of DLO insertion. Robot hearing in the manufacturing environment was realized through an acoustic sensor and sound-event classification. The paper outlines the collaborative robot system and data processing pipeline for audio data classification. The goal is to assess the process of DLO insertion regarding success and failure. The experiments were conducted for an automotive use case. Data collection, pre-processing, and processing are presented and experiments were conducted to evaluate the proposed solution. The experimental findings show that high accuracies for sound-event classification can be achieved and enable reliable and monitored robotic DLO handling.

Predicting the real-time energy consumption of battery-operated electric vehicles (BEVs) remains critical in identifying energy-efficient routes and charging stations. However, accurately predicting energy consumption depends on various environmental factors, such as wind speed, wind direction, temperature, humidity, and precipitation. Although the existing time series (TS) models offer valuable insights into energy consumption prediction and data trends, they need to generate more accurate outcomes, especially with real-time data. Moreover, obtaining com- prehensive and accurate data pertaining to BEV energy consumption poses significant challenges. The scarcity of data can hinder the progress of research in this area. Therefore, this study aims to identify an efficient prediction model by comparing it with existing models, which will contribute to developing more accurate and efficient prediction models for BEV power consumption. We selected particular environmental factors that were expected to impact BEV energy consumption. These findings also involved acquiring BEV energy consumption data and integrating it with various environmental parameters.

The main purpose of this study is to integrate the literature and build relationship between organization strategy, different types of human resource analytics (HRA), enterprise social media (ESM), culture and other organizational variables while performing HR activities like performance appraisal in highly uncertain firms like Differentiators. There is a paucity of literature discussing the possible benefits of adopting ESM in the workplace and role of HRA in enhancing HR practices like recruitment and selection of employees in strategic firms like cost leaders (CL) and differentiators (DIFF) at different management levels, with or without use of ESM. Based on the above relationships, we formulate hypotheses in order to provide organizations with a comprehensive view that adoption of ESM have several benefits in the workplace if handled properly like fairness during appraisals. Secondly, the types of HRA used is dependent on the organization strategy and ESM.

The field of autonomous mobile robots has been gaining significant attention in various industries and research domains. As the future of robotic process automation unfolds, there is an increasing demand for precise robot movement in terms of collision avoidance and trajectory planning. This paper presents a camera-based autonomous mobile robot system that addresses these requirements. The proposed system utilizes a deep learning variational autoencoder with a spatio-temporal model for image analysis processing. This approach enables the system to effectively analyze and understand the visual information. By leveraging deep learning techniques, the system can extract meaningful features and representations from the images, facilitating accurate perception and understanding of the robot's surroundings. This paper contributes to the advancement of autonomous mobile robot systems by proposing a deep learning techniques with reinforcement learning algorithms. The approach offers promising possibilities for enhancing the control and interaction capabilities of mobile robots in real-world scenarios.

Smart and resilient cities are currently hot topics in the academic field and community. Smart cities mainly use technology to maximize the use of resources to provide more convenient services for city residents and enhance life happiness. The resilient city, on the other hand, refers more to the city's resilience against unexpected events and ability to resist strikes. Also, resilient cities possess sufficient technology and resources. To explore the deep relationship between smart and resilient cities, this study will use a system dynamics model (SD) to illustrate the components of smart and resilient cities. Also, this is a preliminary study using SD to analyze smart and resilient cities. Finally, the analysis based on the model concludes that the resilient city contains some of the properties of the smart city to some extent. It also suggests future research directions for the smart city as a milestone in building a resilient city.

In this paper, we develop a modified SIQRS (susceptible-infected-quarantined-recovered-susceptible) compartmental model of infectious diseases based on the mean field theory of heterogeneous networks to analyze the effect of time-delayed quarantine measures on the transmission of infectious diseases. First, considering the nonlinear infection rate with social network structure, this paper establishes a modified SIQRS compartmental model on heterogeneous network, and introduces two time-delayed parameters into the corresponding ordinary differential equation model. Then, the basic reproduction number  of this model is obtained by regeneration matrix method, and the threshold conditions of infection outbreak are analyzed. Then, the disease-free equilibrium point (DFE) and the endemic disease equilibrium point (EDE) are obtained when the system is stable under different conditions, and the stability analysis of these two points is performed. Finally, the transmission mechanism of infectious diseases in reality is analyzed by numerical simulation, and the corresponding prevention and control measures are proposed.

Although discrete-event simulation (DES) can successfully support the clarification of various issues in manufacturing, it is also subject to some limitations in practical applications. With the help of artificial intelligence (AI) some of these limitations may be overcome. The aim of this paper is to give a systematic overview of method combinations of DES and AI implemented in the context of manufacturing planning and control. For this purpose, a systematic literature review was conducted. The evaluation shows that there are five different approaches to combine DES and AI methods. On the one hand, DES can be used to test or train AI systems. On the other hand, AI is used to control, optimize, and analyze DES models of manufacturing systems. These combinations have been used, for example, to solve planning, decision-making, and assignment problems. The approaches found were analyzed and systematized in terms of the decision problems considered, the type of combination and the AI methods used. The results provide a basis for deciding which approaches can be applied best to a planning problem in the context of manufacturing.

Industrial robots have been actively introduced in various fields such as food, cosmetics, and pharmaceuticals, in addition to the automotive and electronics industries. Currently, the introduction of industrial robots requires instruction by skilled personnel. Therefore, there is a need to understand the entire system and detect errors so that not only skilled operators can easily instruct robots. In this study, we propose a motion planning method based on data-driven optimization of industrial robots using Petri nets, which are a discrete event system based on robot teaching data by skilled operators. Petri nets are automatically generated from the robot's event logs using an -algorithm. Using a Petri net simulator, we verify the consistency of automatically generated Petri nets and output the optimal firing sequence. The motion program is applied to a 6-axis robot arm (VS-060) on the robot operating system (ROS) to verify the effectiveness of motion planning optimization in multiple problem settings, including pick-and-place motion from multiple posture candidates. Experimental results show that our proposed method can reduce the sum of angle changes approximately 3% compared with conventional motion planning method using RRT using different initial postures.

Antimicrobial resistance (AMR) has become an emerging and global threat to public health, with significant implications for human health and environmental sustainability. Accurate monitoring of the abundance of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB) in the natural aquatic environment is challenging. Meanwhile, traditional methods for detecting ARGs and ARB in water via field sampling and testing are complex, time-consuming, and economically expensive. These facts warrant the development of prediction tools to meet the needs of water resource managers for timely reporting the pollution level of AMR. However, the abundance predictions of ARGs and ARB have been open questions worldwide. In this study, through innovatively sharing the common information of multiple ARGs and different ARB, we built a multi-task least-squares support vector regression model (MTLS-SVR) to predict the co-abundance of ARGs and ARB in the natural aquatic environment. Compared with other models, the MTLS-SVR model presented better performance for the co-abundance prediction. The introduction of this type of modeling provides practitioners with a new perspective for predicting the co-abundance of ARGs and ARB, which allows managers to keep abreast of the status of AMR and provides decision support for policy-makers.

Cuenca's agroecological MSMEs are the primary income source for those living in the city's rural areas. The research aims to analyze the forgotten effects of the direct or indirect effects on the development of the agricultural sector. The applied methodology is the hypothetical-deductive, quantitative method combined with the Matlab software which will help to find the main incidents through the convolution. The main results indicate a lack of technical assistance and limited Internet access, considerably impairing the progress of these organizations, followed by policies to help the agricultural sector. Since it limits the production of agroecological food as well as the development of the capacities of agricultural producers, unforeseen events such as health emergencies directly affect farmers' primary source of income, which must be analyzed for correct decision-making.

This paper studies a digitalization of e-methanol supply and production planning Power-To-Methanol pathways is simulated using a steady-state simulation of biogas-to-methanol conversion to feed information to an agent-based simulation model.  The agent-based simulation model consists of autonomous agents of biogas, hydrogen, and methanol producers as well as distributors. The outputs of the simulation model are CO₂ and H₂ inventory policy, optimum methanol inventory allocation by transporters. Power-To-Methanol is a potential technology to lower CO₂ emissions, with viable economic benefit, and less sensitive to biomass and electricity price fluctuations. This paper contributes to simulation modeling application by proposing a new way of combining multi-method simulation to solve real life supply chain planning.

Southern African rail operators are under pressure to meet rising demand because of globalization and rapid technological advancement. This study examined the effects of South African railway company contract cancellation with locomotive suppliers. The study relied on the case study and secondary data to meet the research objectives. The total number of locomotives in the fleet that was the subject of the investigation was 83, and the model involved making transitions between active or in-service to the depot for maintenance and in-service to the factory for repairs. According to the Markov model, the number of locomotives in service was expected to decline and stabilize at approximately 59%, meaning a bigger proportion would be unavailable for service. The locomotives sent to the factory for repairs were expected to grow exponentially and stay at approximately 17% of the fleet, while those at the depot were expected to stay at approximately 23%. This research showed that companies should reduce failure and improve repair rates before cancelling contracts or developing internal capabilities to ensure technology performs as expected without supplier support.

With the rapid development of science and technology, the structure and states of systems have become more and more complicated. Multi-state systems have widely been used in practical engineering, including aviation, aerospace, maritime, transportation and mining industries. To complete the complex required tasks, these multi systems need to keep the higher reliability, so it is important to identify the weakest link of multi-state systems. This paper proposes a weakness analysis method based on integrated importance measure (IIM) for multi-states. To better evaluate IIM, the universal generating function (UGF) method is introduced to analyze the reliability of multi-state hybrid systems. Taking the height adjustment hydraulic system of the electric traction shearer as an example, the critical states and the critical component can be determined by IIM. The performance is verified by comparing with classical multi-state importance measures, Griffith Important Measure (GIM) and Birnbaum Important Measure (BIM). The ranking accuracy is obtained by mean average precision (mAP), which shows that the ranking accuracy of IIM is 97.33%, which is 13.12% higher than that of GIM and 3.86% higher than that of BIM.

The signals of different modes often contain different information and reflect different aspects of the detected system. In the fault diagnosis of rolling bearings, the feature fusion of multimodal signals can make the diagnosis result more accurate and more robust. Therefore, the vibration signal and acoustic signal of the rolling bearing are adopted, and the 8-dimensional energy features of the two signals are extracted respectively by the wavelet packet transform (WPT) method. Then the features of the two modes are fused, and the fused feature vector is input to the K-nearest neighbors (KNN) classifier for fault classification. Experimental results show that the proposed method is superior to the single-mode signal fault diagnosis method, which shows the effectiveness and superiority of multimodal feature fusion.

This paper proposes a joint replacement and spare ordering policy, which utilizes prognostic information to update the adaptive decision of when to order spare parts and how long the repair is postponed after triggering the maintenance decision. A nonlinear Wiener process with randomness is established to characterize the degradation trend, along with updating online parameters under Bayesian framework at each inspection point. Unlike traditional discrete models, this strategy optimizes both ordering and maintenance, relying on a comprehensive cost rate indicator. Furthermore, based on residual useful life (RUL), this model adopts predictive maintenance to avoid resource waste of scheduled maintenance. Additionally, due to the timely maintenance accompanying monitoring reduces the availability of logistics resources, this model adopts a delay interval determined by RUL's expectation and a delay coefficient, and then which is optimized through order time and delay coefficient. Ultimately, the applicability of the proposed policy is verified by the actual case study of high-speed train bearings.

Some engineering systems are supported by protective devices to mitigate system failure risks and extend system lifetime. Nevertheless, existing research on the reliability of systems with protective devices has some limitations regarding the system composition, shock impact mechanisms, protection mechanisms and triggering mechanisms of the protective device. To fulfill these research gaps, this paper proposes a reliability model for a two-dimensional voting system consisting of n subsystems with multi-state protective devices in a shock environment. The components in such a system degrade gradually under a novel mixed δ-shock model. Each subsystem is supported by a protective device responsible for isolating failed components in the subsystem to ensure the stable operation of the entire system. The triggering failure of the protective device is considered and a maximum number of triggering attempts is preset for the protective device in each working state. Probabilistic indices of system reliability and the device performance are obtained by using the finite Markov chain imbedding approach and universal generating function technique. Ultimately, a case study is presented to demonstrate the applicability of the proposed model.

With the advent of smart factory technology, data-driven condition-based maintenance (CBM) has been developed to automate the control of the production process in the engineering field. CBM primarily focuses on diagnosing the production status using real-time sensor data. In general manufacturing, production equipment's performance gradually declines over time due to wear and deterioration. In this paper, we propose an image degradation-based condition monitoring scheme called as change-point spatio-temporal process (CP-STP). To describe the deteriorating patterns of image observation, degradation based on spatial and temporal relationship is conducted. Simultaneously, it estimates change-points to differentiate between normal and abnormal production status. By applying this approach to real industry image streams, the proposed monitoring scheme effectively represents the bi-phase change of manufacturing processes and provides valuable change-point information.

We present an effective framework for improving the breakdown point of robust regression algorithms. Robust regression has attracted widespread attention due to the ubiquity of outliers, which significantly affect the estimation results. However, many existing robust least-squares regression algorithms suffer from a low breakdown point, as they become stuck around local optima when facing severe attacks. We propose a novel framework that enhances the breakdown point of these algorithms by inserting a prior distribution in each iteration step, and adjusting the prior distribution according to historical information. We apply this framework to a specific algorithm and derive the consistent robust regression algorithm with iterative local search (CORALS). The relationship between CORALS and momentum gradient descent is described, and a detailed proof of the theoretical convergence of CORALS is presented. Finally, we demonstrate that the breakdown point of CORALS is indeed higher than that of the algorithm from which it is derived. We apply the proposed framework to other robust algorithms, and show that the improved algorithms achieve better results than the original algorithms, indicating the effectiveness of the proposed framework.

One of the problems in the plastic manufacturing sector is the low level of efficiency, this through defective products, which forges the exploration of tactics to control this index. In this context, the present investigation proposes a model of improvement of the plastic accessories productive processes, which will provide an increase in productive efficiency. After the analysis through the Visual Stream Mapping (VSM), Overall Equipment Effectiveness (OEE) and Failure Mode and Effect Analyses (FMEA) of the current situation, a low level of efficiency of the production of plastic accessories and their respective reasons were diagnosed. For which, the Total Productive Maintenance (TPM), 5S and Jidoka tools will be implemented in the production processes in order to reduce problems or anomalies.

Multi-material additive manufacturing (MMAM) offers a flexible way to produce complex and functionally graded materials (FGMs) with varying mechanical, electrical or chemical properties, leading to more sophisticated and cost-effective part designs and fabrications. However, the lack of material-aware printing capabilities in existing CAD and CAM software poses a significant challenge. MMAM with existing CAD/CAM software can only fabricate multi-material parts with sharp multi-material interfacial transitions. To address this challenge, we propose a novel adaptive voxelization and parameter assignment technique integrated with the commercial CAD software Rhinoceros 3D to achieve material-dependent variable printing toolpath generation. The proposed method enables adaptive process parameter assignment at variable resolutions. To meet the multi-material design specifications, the proposed method can generate G-code commands with adaptive process parameter assignment. The key novelty and contribution of this work is the mapping between the solid CAD design to the final printing toolpath with parametric transitions in the multi-material interfaces. Various transition functions, including radial distribution and linear transitions, can be defined to achieve graded material distributions. Graded voxel size is also achievable with finer resolution at multi-material interface regions to enable precise control of process parameters and material distributions, whilst coarse voxels are used for deposition in single-material regions. The proposed method sets the foundation for producing complex multi-material components by AM.

Nonconforming items and machine failures make the production system unreliable. In practice, quality control and maintenance technologies are introduced into the unreliable production systems to manage both the products and machines, where quality control technologies can be achieved by inspecting the quality characteristics of the products. However, some existing work ignored the variation in quality characteristics or the machine failure. Quality control can provide information for maintenance. Meanwhile, systems integration brings challenges for management. To solve these problems, considering that quality characteristics may vary and machines may fail, a profit model is proposed for modeling the relationship between profit and management policies. The goal is to maximize the production profit through the optimization of management policies via Genetic Algorithms (GA), which involve production planning, quality inspection, and maintenance policies. Finally, a case study is used to demonstrate the application of the proposed method, and the results demonstrate that our proposed method can generate higher profits compared to the traditional methods.

This paper presents a novel method for identifying the operating condition of a mechanical system by combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the Genetic Algorithm-optimized Deep Belief Network (GA-DBN). The study focuses on the extraction of condition features and the optimization of DBN parameters. Firstly, the CEEMDAN algorithm is employed to decompose the signal and extract several intrinsic mode functions (IMFs), which are used for signal reconstruction. Subsequently, the GA is employed to optimize the parameters of the DBN, including learning rate, number of hidden layers and number of hidden layer nodes, etc. Then the power spectrum of the reconstructed signals are used as feature vectors, serving as input to the GA-DBN model for identifying the mechanical system operating condition. To evaluate the effectiveness of the proposed method, a case study is conducted using a sliding bearing friction test data. The results demonstrate the high accuracy of the method, which achieving 96.00%. Moreover, compared to the DBN model, the GA-DBN model has a significant improvement of 7.67% in the operating condition recognition.

The current study investigates the integration of unrelated parallel machine scheduling and inventory (WIP) buffer allocation in the production process using Mixed Integer Linear Programming (MILP). Industrial variables like pre-emption and job batching are considered to make it more realistic. The experiment showed that buffer stock is essential for managing unpredictability and meeting customer demand. The findings highlight the significance of the current schedule process with batching of jobs and buffer stock as a crucial mechanism for handling and reducing the negative impact of disruptions in the manufacturing process. Additionally, it also finds pre-emptive job locations and tardy jobs, both of which are crucial for on-time delivery. The CPLEX 12.8 solver is used to analyze the MILP model, which gives the integer results in a suitable time.

The challenges facing manufacturing companies today increase the importance of developing assembly worker competence. At the same time, the complexity of competence development is growing with the expanding number of available devices and methods. To support managers in planning competence development, a systematization is needed. In this paper, a structured literature review of definitions regarding human resources development and existing methods for planning competence development was conducted. Building on the findings, a concept was elaborated on how a production manager can plan and control the learning process of an assembly worker. The concept is based on the Plan-Do-Check-Act-cycle and a learning process model and contributes to achieving more resilient productions.

The significance of maintenance as a value-added factor for quality assurance is unquestionable, as it has a considerable impact on the long-term success or failure of an organization. The challenge lies in establishing an effective maintenance system that adheres to global standards and considerations of efficacy, within a maintenance environment. This paper aims to investigate the difficulties encountered in standardizing and maintaining adopted maintenance processes. To collect the data, 30 participants were interviewed using structured questionnaires, revealing that attitude, lack of skills and training, poor work execution, and sustainability were the most significant challenges. To prevent adverse effects on production, resources must align with the initially adopted maintenance strategies. The research delves into exploring the challenges of standardization and sustainability in maintenance processes for a maintenance business.

An assortment optimization problem for vending machines has received limited research attention due to its unique factors. These include various product types, limited inventory and replenishments, and sales fluctuations. An optimization approach based on the partially observable Markov decision process (POMDP) has been proposed. This paper applies the POMDP approach to the optimization problem under various conditions and validates its effectiveness through simulations. Additionally, simulations are conducted to explore alternative selections when the product that a consumer wants to purchase is sold out. The results reveal that the POMDP model contributes to increased sales and improves consumer satisfaction through alternative selection.

Product-centric approach to business can be valuable as economic value is generated through products. However, the generation of value necessitates choices and decisions through the product lifecycle to ensure the long-term well-being of not only the business, but also the environment, society, and future generations. This study aims to provide clarity on key elements of product focus at operational, tactical, and strategy levels to enable sustainable growth. A scoping review is carried out to synthesize a conceptual model. The results indicate a systematic approach to product focus with the potential to enable sustainable growth and long-term economic value. Products and related productization are at the core when managing and maintaining the identified key elements. It appears that promoting consistency and systematics through the operational, tactical, and strategy levels is necessary.

Chinese President Xi Jinping emphasized in the report of the 20th Party Congress that we must adhere to the principle that "talent is the first resource", thoroughly implement the "talent strengthening strategy" and adhere to the "talent-led drive". This paper focuses on exploring ways to better help Xi'an attract talents and write the Shaanxi chapter on catching up and Chinese-style modernization. An index system for talent attraction and a talent attraction model are constructed, and twelve cities of the same type as Xi'an, such as Chengdu and Wuhan, are selected for empirical research to evaluate the attractiveness of thirteen cities. Through comparative analysis, the influencing factors limiting the mobility of new generation of scientific and technological talents are identified. It also proposes countermeasures in terms of increasing investment in public facilities and improving economic strength to enhance comprehensive capacity.

Traditional models for decision-making under uncertainty, primarily rooted in the expected utility theory, often fail to address the unique nature of one-time decisions. However, salience knowledge has been highlighted as a significant factor in human decision-making according to recent research. Diverging from conventional lottery-based methods, the Multistage One-Shot Decision-Making Approach (MOSDMA) proposes a unique scenario-based approach to multistage decision-making under uncertainty.In this paper, the application of MOSDMA to a distinctive one-time museum investment problem within the public sector in Oman is explored, thereby evaluating its practical efficiency. It is the first application of this innovative approach to a current issue, rather than reevaluating previously addressed decision problems that have been done before. The results underscore the effectiveness and simplicity of MOSDMA in a new decision-making process, underlining the need for further exploration of this method in future research.

In the last decades many city logistic projects attempt to tackle the unyielding problems caused by urban freight transport. Due to the rise of urbanization and the rising volume of parcel shipments, a steady increase of emissions, especially in inner cities were inevitable. Although many projects offered reasonable and viable solutions, very few have survived the post-government funding stage due to the limited engagement and interest from the general public and other stakeholders, which could have enhanced the projects' profitability and feasibility. This paper wants to introduce a method to create an analysis tool that encompasses all the needs and goals of all stakeholders related to city logistic projects to facilitate cooperation and consensus by providing transparency and scenarios to achieve the best outcome for everyone. In summary, the aim of this paper is to present a tool capable of achieving consensus among all stakeholders involved in a city logistics project and identifying the best scenario to be profitable.

When a product is purchased, its value includes both its design and functional aspects. In the product design, Kansei, affectiveness, or sensibility of product is very important for the user value. However, designs that match the sensibilities tend to become ordinary designs. The design must be novel and align with the sensibilities of the user. This study aims to create sensible and novel design. In our previous study [3], we attempted to match the sensibilities of many users and we conducted research on the novelty of the design. However, sensible and novel design is very different among the users. Therefore, in this study, we focused on the sensible and novel design fit for each user, and we propose a design method for creating sensible and novel design. In our method, we repeatedly conduct interactive survey of Kansei in order to be able to better match the user’s sensibilities and novelty. The rough set theory is applied to extract rules fit for the user sensibility, and arrange the rules, and according to the interactive survey the rules are refined, and create sensible and novel design fit for the user.

Decarbonization is a strategic topic of interest in view of issues in climate change and global warming. Various countries and industry sectors set targets to reduce Greenhouse Gas emissions. There are many ways to help achieving decarbonization, including technical and operational measures and adopting cleaner and greener alternative energy sources. At the same time, industries increasingly make use of data science techniques in enhancing planning, processes and operations. Data science techniques such as machine learning as a branch of artificial intelligence facilitate decision support and management. By tapping on machine learning, there are plenty of opportunities for organizations to attain decarbonization goals and become more cost-efficient simultaneously. This study provides illustrations and examples from analyzing fuel consumption and emissions in transportation. It shows how machine learning can be used in decarbonization research.

Turnaround strategies become critical for companies facing financial decline or crisis. Cost cutting and downsizing have been the most immediate response management undertakes to overcome shortly. Nevertheless, these strategies result in recovery for a short term period. A value creation strategy is required to convert the firm from a state of decline to a growing state. This paper is to study how system thinking and entrepreneurial thinking is applied to formulate corporate turnaround strategies. We will employ a comprehensive systematic literature review on research articles in business turnaround, system thinking and entrepreneurship. This will discuss the dynamics of corporate turnaround from different approaches. Expected findings will provide a conceptual framework of system and entrepreneurial thinking in corporate turnaround. This conceptual framework will help researchers to study corporate turnaround strategies from different perspectives. Further, this will help managers to apply system and entrepreneurial thinking in selecting and developing the strategies.

Contact tracing is essential for identifying a population exposed to infectious diseases such as COVID-19. Often, there is a lag between the infection date and confirmatory testing, leading to uncertainties while tracing the potential of disease transmission. Our research proposes to predict the maximum likelihood infection tree across known infected nodes and potential spreaders based on partial available information. Using a social-contact network, we infer the spatiotemporal path of infection for a given pandemic. An integer linear programming method there traces back the infection tree and identifies modalities to disrupt the infection propagation. Here, the novelty lies in accurately predicting the paths of infection travel in real-time with the model leveraging incubation period, multiple contacts and time-varying contact probabilities. The results thus obtained are validated using the Susceptible-Infected-Recovered (SIR) simulation.

This paper examines the importance of different features that can be displayed on a website environment and their impact on customers' preferences, decisions, and behavior. The features under investigation in this research include promotional offers, product information, electronic word of mouth, sustainability, warm-tone color, cool-tone color, and music. The study focuses specifically on the fashion retail industry, aiming to provide insights for improving website environments to attract more customers. To investigate the effect of different features on customers' preferences and decisions, a research model based on the Theory of Planned Behavior and the Stimulus-Organism-Response model has been proposed. The study also incorporates the use of Electroencephalogram (EEG) technology from neuromarketing research. Participants in the experiment were asked to wear a device that detects their brain activity during the experimental setup. The results of the study revealed that factors such as promotional offers, colors, and music significantly influence customers' purchase behavior.

This paper explores the crisis management strategies and contributions of Islamic Boarding School Business Units (IBS-BUs) in Indonesia toward sustainable entrepreneurship and the achievement of Sustainable Development Goals (SDGs). Drawing on qualitative research and thematic content analysis of interviews with IBS-BU managers, the findings reveal practical insights for IBS-BUs, highlighting human-centered strategies such as strengthening faith, fostering an innovative and agile workplace, maintaining constant coordination, cultivating employee loyalty, and prioritizing entrepreneurial skills. These strategies enable IBS-BUs to overcome challenges, achieve resilience, and sustain their businesses. Moreover, the study demonstrates how IBS-BUs can contribute to specific SDGs including SDGs 1, 4, 8, 10, and 17. By aligning their practices with the SDGs, IBS-BUs can actively engage in sustainable entrepreneurship and address societal, ethical, economic, and ecological objectives. This research bridges a knowledge gap by providing a novel theoretical framework for the development of IBS-BUs and their potential as agents of sustainable change.

Emergency medical supplies are vital to successful disaster preparedness and response processes. Considering uncertain multi-channel emergency supply in reality, this study proposes a two-stage stochastic programming model for integrated emergency medical supply planning in healthcare coalitions. In the first stage before disasters, supply pre-positioning and signing of two types of supply contracts are determined, and recourse decisions of emergency supply procurement, allocation, and transshipment are made based on the realized disaster impacts and the first stage decisions. We develop four comparison models to highlight the benefits of considering multi-channel emergency supply. With a case study on the healthcare coalition of West China Hospital, Sichuan Province, China, in the context of the COVID-19 pandemic, we show the effectiveness and benefits of the proposed model, and we generate managerial insights and policy suggestions to facilitate the management of multichannel emergency medical supplies in healthcare coalitions.

We address the challenge of cost management for pre-disaster emergency funds, with ample warning time available for completing emergency operations. We formulate a mathematical model for a complex vehicle routing problem involving a single depot, a fleet of heterogeneous vehicles with limited capacities, and simultaneous delivery and pickup tasks. Each vehicle type is assigned a specific road network based on vehicle-road compatibility. We develop heuristic approaches to generate high-quality solutions for this problem and compare them with a state-of-the-art commercial solver. Our findings reveal that our heuristics perform exceptionally well for very large problem instances, while the commercial solver outperforms them for smaller instances. Moreover, our algorithms can handle scenarios where customers have either delivery or pickup demands, as well as cases where both operations are required. We evaluate the performance of our exact formulations extending existing data, as well as generate new data sets demonstrating the effectiveness of our bioinspired heuristic methods in achieving satisfactory outcomes.

Chiang Rai, Nan, and Phayao, located in the provinces of northern part of Thailand, encountered flood, landslide, river overflow during raining season. Floods in this area are so difficult to predict. Flood mitigation plan is a critical key for relief and recovery. We focus on the preparedness plan for locating relief facilities. Our method to locate the optimal relief facility location is based on the expected regret P-median model with historical data for both populations and transportation routes. We conduct two experiments when the district is served by one relief facility and when the is served by the number of facilities following by the heuristic rule. Numerical results show that Muean Chiang Rai, Wiang Chai, Chiang Khong, Mae Chan, Mae Suai, Mueang Nan and Pua are suitable for locating relief facilities.

Blockchain technology (BcT) presents opportunities for improving transparency and traceability in an agriculture supply chain. The BcT enables the combination of the immutable decentralized data ledger with smart contracts to unify different actors in an agriculture supply chain into a digital platform. The latter is an executable program that enables automatic running if certain conditions are met. It brings BcT implementation in improving supply chain performance in minimizing non-added value processes, especially from the farmer's perspective. An agriculture supply chain comprises several actors with various digital infrastructures and standards. The information regarding products and services is also scattered, and individual actors' various infrastructure facilities silo the access. Thus, this study carries a maize supply chain (MSC) case study. This manuscript presents the development of a conceptual architecture, using the BcT and smart contract feature, for an MSC's aggregator platform.

While global attention on reducing food waste has increased, the demand for perishable commodities such as food and pharmaceuticals is growing. This emphasizes the need for effective perishable inventory management, which has become increasingly complex due to the perishability of these products. Traditional optimization methods, such as Dynamic Programming, require significant time and effort to solve these challenges. In this study, we use Deep Q-Network and Proximal Policy Optimization, which are deep reinforcement learning methods that can give numerical and approximate solutions to complex problems. In the inventory problem considering costs such as ordering, storage, lost opportunities, and spoilage, we define the inventory status as the state, the ordering as the action, and the negative total cost as the reward. We conducted a performance comparison of the two methods with an aligned total number of time steps. Furthermore, through numerical experiments, it was confirmed that the application of both methods resulted in a cost reduction of at least approximately 30% compared to the basic stock policy.

The uncertainty within the agricultural supply chain often leads to substantial wastage and unmet demand, underscoring the imperative of addressing the crop planning problem within agricultural supply chain management. In this paper, we developed a set of mathematical programming models that considers both the free market scenario and the contract farming scenario. The model accounts for yield and price volatility induced by meteorological and market conditions. Through the simulation of real-world crop planning scenarios using mathematical models, the planning problems faced by both farmers and consumers in building optimal matching mechanisms to obtain optimal overall farm revenue are evaluated. Numerical experiments have been conducted to validate the model, and we present management recommendations tailored to various scenarios.

Class-based storage is a commonly used storage strategy in unit-load warehouses for organizing SKUs into classes based on their turnover, thereby minimizing the overall pick distances. Unit-load warehouses with a V cross-aisle are even more effective in lowering pick distances. The position of the V cross-aisle is kept at its optimum depending on the product demand profile and the storage policy used. Previous research has determined the optimal position of the V cross-aisle and storage class boundaries for different product demand profiles. However, the irregular shape of the optimal storage class contours (boundaries) makes the practical implementation challenging. In this paper, we propose a simpler storage class boundary for a V cross-aisle warehouse which can be easily implemented. We also demonstrate that such an adjustment is marginally sub-optimal in comparison to the original arrangement and apply our model to a real-life warehouse setting. Our findings can be used to further extend the model to higher number of classes with multiple V cross-aisles.

Technological advancements have transformed the landscape of examination grading, necessitating efficient and reliable solutions. This paper introduces "One-Shot Grading," an Automatic Answer Checker that utilizes machine vision and image processing to evaluate multiple-choice and true-false questions. The system offers adaptability to diverse exam formats, allowing for modifications to the answer sheet's choices, parts, and size. Extensive validation tests demonstrate consistent performance with 100.0% accuracy in recognizing student IDs and grading sheets marked with a 2B pencil. The system effectively identifies and rejects erroneous sheets, maintaining a robust grading process. Comparison with the ZIP GRADE app confirms the reliability and accuracy of the Automatic Answer Checker. The system displays flexibility and accuracy in grading papers of varying weights, showcasing its adaptability to real-world conditions. Future research should explore extending the system to different question formats and investigating environmental factors' impact on performance.

AME4163: Principles of Engineering Design is a design, build and test course offered at the University of Oklahoma, Norman, USA. Throughout the semester students are expected to reflect on authentic and immersive experiences and write Take-aways and Learning Statements. At the end of the semester each student submits a summative Semester Learning Essay. Between 2019-2021 we collected about 10,000 Take-aways and Learning Statements from the Semester Learning Essays of nearly 400 students. In this paper, we attempt to help instructors gain insight of what students have learned through these materials and thence improve the delivery of the course in the future. We analyze their Take-aways from summative Semester Learning Essays by using dictionary-based sentiment analysis to assess students' subjective feelings toward what they have learned. The results prove that we gain insight into their learning thereby providing instructors with evidence-based guidance on modifying the course from different perspectives. The method proposed is generalizable to courses that involve authentic immersive experiences.

The new technologies have come to revolutionize not only the industry but also how we prepare the new generations so that they are able to make the best use of these technologies. This paper consists of three parts: 1) the industrial engineering trends and needs of the industry, 2) the support systems and methodologies developed to satisfy these needs, and 3) how the Industrial Engineering degree program and engineering education needs to evolve according to not only the new challenges that the world is facing but also the way the new generations learn. An overview of the learning methods used in Education 4.0 is presented and how these methods give an integral formation to the students of higher education.

The importance of entrepreneurship education in TVET is emphasized to enhance graduates' employability, entrepreneurial skills, and sustainable economic development. However, entrepreneurship education has not yet had a very positive effect on the number of capable independent entrepreneurs. This weakness was identified by the institution's absence of an education system and several ignored aspects. A systematic review used to pinpoint the main research themes, topics, and methodologies employed in the literature, gaps, challenges, and opportunities for future research and practice in Malaysian TVET entrepreneurship education. Based on literature, authors raised three research questions which provide the insight and direction of entrepreneurship education in Malaysia. It is suggested that the development of comprehensive TVET entrepreneurship curriculum should consider the integration of potential fields and human behavior theories.

A semester wide Project-Based Learning case study was conducted using a double survey aiming at gathering students’ perceptions on a number of relevant aspects of teamwork, including peer assessment within teams. The peer assessment process was conducted three times during the semester, and the survey was issued twice, i.e. just prior to the first peer assessment, and after the third peer assessment. The study highlights that the students were generally more optimistic, regarding all categories of teamwork, before conducting the first peer assessment, and less enthusiastic at the end of the project. Results also unveil a marginal gap between the surveys, relating cooperation of team members, denoting a progressive degradation of attitudes and commitment towards teamwork. This is partly attributed to misalignment of self and peer perceptions, of individual contributions to teamwork and its outcomes. Additionally, a gender analysis showed significant differences on some questions, namely in the appropriateness of using peer assessment. Female students held a more conservative standing at the beginning of the semester, and improved considerably, while male students experienced exactly the opposite movement.

We address the container truck routing problem that arises in hinterland logistics, where trucking companies are responsible for transporting empty and loaded containers between various stakeholders in the shipping container supply chain, including consignees, shippers, and others. This problem involves determining efficient routes that balance empty and loaded trips, enabling trucking companies to meet customer transportation requests while optimizing their operations.In this paper, we investigate a variant of the container truck routing problem that focuses on maximizing the net worth of a trucking company by identifying the most profitable set of routes. The model takes into account two types of containers (40-ft and 20-ft) and two types of trucks (long trucks and short trucks). We prove that the problem is NP-hard and propose a compact mixed-integer programming formulation. To evaluate the effectiveness of our approach, we conduct numerical experiments. The results demonstrate that the proposed model delivers optimal solutions for relatively large randomly generated instances. This evidence underscores the practical applicability and efficiency of our proposed approach in solving the container truck routing problem.

Several research on Reverse Logistic (RL) in plastic packaging have been conducted, but just a few have concentrated on empty pesticide container (EPC). The RL program in the agrochemical business is primarily supported by government regulation, as well as understanding that incorrect disposal of EPC on farmland has major environmental and public health effects. This study intends to present a systematic literature review of previous studies in managing EPC through reverse logistic implementation (RL/EPC) in globally and to analyze whether the implementation of RL/EPC is imposed by local regulation. To attain this purpose, a bibliometric study was carried out with the objective of acquiring and assessing key publications in RL/EPC utilizing a structured literature review. A qualitative Thematic Analysis Grid was performed in order to determine whether the RL/EPC implementation is enforced by government regulation or organizational initiatives. The analysis reveals that most countries that are implementing reverse logistic scheme for EPC is mandated by the regulations. Non Governmental Organization or industry association commonly facilitate and cordinate the RL/EPC process.

This paper proposes an optimized tourist itinerary recommender system (OTIRS) using a capacitated vehicle routing problem with constant service time (CVRPCST) approach, slightly modified from capacitated vehicle routing problem (CVRP). The goal is to maximize tourist destinations around a major city while taking the shortest routes. To achieve this, a comprehensive dataset of major cities and tourist destinations in India is constructed by scraping data from commercial websites.  Post data accumulation, we formulate the problem as an Integer Linear Programming (ILP). The CVRPCST has been modified to represent each day of the itinerary as a vehicle, with its capacity determined by the available hours, while the tourist destinations and their respective travel and stay times are analogous to products with varying volumes in the vehicle routing problem. Modifications to the Miller-Tucker-Zemlin (MTZ) sub-tour elimination formulation improve itinerary optimization accuracy and effectiveness. Optimizing tourist itineraries based on user input parameters and accumulated data validates the system, providing efficient and personalized travel recommendations. This optimized tourist itinerary recommender system aims to improve travel and enable the exploration of diverse Indian destinations.

This paper explores an efficient algorithm for a closed-loop supply chain (CLSC). The CLSC problem is formulated as a Mixed-integer Linear Programming (MILP) with multiple scenarios. The variables in the model, such as the location selection of factories and collection centers, are integer variables, whereas the transportation flow between nodes in the model are continuous variables. Due to the inclusion of multiple scenarios, the number of constraints in the continuous problem part is significantly large. Furthermore, the presence of integer variables extends the computation time required for direct calculations. This paper attempts to use Benders Decomposition to divide the integer and continuous parts of the model into two steps of solution, aiming to reduce the computation time. This paper extends the original model, which considers random demand quantities and recovery rates as stochastic scenarios, to include scenarios for insufficient raw material supply and fluctuating recycling values. The original model is transformed into a multidimensional stochastic model, and the practicality of Benders Decomposition is demonstrated.

With the popularization of electric vehicles and the advancement in battery swapping technology, the number of battery swapping stations is increasing rapidly. In the meantime, a new type of station, integrated battery swapping and charging station (BSCS), has emerged, combining the functions of charging and battery swapping.  In this paper, we study the design problem of the BSCSs, including determining the capacity allocation and a discount price. We employ a choice model to describe customer behavior and develop a novel queuing model for the BSCS. Furthermore, a recursive algorithm is provided to evaluate the system performance. In the numerical analysis, we discuss some basic cases and the effects of several key factors on the system.

This study presents a solution to optimize the organization of garbage collection lanes in large cities by investigating garbage collection systems and transportation management. In urban areas, designated parking spots are assigned for garbage collection vehicles at city offices within the collection areas, while waste disposal points are typically located outside the city. The aim of this study is to minimize processing costs under specified conditions, with the problem's complexity dependent on the number of collection points and waste quantities. The study focuses on path optimization in garbage collection systems, considering factors such as travel distance, fuel consumption, and labor requirements. By proposing an optimized approach, the research aims to enhance the efficiency and cost-effectiveness of garbage collection operations in large cities.The findings of this study offer a systematic and comprehensive methodology to improve the cost efficiency of garbage collection systems in urban settings. Practical recommendations derived from this research can assist waste management authorities and urban planners in making informed decisions regarding garbage collection lane organization and transportation management in large cities.

This study investigates the knowledge management practices in the end-of-life phase of the product-service systems through both a review of the recent literature and an empirical interview study with four recycling and waste management firms in Finland. The focus, in particular, is on the types and sources of knowledge used, the practice of knowledge sharing, and the impact of digitalization. It was found that companies at the end-of-life phase conduct effective knowledge management practices within and between their organizations. The information loop between stakeholders at the end-of-life phase and stakeholders at the beginning-of-life and middle-of-life phases, however, is inadequate due to certain constraints and conditions. The findings suggest neutral agreements be made between recycling companies and waste management companies to promote knowledge exchange. Closer collaboration should be executed between companies and scientific research institutes to support each other’s operations.

This paper systematically describes how the dependencies of natural language requirements can be implemented using a data based approach. A basis for further validation is conceptualized. In a short introduction and motivation, it is described that the need for a cross-system interconnection of individual requirements from different stakeholders can be met with data based methods. The methodology of automating the cross-linking as well as the representation of the interdependent requirements are described in the context of graph-based methods and generative large language models. Finally, an abstraction of the pipeline result is exemplified. This paper describes the basis for a methodology, which will be detailed in future work.

This paper aims to explore business models for Additive Manufacturing (AM) enabled consumer-production. The authors investigate customer integration as a marketing strategy, emphasizing the decentralized production capabilities of AM. The study uses a mixed-method approach, combining a quantitative survey and qualitative expert interviews. Results reveal that consumer-production enables the economic offering of niche products. Specialized products with low quantities become economically viable by separating development and production activities between the company and the customer. Companies successfully utilize consumer-produced accessories to complement their product lineup. The findings help assess a company’s suitability for implementing a consumer-production approach and provide implementation suggestions. The study highlights the dynamics of utilizing consumer’s AM production abilities and suggests product strategies that exploit consumer-production.

Digital transformation is driving major changes in the overall way the automotive industry operates and its business models. The purpose of this study is to analyze how government regulations and support affect the product innovation of automotive companies in order to respond digital transformation. This study used data from 393 companies belonging to the automotive industry among the 2020 Korea Innovation Survey data. This study was conducted in two stages. The first stage was the logistic regression analysis, which statistically confirmed the effects of government regulations and support on automotive companies’ product innovation. In the second stage, fsQCA analysis, a qualitative comparative analysis methodology, was performed to derive configurational paths affecting automotive companies’ product innovation. The result of logistic regression analysis shows that the government's financial support has a negative effect on automotive companies' product innovation, but non-financial support has a positive effect. In the case of government regulations, economic and social regulations have a negative effect, but administrative regulations have a positive effect. In addition, the result of fsQCA shows three configurational paths for product innovation.

How can governments assess the contribution of evolving technologies to evolving socioeconomic goals? While such situations in the private sector typically command backcasting approaches, in national technology strategy contexts, conventional methods are uniquely challenged by institutional complexity, extremely long time horizon, technological uncertainty, and technological interdependence. First, we review established national technology backcasting practices at NASA (US) and Ministry of Economy, Trade and Industry (Japan) to set a baseline. Second, we outline the process of Drivers of Technology Needs (DTN), a technology foresight and systems decomposition approach developed at Government Office for Science (UK) in collaboration with the authors in 2022. Third, we contrast the data and policy recommendations resulting from the baseline methods and DTN. Finally, we consolidate the DTN process into an operational guide and outline lessons learnt in each step. Our finding is that the decomposition and consideration of intermediating system capabilities relate low-level emerging technologies with high-level sociotechnical goals more consistently. Those interested in our study would include RD&E policy stakeholders who wish to apply foresight methods to develop applications for emerging technologies.

This study explores the perceptions of master’s program students in Industrial Engineering and Management (IEM) at Umeå University, Sweden, concerning the current and future impact of artificial intelligence (AI) on their discipline. Employing a descriptive, cross-sectional survey design, we collected quantitative data from participants asked to assess AI’s influence on decision-making, human-computer interactions, and information management, among other areas. While ordinal regression analysis revealed no significant correlation between the student’s academic year and their survey responses, a Wilcoxon signed-rank test indicated a statistically significant belief that AI’s impact on all surveyed areas would intensify within the next decade. Our findings suggest a need for engineering education to evolve to adequately equip future professionals for the expanding influence of AI in IEM. Furthermore, the results add to the ongoing discussion of AI’s role in engineering education and the broader industrial engineering and management field.

The e-commerce market has been expanding rapidly in recent years, and various recommendation systems have been studied. Some of them only recommend products that are selling well at a given time and do not take user characteristics into consideration. It is desirable for a recommendation system to take into account the characteristics of customer attributes, such as personalization, in order to efficiently stimulate the desire to purchase a product. Many conventional recommendation systems use NMF, but one of the issues with NMF is that the analysis results include noise that has no intrinsic meaning. Therefore, in this study, we proposed an analysis method based on DeepNMF, which can remove noise by layering NMFs, as a precondition for a system that focuses on consumer characteristics and recommends appropriate products at the timing when user demand is increasing. The results confirmed that by extracting only essential information through noise elimination in DeepNMF, it is possible to appropriately identify customer characteristics. It was also confirmed that it is necessary to change the timing of product recommendation for each consumer characteristic.

This study proposes a new method for categorizing and visualizing the characteristics of user evaluation tendencies via customer satisfaction data from hotel reservation sites. We focused on the fact that there are individual differences in the way customers evaluate items, and that there are items for which the evaluation is lenient and others for which it is not. We proposed a new method for calculating feature quantities; furthermore, we proposed a user clustering and visualization method using SHAP and UMAP for feature values and analyzed the data. The proposed method enables us to visually understand the features of the data by comparing the relationship between the SHAP values and evaluation trends on a low-dimensional map. The data were based on customer data provided by Rakuten, a major IT company in Japan.

In the domain of mechatronic system development, there exists a significant potential for the collection and utilization of vast amounts of operational data. The practical implementation of this potential presents considerable opportunities, although accompanied by substantial challenges. While some companies have already gathered large volumes of data, they are yet not able to utilize its full potential for optimizing technical systems. An essential factor in enabling developers to effectively leverage operational data lies in ensuring accessibility to the data and facilitating intuitive navigation within the database structures. This article examines the prerequisites for a database structure that can be intuitively utilized by developers as end-users. Using the example of a machine tool manufacturer, an architecture for metadatabases is presented, which incorporates user involvement in the architecture development process and is implemented within the research environment of the present research work. A comparative analysis against the pre-existing metadatabase architecture reveals significant enhancements in the usability of metadata for developers.

Majority of the load forecasting literature use point error metrics for model selection. There is limited attention to the possible issue of different model rankings obtained by considering different point error and probabilistic error metrics. In this paper, we consider a reasonably wide set of point error and probabilistic error metrics for model selection in the validation period. We also consider the two possible routes of model selection- ex-ante validation and ex-post validation. Based on ex-ante validation, competing models arise considering different subsets of evaluation metrics. However, this conflict disappears when model selection is done based on ex-post validation. It is found that the best-performing model in the test period is the model selected based on ex-post validation performance.

In many real-world networks, the relationships between objects have polarity and these networks are known as signed networks. The relationships in signed networks have positive/negative signs that represent the trust/distrust between objects. Sign information in these networks would be useful for various data mining tasks and decision-making processes. In this work, the problem of sign prediction for unlabeled/unsigned edges in undirected signed networks is considered and a representational learning method for nodes is proposed. Using these learned low-dimensional representations of nodes, sign prediction for unlabeled edges is performed. This method is domain-independent and independent of the distribution of signs of edges in the network and requires only the structural information of the network. Experiments are performed on real-world benchmark signed networks and the performance of the proposed method is compared to the earlier domain-independent methods. The results show the effectiveness of the proposed method for node representation learning for the task of sign prediction for unlabeled edges.

The dissemination of information through social media is influential in shaping user psychology and behavior, and it can also impact the spread of epidemics. Prior research focused on quantitative analysis of social data, neglecting subjective opinions. This study utilizes opinion mining techniques to predict the number of epidemic cases by analyzing opinions extracted from social data. This study employs sentiment dictionaries to evaluate opinions by quantifying positive and negative words and aggregating them to determine overall sentiment polarity. Subsequently, Daily sentiment polarity is calculated based on the generation date. In conclusion, the estimation of the total number of confirmed epidemic cases is performed using deep learning models, DNN, RNN, and LSTM. The estimation is achieved by incorporating the number of confirmed cases and daily sentiment polarity as inputs to the aforementioned models. The presented algorithm showed that incorporating opinion mining improved the accuracy of predicting confirmed cases by 2.26% compared to models without this technique. Consequently, opinions expressed in social data have been confirmed to hold value as attributes that can be utilized for predicting the quantity of confirmed cases.

With the widespread use of Internet of Things (IoT) technology in the supply chain, the threat of attacks targeting the transmission of IoT data packets is getting more serious. Existing solutions used in traditional network security cannot be deployed in IoT networks due to the simple structure and low computational capacity of IoT devices. Hence, an IoT-specific security framework is required. This paper presents a security framework for IoT networks that includes a dynamic watermark authentication and detection module as well as an anomalous cause analysis module. The former employs the Long Short-Term Memory (LSTM) method to extract features from transmitted data packets, creating watermarks dynamically to encrypt data and detect attacks during transmission. The latter introduces the game theory to model the interactions between the attacker and the gateway on each IoT device in the network to analyze the root anomaly cause and the most vulnerable device to attack. Simulation results have shown the effectiveness of the proposed framework.

Different social backgrounds and planning policies give rise to diverse urban morphologies. These morphologies influence urban microclimate factors and contribute to the formation of unique local microclimates, particularly in terms of outdoor temperature. In recent times, the heat island effect has gained increasing significance during the summer season. Therefore, this study aims to explore the correlation between urban microclimate simulation and urban morphology within the context of the heat island effect. Specifically, we investigate how the outside temperature varies across different types of residential buildings in Yeongdeungpo-gu, Seoul, South Korea, during the summer period. We compare temperature conditions using a multi-dimensional system of building clusters' morphological indices and employ ENVI-met software for simulation purposes. The results of the urban microclimate simulation are comprehensively analyzed, revealing a significant finding: high-rise residential buildings exhibit considerably higher outdoor temperatures compared to low-rise residential buildings. Furthermore, the presence of open spaces plays a crucial role in mitigating high neighborhood temperatures. By deriving insights from these findings, we aim to provide valuable conclusions to support city managers in making informed decisions.

Qualified workers are becoming increasingly scarce in various industries in Europe. This also affects the labor-intensive assembly sector. Therefore, it is crucial to consider the availability of qualified workers during the selection and planning of assembly systems. This paper presents a procedure to assess the impact of the availability of workers with different qualifications in the simulation-based planning of assembly systems. An approach for representing the qualification of employees is selected and the implementation in the simulation is described. Additionally, a reference work plan is developed to compare different assembly organization forms. This plan is derived from analyzing work plans used in various companies and assembly forms. To represent typical scenarios related to the deployment of qualified workers, three simulation scenarios are analyzed for each assembly form. The evaluation of the simulation experiments takes into account the output and the workload of the workers as well as the task variety and the task identity in order to support prospective human-centered work design already in the planning phase. One key result is that group assembly shows greater flexibility potential than other forms of organization.

Usage of Photovoltaic (PV) system has been increased tremendously due to less payback period, free maintenance and the ability to convert solar energy directly to electrical energy. Global Maximum Power Point Tracking (GMPPT) scheme is used to track maximum power path of the PV array under non-uniform irradiance condition. The GMPPT controller developed using metahuristic algorithms tracks the global power path successfully. Apart from this, the GMPPT controllers fail to addresses string mismatch losses. Hence, this paper presents a simple reconfiguration technique to nullify the mismatch losses between the strings. The proposed reconfiguration technique is cost effective and easy to implement. Initially, the proposed reconfiguration model is developed and simulated using MATLAB/SIMULINK and the results are compared with the conventional series and parallel connection of the PV array. Results show that this reconfiguration method enhances the maximum power by reducing sinking or mismatching losses. 

Electric Vehicles are becoming more popular these days. Bidirectional inverters are used to implement regenerative braking in electric vehicles. A unique multi-level bidirectional converter structure is proposed in this paper for applications in electrical vehicles (EV). It is equipped with a multi-level dc-dc transformer with a dc link condenser. The two-way operation of the DC-DC multi-level converter is a critical requirement in electric vehicles. In comparison to the conventional configuration, the proposed one uses only two additional switches and a condenser. The voltage of the multi-level inverter (MLI) T-type is balanced over under defect conditions. The controller proposed reduces the power system's THD and maintains the quality of power. It has benefits like low switching losses, reduced THD, fewer filter needs and superior output quality compared with three-level MLI T-type. The capacitor voltage balance circuit is used to keep the three capacitors in balance. This results in a tidy, smooth, and efficient capacitor voltage balance between the capacitors. The proposed configuration is examined using a MATLAB/Simulink simulation model and results support the applicability of the converter.

The objective of this study was to address a knowledge gap by examining a less-explored domain and exploring the influence of business and political connections on innovation, specifically in the context of small and medium-sized enterprises (SMEs) in emerging markets, through the lens of internationalization. This study has specifically examined the importance of business and political connections and their potential impacts on the internationalization and product innovation of small and medium-sized enterprises (SMEs) in emerging economies. The proposed model was tested by collecting data from 110 small and medium-sized enterprises (SMEs) in the United Arab Emirates. The findings indicate a significant correlation between both business and political connections and the internationalization process of small and medium-sized enterprises (SMEs). Additionally, it was observed that the relationship between business ties and the internationalization process is significantly influenced by the implementation of a low-cost strategy.

Coffee has become an important part of any individual nowadays. The purpose of this study is to determine the marketing strategy for the coffee shops. This study uses several coffee attributes such as coffee type, sugar level, temperature, coffee beans, and frappe were analyzed by utilizing a conjoint approach. A questionnaire was utilized for this research which collected 500 respondents. Conjoint analysis showed that sugar level was the most important coffee attribute (43.013%), followed by temperature (37.75%), coffee type (14.701%), coffee beans (2.601%), and frappe (1.936%). Interestingly, cold cappuccino with Arabica coffee bean, less sugar, and with frappe was found to be the best combination for marketing strategy. This study can be utilized for developing the marketing strategy of coffee shops. By Understanding the relationship between the factors, coffee shops can find out which factors influence customers the most.

Although introducing a resale market to games may be beneficial for game companies, many are still hesitant to do so due to the additional investment required and the difficulty in quantifying the extent of these benefits. In this paper, we propose a consumer-behavior based loot box opening analysis framework with a resale market and systematically describe players' stochastic behavior through a loop of loot box opening and trading until the steady state. By carefully designing the simulation process, we found the optimal pricing strategy for game companies and demonstrated that this strategy can increase revenue by around 63.1% compared to the optimal strategy without a resale market.

This study investigates the use of Multi-Level Perspective (MLP), National Innovation Systems (NIS), and Socio-Technical Systems (STS) theories to understand national innovation pathways. Through synthesising this framework and associated theories, a comprehensive MLP framework for NIS is developed, designed to analyse the holistic innovation ecosystem by considering the socio-technical landscape, actor alignment, institutional roles, and knowledge flows. Implementing the framework provides critical insights into innovation pathways and potential trajectories within a national context. The framework’s adaptability allows it to fit various national ecosystems, making it a valuable concept for future research on national innovation systems. The findings highlight possible areas for improvement within any national innovation ecosystem, such as promoting academia-industry collaboration in R&D, improving knowledge flows, and expanding the pool of human resources for innovation. Using the developed framework as a case study can offer insights into Brunei’s innovation pathways and potential trajectory. The findings demonstrate the framework’s adaptability to specific national contexts, making it a valuable tool for future research on national innovation systems.

An omnichannel retail strategy integrates multiple channels, enabling customers to shop across all available online and offline channels at the same time. This strategy is required to overcome different obstacles that develop as a result of changes in the retailer's business environment. This study aims to provide an overview of the extent to which SMEs in Indonesia implement an omnichannel strategy. Omnichannel retail implementation in SMEs has shown positive developments in recent years. They recognize the importance of providing a consistent and unified shopping experience across multiple channels. In this context, the marketplace is essential for SMEs to expand their reach of customers in various locations. However, despite positive developments, there are still some challenges to implementing omnichannel for SMEs in Indonesia. These challenges include dynamic changes in consumer behavior, limited staff capabilities, and the mindset of SME leaders or owners.

Three-dimensional packing problems are important optimization problems with practical applications in various fields including manufacturing, logistics, and transportation. In this study, we focus on optimizing a multi-objective three-dimensional robotic packing problem. Our purpose is to simultaneously minimize both the processing time of the robot and the container's volume for a single packing task. To encode the packing solutions, we use the sequence-triple representation. Then, we calculate the robot processing time for each packing solution using the Rapidly Exploring Random Trees algorithm. The Non-dominated Sorting Genetic Algorithm II is employed to tackle this optimization problem. To examine the usefulness of the proposed approach, we conduct experiments using a 6-DOF robot manipulator. The results of our experiments illustrate the proposed algorithm can obtain a set of Pareto solutions, and a trade-off relationship exists between the processing time and the volume of the container.

In recent years, hairpin coil motors have been widely utilized to enhance the efficiency of electric vehicle systems. These motors are manufactured by bending hairpin coils. But this process leads to issues such as springback during the bending process. Since springback is significantly affected by the mechanical properties of the material, it is necessary to measure material properties in the manufacturing process line. This paper develops a system that utilizes the principles of eddy current to measure the impedance of materials and predicts their mechanical properties based on the eddy current-based measurements. Hardware components, including eddy current probes and measurement systems, were developed, along with an algorithm that utilizes machine learning to predict material properties. The results of this study demonstrate the feasibility of using the eddy current system to measure the mechanical properties of hairpin coils.

Effective modeling and monitoring of tool condition deterioration can provide technical basis for maintaining production efficiency and quality. Inspired by the need of tool condition monitoring in joining dissimilar materials, especially the micro friction stir welding (μFSW) process, we aims to model and monitor the spatial and temporal patterns in the dynamic tool wear propagation in μFSW. A hybrid hierarchical spatio-temporal model is developed for the time-ordered, high-dimensional tool surface measurement images to characterize the dynamic tool wear propagation in μFSW. Kalman filter is adopted to estimate the posterior distributions of the state variable (temperature distribution) and the error between the measured tool surface image and the predicted images. Regularized Mahalanobis distance is proposed to monitor tool wear progression. Numerical studies on three abnormal tool wear progression patterns demonstrate the effectiveness of the proposed spatio-temporal modeling method, as well as the timeliness, confidence, and power of detection. The method developed in our work is expected to facilitate early detection of abnormal tool wear progressions, reduce the efforts in manual inspection, and support smart manufacturing.

Although the application of AI has been frequently studied, it is unclear whether and when AI investments lead to true improvement of operational efficiency, which is a key factor in operations management. Specifically, we apply stochastic frontier analysis and GMM techniques to examine this impact and relevant moderators. By using machine-generated AI investments data from Burning Glass Technologies and Compustat’s data, we show that AI investments have a significantly positive impact on firms’ operational efficiency. Moreover, we confirm that this impact is stronger for firms with higher industry dynamism and higher R&D intensity. Drawn on the contingent dynamic capabilities perspective, which enriches the dynamic capabilities theory by considering a contingency perspective, we show this impact is continuous and stronger for firms in highly complex market environments. These findings provide valuable insights that make firms’ AI capability a more crucial asset in the intelligent machine era.

The drive for competitiveness in smart manufacturing compels organizations to embrace the circular economy (CE) within their industries. This emerging trend combines artificial intelligence with the latest digital technologies, particularly industry 4.0 technologies like the Industrial Internet of Things, Cyber-Physical Systems, big data analytics, and more. The goal is to offer an alternative to the traditional linear economy (take-make-waste). This research aims to present an architectural framework that utilizes I4.0 technologies for the adoption of the CE in various industry sectors. In doing so, we consider every component of the manufacturing process, including input and output stations, manual service centers, machinery, equipment, and others, all integrated within the cyber-physical system. Furthermore, smart technologies are integrated into this system to replace the linear economy model. Additionally, this article demonstrates how the 4R principle (repairing, remanufacturing, recycling, and replacing) plays a vital role in the transition away from the linear economy. Undoubtedly, the adoption of this approach will provide managers with the means to achieve sustainability and foster ongoing economic development.

Transport and Material Handling Systems (TMHS) are characterized by behaviors, properties, rules and restrictions which makes their representation challenging. The literature about representing and/or modeling TMHS is not extensive, despite its acknowledged importance. Several challenges to represent TMHS are identified and discussed in this paper, such as TMHS with several devices, restrictions, shapes of networks, places to visit, transport and handling activities to execute or communication with manual and automatic equipment. The synchronization and integration of transport and handling activities with the remaining processes of manufacturing systems is also one of the main challenges discussed. Finally, the authors suggest that this research gap should lead to the development of new models and techniques to represent TMHS in manufacturing systems – preferably based on a generic approach to meet the requirements of different organizations and TMHS. The authors have selected two research questions to structure the paper but further research is also necessary on this topic.

We consider decision problems involving the simultaneous optimization of conflicting criteria. In this context, we introduce the notion of anti-clustering. One looks to a partition of objects characterized by a high level of intra-group heterogeneity and a high level of inter-group homogeneity. This goal is in opposition with traditional clustering methods. In addition, it is related to the notion of fairness in clustering. We propose a first implementation based on a genetic algorithm and compare it to random algorithms on illustrative data sets such as the academic ranking of world universities. Tests are performed to fine- tune the algorithm’s parameters.

In dealing with emergencies, rational decisions are needed with limited time to reduce losses to death. Emergencies, which affect many public interests, require the participation of decision-makers with different perspectives to mask the lack of knowledge and experience of one decision-maker or a smaller group. Large-scale group decision-making (LSGDM) has become an exciting research topic in the last decade. Previous researchers have widely discussed the development and application of the LSGDM model. Time constraints and rational solutions are the main challenges for LSGDM in emergencies. This literature study explores LSGDM research trends in emergencies and provides new insights and opportunities for determining future research.

Many companies strive to incorporate Industry 4.0 into their strategies, considering it the upcoming major trend with new benefits. As it continues to evolve, industrial growth and competitive advantage must understand the key factors driving Industry 4.0's development. Despite the benefits of Industry 4.0, its impact on employment, wealth and distribution in developing countries is not fully understood. In emerging economies, companies often possess restricted capabilities and typically function at a considerable distance from the frontier. Furthermore, businesses, especially small and medium enterprises (SMEs), do not understand the tangible benefits of Industry 4.0. At the same time, the relationships of key driving factors could differ in the context of developing countries regarding limited infrastructure, workforce skills, or different regulations. This paper examines the interrelationship of Industry 4.0 maturity models driving factors. 16 driving factors were extracted from 30 references primary studies through a structured literature review and taxonomy development and grouped into 6 domains. By using expert opinions from developing countries, the study employs a linguistic fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method to quantify the interdependencies of each factor. This study found that although categorized as an affecting factor, strategy for I4.0 has the most relation to others. The findings presented in this study offer insights into the relative importance of I4.0MM driving factors, enabling policymakers, industry practitioners, and researchers to prioritize their efforts and allocate resources effectively as part of their adoption and growth strategies.

For several years, the sustainable development issue has encouraged businesses to see beyond a purely economic vision, based solely on financial criteria, by also integrating an environmental and societal dimension. Integrating sustainable development into its long-term strategy, a company ensures maintenance and longevity over time. The sustainable company must guarantee an environmental and social vision while pursuing at the same time its economic performance. Embedding the SDGs into business practice is often difficult to achieve. The purpose of this work is to identify how companies of different sizes evaluate a list of vital to businesses sustainability indicators at the national level (Greece). Using pairwise comparison matrices, the Analytic Hierarchy Process (AHP) determines how priorities in 27 sustainability indicators are ranked by experts at business level. Weights are calculated to quantify the overall importance of different sustainability indicators based on a questionnaire survey conducted to evaluate the progress towards sustainable development. The resulting prioritization depicts how companies, based on their size, evaluate sustainability indicators and provides a baseline for policy-makers to suggest appropriate business incentives.

In Engineering Procurement and Construction (EPC) business, project cost and time estimation are very critical tasks. When it comes to off-shore pipe-laying projects, external uncertainties, such as weather and sea conditions, deeply affect project execution and performance. Current practice in leading EPC companies is still lagging behind in the adoption of quantitative models to address uncertainties for optimal project planning and execution. We propose a Decision Support System (DSS) capable of estimating the time variability of pipe-laying projects, under the stochastic effect of multiple sources of uncertainty, and consequently the expected performance of different project configurations. A case study is used to demonstrate the feasibility and the added-value of the proposed approach. The considered system is composed of a logistics fleet supplying the required materials to a floating Reconfigurable Manufacturing System (RMS), responsible for the assembly and laying of the pipeline. In view of a growing adoption of digital and Industry 4.0 technologies, the proposed approach paves the way to further developments towards a real-time management and mitigation of unexpected operational conditions.

Globally, farmers are faced with the dilemma of supplying optimal water for crops amidst the ever-increasing extreme weather conditions. Optimal water supply to crops has both cost and crop productivity implications for farmers. New technological advancements have led towards the developing of smart irrigation systems which ensure the efficient consumption of water during irrigation, mainly by applying Internet of Things (IoT). This research considers three crop types namely, beans, chilli and potato, and their respective threshold soil moisture content values. The results show that when beans, chilli and potato were selected, the system issued a command to irrigate for soil moisture values below the threshold soil moisture content, and not irrigate for values above the threshold moisture content, respectively. Moreover, the use of machine learning will enable the system to reduce the need and the cost for extensive sensor network infrastructure, thereby improving on cost efficiencies reported on smart irrigation systems that incorporate IoT technology.

The anomalies in product manufacturing process are related to product defects, and the accurate detection of these anomalies is conducive to improving product quality. The feature-based control chart pattern recognition (CCPR) method has been widely applied to this problem. However, most of the existing feature methods only focus on the amplitude characteristics of the data, ignoring the structural characteristics and sequence relations of the data. A novel feature extraction and recognition method of control chart pattern (CCP) based on multi-delay weighted ordinal pattern (MDWOP) is proposed. MDWOP features integrate the amplitude and sequence structure characteristics of the data, and comprehensively characterize the complexity of CCP from different scales based on time delay parameters. An ensemble classifier recognition method based on multi-delay features is proposed to improve model recognition accuracy. Simulation results show that the average accuracy of the proposed method for eight small fluctuation CCPs is 95.44%, and is better than that of the single classifier method.

In many manufacturing processes, multiple machines operate in parallel, each with its own process control. However, this approach faces a practical issue where the number of active machines fluctuates based on inventory demand, resulting in insufficient data from some machines and reducing the accuracy of predictive models. Hence, our research aims to enhance the precision of predictive models. This paper proposes a novel framework that uses machine learning techniques to predict the quality of multi-machine production lines. The method proposed relies on prediction outcomes and employs filtering and merging techniques. It filters and combines relevant data from multiple sources for better predictive outcomes. The method was validated in an industry-academia cooperative chemical project where data from ten machines were collected and analyzed. The manufacturer's standards classify model predictions as excellent, usable, or unusable. Results show improvements in prediction results from (3, 3, 4) to (6, 4, 0) out of ten models, proving the proposed method's efficacy in establishing multiple predictive models with insufficient data. The study offers practical solutions and future research directions.

In this current era, manufacturing processes are highly capable of producing conforming products. However, due to wear and tear of the machining parts, the manufacturing processes do not remain stable and hence cause non-conforming products over time. For example, the machine's needle plays a significant role in adequately sewing garment products in manufacturing. When the needle tip is damaged, the flaws in sewing increase with time. In literature, such gradual changes over time are known as drift, while a sudden temporary change is known as shift. Generally, location control charts are used to monitor the processes' shifted mean. However, these charts are not widely discussed under the drifted setup; therefore, this study is intended to show the performance of location charts under linear and quadratic drifts. A simulation setup is designed to assess charts' zero-state and steady-state run length performance under normal and non-normal setups. Moreover, the proposed charts are implemented on a real-life dataset to highlight the stated proposal's importance.

Developments of superior measuring devices, sensor-based technologies, computerised or robotic record-keeping and cloud storage provisions offer data streams involving hundreds and thousands of variables for data-driven decision-making. Subsequently, surveilling high-dimensional (HD) processes has emerged as a vital topic. Statistical process monitoring of HD data streams is challenging because of inherent estimation problems of many process parameters without ample reference samples. Therefore, significant attention has been paid to designing effective, conveniently implementable monitoring schemes for HD processes in recent years. Some Phase-I and Phase-II nonparametric schemes have been introduced recently and can significantly serve the purposes in various situations. This paper comprehensively reviews distribution-free schemes to monitor HD processes and outlines their strengths and weaknesses with an extensive comparative study. Finally, some new directions of research are suggested. 

To solve the two common problems of simultaneous existence of qualitative and quantitative factors and the constraint experimental space in experimental design, a Maximum Projection Coordinate Exchange algorithm (MPCE) in normalized constrained space is proposed. Firstly, three typical constraints, necessity point constraint, disallowed factor combination constraint and critical area constraint, in engineering application of experimental design are defined and normalized. Then, a unified experiment space description method is presented to represent all types of constraints, such as analytical constraints and non-analytical constraints, and construct constrained experimental space. Furthermore, the maximum projection coordinate exchange algorithm is improved according to the constrained experimental space constructed. The improved algorithm can effectively solve the experimental design problem of the constrained qualitative and quantitative factors mixed. Finally, the proposed method is compared with the fast and flexible filling method. The effectiveness of the proposed method is verified by calculating evaluation indexes such as space filling and optimality criterion and analyzing simulation examples.

This paper examines key factors for effective Total Quality Management (TQM) implementation in service industries and investigates the resulting benefits. Utilizing a mixed-methods approach, data were collected via questionnaires and interviews with top management, supervisors, employees, and customers of a South African service company. Results show that most TQM principles were practiced in the organization, but full benefits were not realized due to limited top management commitment and low staff awareness of TQM principles. Recommendations include consistent customer feedback collection, clear communication of short- and long-term objectives to employees, and provision of necessary training resources. Implementing these suggestions will enable the company and other organizations (both service and manufacturing) to fully benefit from TQM and maintain a competitive edge in the market.

This paper aims to justify the importance of machine learning (ML) for the digital Supply chain (SC) in real-time. Disruption in SC is strongly affected by the COVID-19 pandemic. Worldwide, continuous lockdowns and shutdown of manufacturing plants have increased the stress on supply, resulting in disturbance amongst the demands and supplies, which increased the overall cost. Tracing the material and transparency in SC are current challenges for manufacturing organizations. Therefore, Blockchain (BC) can be seen as a solution to SC’s transparency, traceability, trust, security, etc. But whenever we talk about real-time records, information without integration of ML with BC-integrated SC is incomplete. ML develops the real-time authenticity factor model that incorporates Women’s empowerment. This mathematical model is easily integrated with the digital SC procurement problem to estimate real-time procurement costs. This developed ML-based authenticity factor will be a new milestone for optimizing the SC cost in the digital era. This proposed research develops the authenticity factor through machine learning. This model will reduce the errors from SC and make the system more resilient.

This paper aims to analyze the development status of overseas warehouses in cross-border logistics under the global economic environment, and the mutual driving force between logistics and overseas warehouses, combined with the One Belt and One Road strategy. An algorithmic search integrated with meta-analysis was developed to retrieve China’s overseas warehouse sustainable development strategy from digital literature databases. This research discusses existing problems in developing cross-border logistics overseas warehouses and puts forward specific countermeasures.

The agriculture supply chain (ASC) carries inherent risks because of its complexity, internally and externally. It is necessary to bring more efforts and studies in risk management within the agriculture supply chain. Meanwhile, digital technologies have been implemented in various agriculture supply chain functions for improvement and transformation. The question is how digital technology has implemented risk management in the agriculture supply chain. Hence, this research reported the conceptual model of digital technologies for risk management in the agriculture supply chain. The study started with a literature review to explore the topic in extant literature from academic databases, followed by digital technologies platforms’ data collection and analysis. Next, we use the case study of the agriculture supply chain's risk management based on digital technologies in local government in Indonesia. Finally, we propose a generic conceptual framework for the risk management process in the agriculture supply chain, which implements digital technology.

Rail freight transportation is one of the essential modes of freight transport. It is regarded as a cheap means of transportation and is appropriate for moving containers, agricultural items, and bulk materials. However, for a number of reasons, rail freight transit in Thailand has been constrained. One of the most worrisome issues from the perspective of the clients is the ability to track and trace the shipments. Due to the lack of efficient information flow among all parties involved in transportation, customers are unable to track down shipments and determine when they will arrive at their destination. This study used information flow diagrams to show where the information was lacking. As a case study, the State Railway of Thailand (SRT) practice is utilized to analyze the issues and make improvement recommendations. Finally, in order to enhance the information flow for rail freight transportation, a traceability system for rail freight has been developed.

Last mile delivery as a challenging area of supply chain is facing growing challenges for e-commerce firms. One of the most important aspects of these challenges is related to the sustainability of last mile delivery. This study has targeted customers’ awareness and eagerness to play an effective role for sustainable last mile delivery. The paper has also focused on the capabilities of blockchain technology for enabling the tracking and tracing of the contributions of the customers through NFT (non-fungible tokens) tokens. The paper has designed surveys to examine the awareness and tendency of customers for sustainable last mile delivery. It has been found that there is a significant gap when it comes to completely understanding the blockchain and its potential benefits in terms of reducing CO2 emissions concludes. The results show that a high proportion of respondents who indicated a willingness to delay and consolidate deliveries if offered an NFT token incentive. Finally, it has been concluded that there is high potential for blockchain technology to promote sustainability in the last mile delivery industry.

Following the Covid-19 pandemic, the adoption of Smart Green Supply Chain (SGSC) practices has drawn a lot of attention due to its potential to improve sustainability and operational effectiveness. Due to the crucial role that Indian SMEs play in the GDP of the nation, it is essential that they adopt smart and sustainable supply chain practices. This comes as a serious challenge to many SMEs that are mostly run by families with limited resources. This motivates us to identify the factors influencing the adoption of SGSC in Indian small and medium-sized enterprises (SMEs) and understand their eco-system. Through an extensive literature review and consulting experts from the domain, the study identifies several important factors and sub-factors influencing the adoption of SGSC. To understand the cause and effect among these factors and the sub-factors we used Decision making trial and evaluation laboratory (DEMATEL). These factors and their cause-and-effect diagram can help policymakers, practitioners, and researchers develop strategies to promote the adoption of SGSC practices in Indian SMEs.

This study utilized a hybrid scholarly network analysis by combining citation-based and text-based approaches to understand the conceptualization, measurement, and antecedents of operational resilience in the supply chain risk management literature. Specifically, we employed a Bibliographic Coupling Analysis in the research cluster formation stage and a Co-words Analysis in the research cluster interpretation and analysis stage. Our analysis reveals three major research clusters of resilience research in the SCRM literature, namely (1) supply chain network design and optimization, (2) organizational capabilities, and (3) digital technologies. We portray the research process in the last two decades in terms of the problems studied, commonly used approaches and theories, and solutions provided in each cluster. We then provide a conceptual framework on the conceptualization and antecedents of resilience based on studies in these clusters and highlight potential areas that need to be studied further. Finally, we leverage the concept of abnormal operating performance to propose a new measurement for resilience. This measurement overcomes the limitation of most current measurements that focus on the resistance or recovery stage - without capturing the growth stage.

In the field of civil engineering, there is a growing demand to bridge the gap between academia and industry by equipping students with advanced digital technologies and fostering thinking creatively. A chatbot has emerged as a potential solution to alleviate the challenges of civil engineering higher education by supporting students’ autonomous learning. Although the recent increase in available lecture videos has made it possible to build a domain-specific knowledge base, it remains unclear how to enhance the Question Answering (QA) performance on lecture video dataset that exhibits spoken language using limited train datasets. This study aims to investigate the potential of lecture video-based QA datasets and propose a training strategy by evaluating the impact of linguistic features, dataset quantity, and train order on QA performance. The experimental results show that the lecture video-based dataset has the enough potential to be used alone, but when its size is small, it is recommended to train the large-scale benchmark dataset first, even if the linguistic features are different.

The introduction of Industry 4.0 has boosted the use of new technologies. Product and service companies are aiming for improvements in operational efficiency and profitability. In this context, collaborative robots (Cobots) are a promising technology offering safe collaboration with humans in the workplace. Higher education must provide skilled personnel in these new technologies. One of the significant university offerings is the practice laboratory, where theory is applied, and relevant projects are developed. The Bern University of Applied Sciences in Switzerland has introduced practical activities using Cobots in their laboratories and student projects for local manufacturing companies focused on Industry 4.0 activities. At Tecnologico de Monterrey in Mexico, a private university, programs under new curricula in its educational model (Tec 21) redirect efforts toward digital transformation. This work analyzes the Swiss and Mexican models. It proposes a method to adapt the activities in Mexico that emphasizes using new technologies as part of the digital transformation.

To improve maintenance management processes, it is crucial to identify and overcome any potential obstacles to adopting computerized maintenance management systems. That is why this study focuses on examining the common barriers to adoption. By doing so, organizations can streamline their maintenance operations and improve overall efficiency. The survey and questionnaires were used as research methods in this study. The information was gathered from engineering employees over a month. Following exploratory factor analysis, the number of variables in the model was reduced from six to three. According to the study, perceived usefulness, relative advantage, and perceived ease of use are three factors that influence the adoption of computerized maintenance management. People will embrace technology if they believe it will provide useful results and help them perform better. It is suggested that the company expand and provide more technical support so that people can easily see how the computerized maintenance management system can help them. The company should also ensure that the computerized maintenance management system is simple to use and outperforms the old one in terms of benefits and results.

This paper briefly addresses the terms Online, Virtual and Remote Laboratories in modern engineering education and evaluates online labs from different angles. The main contribution of this work is collecting the feedback of both the instructors and students and comparing their responses for the purpose of evaluating the different approaches of lab work delivery mode and effectiveness in the context of electrical engineering education. Despite their advantages, the results attained from this work show that online labs do not easily replace face-to-face, whether for the hands-on and in-lab work experience or the soft skills of teamwork and effective communication.

This study investigates user requirements for the design of a Learning Analytics Dashboard (LAD) tailored for assessment in maritime simulator training. User requirements for LAD components and visualization elements were examined. Further, perceptions towards the integration of LAD in performance assessment was explored using Likert-scale questions. Data was collected from three Nordic maritime institutions. Situational awareness emerged as the most important component of a maritime LAD, with heat maps preferred for visualization. Both teachers and students have positive perceptions towards the utilization of LAD. Disparities in user requirement and perception towards LAD use across universities, study levels, and simulator modality experience were explored. These insights are pivotal for the advancement and tailoring of LADs in maritime simulator training contexts.

The ODeL education has emerged to be the fastest growing method of teaching in the world today. In South Africa, it has been adopted by several universities and the universities of technology. The University of South Africa (UNISA) is the largest full accredited institution of higher learning to offer ODeL in South Africa. The new programme qualification mixture (PQM) in engineering, which has been implemented requires change in teaching methods, study materials and improved virtual learning environments. The Engineering Council of South Africa (ECSA) standards and requirements need to be met by this new curriculum. This paper evaluates the new PQM based on ECSA requirements. A comparison is made between the module of the same content in the new PQM and the old PQM. We conclude by paying attention to new approaches to be utilized and further identify the challenges and possible solutions to this new PQM.

Engineering teachers play an important role in engineering education to develop the next generation of engineering human resources. In this sense, the education and training of engineering teachers are important. The recent development of artificial intelligence (AI), especially generative AI, has impacted many industries, including education. Thus, this paper aimed to explore existing research focuses and trends in the field of education and training of future engineering teachers in the age of artificial intelligence (AI). Based on scholarly publications obtained between the years 2018 and 2023, a bibliometric analysis has been performed, which includes analysis such as keyword co-occurrence analysis and thematic-based content analysis. The analysis in this paper is performed using bibliometric software named VOSViewer. The results from the analysis have identified five research hotspots in this field based on keyword clustering, with each hotspot discussed. Finally, this paper concludes with some elaborations on limitations and future work.

The purpose of this study is to investigate the relationship between entrepreneurial motivation, entrepreneurial attitude and entrepreneurial intention among students from science and technology college who have taken innovation and entrepreneurship courses and to determine whether or not entrepreneurial attitude influences the relationship between entrepreneurial motivation and entrepreneurial intention. Research findings: Entrepreneurial motivation influences entrepreneurial attitude and entrepreneurial intention, entrepreneurial attitude has a significant effect on entrepreneurial intention and entrepreneurial attitude has a complete mediating effect on the relationship between entrepreneurial motivation and entrepreneurial intention. In other words, in order for entrepreneurial education to enhance students’ entrepreneurial intention, it is far more effective to enhance students’ entrepreneurial attitudes compared to enhancing their entrepreneurial motivation. It is recommended that entrepreneurial education in schools can be achieved by acquiring entrepreneurship knowledge, providing students with a support system, participating in entrepreneurship internship activities, hearing stories of entrepreneurial failures or seeking professional advice, writing entrepreneurship plans, participating in innovative entrepreneurship activities or competitions and establishing good interpersonal relationships, etc. to enhance students’ entrepreneurial attitude.

This research investigates the distribution network model of a fruit trading company in Thailand, with a specific focus on the problem of excessive travel distances and vehicle requirements in the company's existing distribution system. Linear programming and optimization techniques are used to improve the distribution network model and to satisfy the daily demand. Results are compared between the current distribution scenario and the optimized model. The computational analysis reveals a significant 21.8% reduction (802 kilometers) in total traveling distance and a 33.33% decrease in the number of vehicles required. Additionally, a comprehensive cost analysis is proposed, incorporating fuel costs, CO2 emissions, and overtime expenses, which were previously overlooked. This research offers valuable insights into the potential benefits of optimization, including cost savings and environmental impact reduction, providing a practical template for managing distribution networks, reducing reliance on ad hoc practices, and fostering a sustainable business model to enhance competitiveness in a challenging market.

This paper presents a multiple-case study that aims to standardize process improvement in the control cabinet industry and identify areas requiring significant enhancements. The study provides a comprehensive understanding of observed processes, facilitating efficient process design and waste identification. A standardized process survey is developed and applied to a third control cabinet manufacturing company for validation. The findings reveal limited coverage of the control cabinet industry in existing studies. Eleven challenges faced by control cabinet manufacturing companies are identified, including a shortage of skilled workers and limited space. The analysis highlights low evaluations in all seven fields of action. Recommendations include implementing material flow to improve efficiency and identify bottlenecks. The study introduces a systematic analysis framework for evaluating production sites in the control cabinet industry, contributing to operations research and practical applications. It offers a comprehensive framework for analyzing and optimizing control cabinet production processes. Future research should focus on assessing applicability to small and medium-sized enterprises in other industries with similar inefficiencies.

Accurate forecasting of stock market indices plays a crucial role in investment decision-making. The study investigates the application of the Holt-Winters forecasting method using two optimization algorithms, namely Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA), to enhance the forecasting performance of the Philippine Stock Exchange Index (PSEi). The Holt-Winters method is a popular technique for time series forecasting, capable of capturing level, trend, and seasonality components. GA and CSA are metaheuristic optimization techniques that can improve the accuracy of the Holt-Winters forecasts by optimizing the model parameters. The optimization models are assessed using historical PSEi data spanning several years. The experimental results show that GA outperformed CSA in the goal of increasing the Holt-Winters forecasting method's accuracy. The comparative analysis shows that both algorithms have promising results with little divergence in the outcomes, though there are differences in how well they can optimize the model parameters and recognize the intricate patterns in the PSEi data. The results imply that employing GA in the Holt-Winters forecasting model can be a useful strategy for stock market forecasting, empowering investors to make more informed choices.

The optimization of electricity forecasts is crucial for enhancing the efficiency and performance of power systems. This paper presents a novel hybrid approach that integrates the Cuckoo Search Algorithm (CSA) and Genetic Algorithm (GA) to optimize electricity forecasting. The CSA, inspired by the breeding behavior of cuckoo birds, excels in global exploration, while the GA, based on natural evolution principles, provides effective local search and exploitation capabilities. By synergistically combining the strengths of both algorithms, the proposed hybrid approach aims to improve the accuracy, convergence speed, and robustness of optimization processes in electricity forecasting. This CSA-GA fusion technique leverages the CSA's efficient exploration and the GA's refinement capabilities to offer enhanced solutions for electricity forecast optimization. Experimental evaluations conducted on real-world electricity datasets from Luzon, Visayas, and Mindanao grid data of the Philippines demonstrate the effectiveness and superiority of the hybrid approach, showcasing its ability to achieve improved performance and obtain near-optimal solutions in various electricity data analysis tasks.

In this paper, we focus on the characteristics of improvement targets generated by two distinct types of Data
Envelopment Analysis (DEA) models: the conventional additive (ADD) model and the least-distance DEA model. DEA is a mathematical programming technique employed to evaluate the relative efficiency of decision making units (DMUs) that have multiple inputs and outputs. One of the notable aspects of DEA is its ability to generate improvement targets for each inefficient DMU to achieve efficiency. Thus, the concept of the least-distance DEA model has been introduced to generate the closest improvement target that closely resembles the evaluated DMU and can be easily achieved. To evaluate the effectiveness of the different improvement targets, we compare the improvement targets generated by the ADD and the least-distance DEA models. This analysis is performed using a time-series dataset comprising 86 retail companies in Japan. The results of the numerical experiments indicate that the improvement targets generated by the least-distance DEA model exhibit superiority in achieving efficiency for the inefficient DMUs. These findings shed light on the potential advantages and effectiveness of the least-distance DEA model in improving the efficiency of DMUs.

Electric vehicles (EVs) have the potential to reduce carbon emissions and significantly improve urban air quality. However, the current lack of charging infrastructure poses a major challenge for EV drivers. Therefore, building new charging stations is essential for the mass adoption of EVs. We address the location and capacity planning problem for EV charging stations under uncertainty. The locations of EVs with an uncertain driving range and uncertain charging demand are given, as well as locations at which charging stations can be established. The problem is to determine the locations and capacities of the charging stations such that the cost of establishing stations and building charging capacities plus the fictitious cost of assigning vehicles to stations are minimized and the stations’ capacities and the vehicles’ driving ranges are respected. We formulate the problem as a twostage mixed-integer linear programming (MILP) model and present a branch-and-Benders-cut (BBC) solution algorithm. We present computational results for a case study comprising 1,079 demand nodes in Pennsylvania, indicating the superior performance of the BBC algorithm.

Many research studies have shown the negative changes of global climate is related to the mass consumption of fossil fuels because of exchange for different energies in last decades. Human activities are mostly occurred in buildings where the most energy spent to provide human comfort and facilitate their needs. Different to the built environment and local climate, energy consumption in buildings is varied from their locations. Global Status report 2020 have shown the energy consumption in buildings is accounted 35% of global final energy consumption. Reference to the Energy End-user data report in Hong Kong 2022, 66% of total energy was spent in residential and commercial sectors which occupied total 93% electricity and 72% towngas & LPG. Although these figures are improving when compared to past years, the optimization and continuous improvement for energy efficiency is still to mitigate the climate change issue. This research paper is the study of how the energy efficiency being improved through the intelligent asset management and operational decision support, in react with human behaviors for building automation and optimization of energy performance in buildings.

This article strives to study the direct effects of managers’ disruptive skills to survive in a digital revolution assessed indirectly by organizational agility. In addition, the interactions of digital orientation are also assessed. A conceptual framework was devised to investigate the aforementioned correlations. Five hypotheses were developed and tested with data from 100 respondents who were affected by digital transformation and with positions one level above the manager level or higher.  SmartPLS is used for analysis and to generate 500 bootstrap samples. This study makes three distinct contributions to the existing literature on digital transformation. First, digital disruptive skills have a direct impact on both organizational agility and organizational resilience. Second, leadership abilities have a strong beneficial impact on organizational agility and resilience across all three digital disruptive talents examined in this study. Finally, digital orientation has little effect on organizational resilience when it comes to digital disruptive talents.

Companies operating in the automotive industry are investing a lot of resources in complaint analysis to improve quality. This is primarily driven by the irregularization. of safety-related complaints. As of now, there has been a dearth of research pertaining to the organization and examination of subjective and irregular complaints. This study presents a novel dashboard that enhances the accuracy of classification and facilitates corporate decision-making through the application of natural language processing (NLP) to categorize customer complaints. Initially, a systematic complaint classification framework is developed through the establishment of clear definitions and standardization, employing qualitative methods. Following this, a customer complaint classification algorithm is composed utilizing a keyword framework grounded in NLP for the construction of dictionary. The matching process with related patents is used to help companies make informed decisions and the final results are provided by dashboards. This study holds significance as it can contribute to the establishment of technology development strategies through the identification of trends in technology complaints, examination of related patents, and analysis of the characteristics exhibited by these patents.

This study systematically reviewed the literature on MASS operations related technologies and identified 15 core technologies. A structured survey was designed to assess the perceived Technology Readiness Level (TRL) of the identified core technologies on a one to nine TRL scale based on the European Union (EU) framework. Based on the average rating scores by industry experts and academics, we find that the Global positioning system (GPS), Satellites, Very high frequency (VHF) radio, and the Radio Detection and Ranging (RADAR) are perceived as most ready for MASS. However, On-board robotic systems, Intelligent detection algorithms, Augmented reality (AR) and virtual reality (VR), and Vessel health management sensors (VHMS) are perceived as least ready. Further, the readiness timeline of the relevant technologies are predicted, which indicates that the least ready technologies are expected to reach TRL 9 within the next 10 years.

Forecasting emerging technologies is vital for companies to gain a competitive edge. Patents are the most frequently used database for forecasting, as the majority of novel inventions are patented. The previous studies have attempted to predict how promising individual patents are, focusing on the characteristics of individual patents so as to successful identify and manage the promising patents in firms. Technology group predictions are also needed to provide practical help in establishing technology development strategies for companies. However, only a few studies have focused on identifying groups of patents in forecasting emerging technologies. In this study, we tried to forecast emerging technology fields using the relationship between patents. First, we constructed patent citation network to represent the relationship and then employed graph clustering with Graph Neural Networks which could sufficiently reflect the relationship in network. Lastly, we utilize the characteristics of each technology group to predict which size would increase. The proposed methodology aims to assist companies in identifying and prioritizing technology groups with substantial potential for development and innovation, enabling efficient research by providing clear research directions.

Managing technological innovation is vital for firms to respond to rapidly changing environment, for which technologies need to be linked to businesses and industries. Previous studies have created concordance tables that link patent classes to industry classes for connecting technologies and industries. Despite their value, however, existing concordance tables have several limitations. First, they primarily concentrated on industry-patent association, failing to consider business, which is an important part of innovation strategies. Second, they lack regular updates, containing outdated information, and fail to propose update strategies. Third, they rely on the statistical method, which inevitably requires the intervention of qualitative approaches. In order to address those limitations, this study proposes a novel approach to create a concordance table that connect industry classes (Korean standard industry classification codes), patent classes (cooperative patent classification codes), and trademark classes (trademark similarity group codes) by applying a deep learning method to patents and trademarks firms in target industries. This is one of the earliest attempts to introduce a concordance table linking industry, patents, and trademarks simultaneously, enabling insights into technology-business-industry co-evolution and innovation strategies.

Various quantitative approaches are being studied for patent text analysis, but they face difficulties due to the huge volume of patents and the complexity of individual patent descriptions. In previous studies, researchers mainly adopted a rule-based approach to distinguish necessary information. Nevertheless, adopting and implementing such rules require a significant amount of time and effort. Therefore, we proposes a methodology to automatically extract only the information that needs to be addressed from patent text through Large Language Model(LLM). For this purpose, we pre-defined the technology information commonly used in previous studies and constructed prompt-specific answer sheets to perform transfer learning on LLM. Through this study, researchers can automatically classify various technology information existing in patents. This study represents one of the initial attempts to apply LLM in the field of extracting technology intelligence from patent data. By adopting LLM for technology intelligence extraction based on patent texts, the paper presents the advantages and potential challenges that are expected to arise. In practice, it is possible to reduce the resources to extract technology intelligence from patents by applying a LLM.

The manufacturing industry has experienced significant changes with the advent of Industry 4.0 and its evolution into Industry X.0. This study explores the key enablers, challenges, and implications of Industry X.0, which includes advanced technologies such as digital twin, edge computing, 5G connectivity, and quantum computing. These technologies drive growth, efficiency, and sustainability in manufacturing, but stakeholders must also address the associated risks and challenges, such as cybersecurity threats and potential workforce impacts. This study provides case studies and examples of Industry X.0 applications, including smart factories, predictive maintenance, digital twinning in manufacturing, and cross-sectoral applications in smart cities, healthcare, energy, and defense. Practical recommendations for stakeholders include investing in digital transformation, reskilling and retraining employees, implementing cybersecurity measures, adopting circular business models, and fostering collaboration across supply chains. Moreover, the study emphasizes the importance of addressing ethical implications, such as data privacy and security, while highlighting areas for further research. Overall, Industry X.0 presents significant opportunities for companies to thrive in the new digital economy but requires careful management and strategic planning.

The urbanization and industrialization have exasperated the air-pollution threats to human health. The Sustainable Development Goals (SDGs) promote the decent equilibrium between human performance and wellbeing in a sustainable way. Our research focuses on diverse spatiotemporal analytics for realizing the concentration and dispersion episodes of PM_2.5 monitored in central Taiwan area. Contrasting to traditional approaches, we devise several video processing and machine learning analytic techniques. The shot boundary detection algorithm is applied on the time series of air-pollution maps to segment the pollution episodes. Each episode is a short video which is then condensed in a single gait-energy image for partitioning the episodes into homogeneous clusters. We found the air-pollution episodes agglomerate to several salient patterns which are suspiciously related to terrains, meteorology, and anthropogenic activities in the studied area. To probe into the causes, a query-by-sketch interface is developed to assist the user to specify the interesting air pollution pattern with associations to terrains, human constructions, and weather scenarios. The proposed spatiotemporal analytics discloses the context of air-pollution episodes and benefits to strategy-making of sustainable development.

The online sensing techniques and computational resources provide abundant passive data for data-driven decision-making in a Manufacturing Industrial Internet. Artificial Intelligence (AI) models, such as Deep Neural Networks, effectively improve manufacturing efficiency, quality, and flexibility. However, the shifting distributions, imbalanced classes, and multimodal variables hamper the performance of the trained AI models. Inspired by active data generation through Design of Experiments (DoE) and passive observational data collection, we propose a systematic framework to improve the training data preparation for the online updating of AI models. The proposed framework integrates active high-dimensional data generation and labor-efficient passive data acquisition in Hierarchical Contextual Bandits. A shared low-dimensional latent space is generated by Aligned Variational Autoencoder for controlled data generation and sampling. The data generation and sampling process is optimized to improve the learning performance of AI models. The framework demonstrates its effectiveness and efficiency in two real case studies in Aerosol Jet® Printing process and Fused Deposition Modeling process.

Large amounts of data are generated at various stages of the manufacturing system and stored in traditional ERP systems. Industry 4.0 technologies, such as big data hosted on the cloud, can be integrated into the existing ERP system. Analysing these data and using the insights obtained may help the manager monitor and control real-time decisions to improve manufacturing performance. However, one of the concerns was the threat to the privacy of data and the security of the ERP system if big data is hosted on the cloud. This would be worth investigating how the company address the same. This research develops a research framework using earlier literature and investigates the applicability of big data and cloud computing for a real-life manufacturing company which implemented this system while addressing the above concerns. We use case study methodology due to the lack of such study in academic literature. Further, using a case study, we find that big data and cloud computing together improve real-time information availability (intangible benefits), which results in enhanced decision-making and hence improves plant performance (tangible benefits).

Facing the fierce e-commerce market competition, businesses must have a comprehensive understanding of the market structure and their competitive landscape. Yet, pinpointing close competitors in the swiftly evolving global e-commerce environment poses a substantial challenge, especially for medium and small-scale merchants. To solve this fundamental problem, we develop a two-layer network market structure and a fast competitor search algorithm based on our inverted index dictionary (IID) data structure for 1.3 million e-commerce stores with 0.5 billion products (1.5 billion SKUs). Our fast competitor search algorithm enables businesses to identify their top 100 direct competitors in an average time of 163 ms using Arm-based Graviton2 processors. Owing to its linear computation complexity, our search algorithm can complete the entire global e-commerce search in mere seconds. Our market structure and search algorithm will serve as a practical and efficient tool, offering valuable insights into the market competition. This will empower businesses to make real-time decisions, enhancing their competitive edge in the dynamic e-commerce market.

This presentation outlines the collaboration between an AI engineer and a humanities scholar in developing innovative of AI multimodal movie analysis software The Möbius Trip, and an intelligent solution startup, The Möbius Trip LLC. By combining expertise in film theories, intercultural models, and AI technology, we have established a distinctive methodology and software that pioneers digital methods to uncover cultural patterns and cinematographic trends in movies. Powered by big data, The Möbius Trip offers a comprehensive architecture encompassing character tracking, gender recognition, emotion display, object detection, shot scale, camera angles, framing, shot length distribution, color analysis, music, and dialogue recognition. Our unique filtering process involves movie pulverization, labeling, re-assemblage, and holistic-granular dynamic reading, generating blueprints for movies, actors, franchises, and movie genres. We detail the key steps of the conception, from design to commercialization, and provide an overview of the software's functions. With a corpus of over 1000 movies, Möbius Trip will make a significant impact on the film industry, as well as relevant academic fields such as intercultural communication, sociology, LGBTQ+ studies, and everyday movie enthusiasts.

This current research introduces an innovative digital multimodal approach for assessing gender equality representation patterns on-screen. While the Bechdel test has traditionally been used to evaluate gender representation in films, it has faced criticism for its simplistic and outdated nature, often failing to capture the true essence of a movie's content. In this study, we present a sophisticated alternative to the Bechdel test, leveraging our Multimodal Audiovisual Analysis AI toolkit, The Möbius Trip. By analyzing a diverse corpus of over 1000 movies worldwide, our approach incorporates various dimensions, including characters' screen time, emotional displays, dialogues, camera work, props, locations, and metadata. Through this comprehensive analysis, we are able to unveil a nuanced pattern of gender portrayal based on movie genres, directors, time periods, and countries. By delving into these granular aspects, our methodology allows for a deeper understanding of how gender representation is depicted on-screen, going beyond the limitations of the Bechdel test.

Drug repositioning has emerged as a strategic solution to tackle the significant challenges associated with new drug development, with the selection of appropriate disease candidates playing a critical role. Various preceding studies have actively employed both patent data and academic papers as primary sources of information for the analysis of disease relationships and the proposal of potential drug repositioning candidates. However, due to the inherent differences in the objectives served by these sources of information, they inevitably provide differing data. This discrepancy underscores the need for a detailed comparison of these sources to determine their respective characteristics and benefits for the process of drug repositioning. This study, therefore, aims to provide a comprehensive comparison of these information sources to optimize the efficiency of drug repositioning efforts. Employing three key metrics – Relevance, Completeness, and Accuracy, we conducted an in-depth evaluation of dementia-related patent data and academic papers. This study is expected to contribute to increasing the success rate and efficiency of drug repositioning by helping to select the most effective information sources that best align with the objectives of drug repositioning.

In today's technological era, digital platforms have become an integral part of our daily lives and a crucial component of business operations. The adoption of new technologies, acquisition of expertise, and active contribution of employees and management are ongoing processes necessary for digital transformation, which is imperative for businesses of all sizes. Additionally, this transformation requires a corporate shift in communication and social network usage. This research investigates the resource-based view's relationship between intellectual capital, organizational ambidexterity, digital transformation, and performance outcomes. The study surveyed 209 companies in Taiwan, and the data's validity was assessed through structural equation modeling (SEM) and confirmatory factor analysis (CFA). The findings indicate that intellectual capital plays a significant role in initiating digital change, but organizational ambidexterity can reduce this effect. Digital transformation can also act as a mediator between intellectual capital and performance outcomes.

This study attempts to bring together the pieces of literature on organizational strategy, innovation, organizational culture, personality, cognitive patterns, and organizational learning. Organizational structure, strategy, and culture influence the organization's approach to learning and task completion. There is a paucity of literature discussing the effect and relationship between these organizational variables and learning. This study attempts to synthesize the literature and suggests that learning occurs differently in organizations employing cost leadership, differentiation, and modular, architectural innovation strategies. This literature is founded on well-developed theories and research from the past. It theorizes the relationship between learning loops, such as single loop, double loop, and deutero-learning, and strategy, innovation, culture, personality, and cognitive styles. Based on these relationships, hypotheses are developed to provide organizations with a comprehensive understanding of how learning depends on strategy, innovation, culture, personality, and cognitive style. This article contributes to the literature on organizational behavior by conceptualizing the relationship between learning, strategy, innovation, culture, and personality.

In professional software interfaces, text content usually plays a crucial role as a fundamental component. This study investigates the relationship between the text feature in interfaces and the interface aesthetics. We analyzed the impact of font size, letter spacing, line height, luminance contrast, font weight, and text area proportion on the interface aesthetics. The Text Layout Aesthetic Model and the Text Luminance Aesthetic Model were developed to capture the relationships between the first five text features and the interface aesthetics. We proposed a computational framework that integrates these two models with text areas, employing machine learning techniques to depict the connection between the text feature and the interface aesthetics. As the result, this model demonstrates a good fitting and a generalization performance. This work contributes to an improved accuracy in the assessment of the text aesthetics for professional software interfaces.

In high-tech startups in the seed stage, grappling with limited staffing resources, the expeditious discernment of an individual's potential as a pivotal element of the institutional structure is of paramount importance. The present research enabled a substantial diminution in evaluation duration in contrast to traditional methods by harnessing the power of Deep Learning for transcription, automatic speaker differentiation, and extraction of speech attributes during the Deep Learning procedure. Furthermore, utilizing the attributes excavated via Deep Learning for textual analysis through Principal Component Analysis (PCA), it became feasible to glean quantitative propositions from a broader range of perspectives hitherto unobtainable. Despite the concentration of this study on attributes pertinent to dialogic velocity, it elucidates the feasibility of accruing more multifaceted suggestions through the extraction of additional attributes, such as vocal pitch, from the internal output of Deep Learning and associating them with analysis findings of PCA.

Because we live in a manufactured world, empowering the manufacturing workforce is crucial. People-centric
Production (PCP) in Industry 5.0 places people at the center of the industrial transformation, offering benefits such as bottom-up innovation potential, enhanced shop floor employee engagement, as well as increased acceptance, pace and value-added of digitalization efforts. However, there is a lack of understanding how to envision, implement and assess PCP. This conceptual paper presents an assessment tool for guiding the implementation of PCP in manufacturing, identifying three design criteria - work, learning, and culture - and a total of 13 action areas for PCP. Foundational elements such as health and safety, ergonomics, and remuneration are also highlighted. The evaluation approach based on the EU Common Assessment Framework and Industry 4.0 maturity assessments is proposed to enable continuous monitoring and improvement. The applicability of the tool was evaluated within an industrial case study, with future research focusing on longitudinal effects and the applicability of the tool in small enterprises. Implementation challenges related to the interdisciplinary nature of PCP are identified, highlighting the need for multi-stakeholder collaboration structures.

This paper presents a Systematic Literature Review (SLR) on the Safety Culture Maturity Model (SCMM) article. SLR was conducted on 52 articles published in the last 22 years, from 2001 to 2022. Data were collected by analyzing abstracts, article content, and keywords. Analysis was carried out by classifying articles into publication years, publication types, measurement methods, and the validity and reliability of the SCMM measurement tool. The result is that many studies need to improve in testing the validity and reliability of measuring instruments. Validity and reliability assess how accurate and consistent the measuring instrument is. The literature review results can be used as input to develop a better SCMM tool in future research.

Micromobiles provide a sustainable and space-saving alternative for mobility in urban areas. Although the global market potential for micromobility products is estimated at 400-500 billion USD in 2030, micromobility concepts are only occasionally finding their way into everyday urban life. It is therefore questionable, if  the currently existing products meet the requirements of an urban society. To ensure that products are clearly customer-focused, situational customer needs analyses form the basis for decisions about product features and technical solutions. Therefore, a mixed methods approach adapted to the requirements of urban mobility will be used to identify the basic needs of potential urban mobility customers using the example of Germany. This mixed approach combines qualitative and quantitative research. Specifically, experts and potential customers of micromobiles are interviewed qualitatively and the derived results are validated and prioritised in two quantitative studies. The generated data forms a basis for the subsequent steering of the technical solution finding.

Following the path of Servitization and providing Product-Service Systems (PSS) have emerged as promising strategies for numerous manufacturing companies. By integrating PSS into innovative business models that prioritize use over ownership, these companies aim to gain competitive advantages and achieve continuous revenues while customers profit from mitigating high investment risks. However, to fully exploit these benefits, it is crucial to establish effective and efficient operations. Specifically, the task of operative service delivery planning to ensure the usability of the products poses challenges for many companies and presents opportunities for optimization. Despite the criticality of this task, it is still often conducted by dispatchers relying on their experiences. This paper introduces a new approach to decision support in the operative service delivery planning within the context of PSS. The proposed approach entails a hyperheuristic optimization algorithm that generates solutions integrated into a simulation model.

The gradual decline in Malaysian female entrepreneurs has been a subject of discourse in the literature. This decline has been traced to the series of challenges that these entrepreneurs face which has resulted in the relatively low performance in their businesses. This study sampled female salespeople aged 50 years and above who have not used any self-created videos in their personal sales job. We interviewed 13 female salespeople between February and April 2023. The study finds several non-engagements amongst these entrepreneurs. The entrepreneurs in question attributed their non-engagement to various issues which they consider challenges. Thus, this study concludes that the non-engagement with technology of female salespeople aged 50 years and above accounts for the relatively low performance and gradual decline of Malaysian female entrepreneurs. From a theoretical perspective, the Dynamic Capabilities of the firms of these Malaysians over 50s female entrepreneurs are not being exploited fully. This implies that in view of changing environmental conditions, these organizations are not properly configuring their strategies and resources to achieve the needed competitive advantage necessary to enhance their productivity and business performance.

This study explores the applications of circular economy principles at the product development phase to enable circular product development. In the climates where light rains are common in comparison to heavy downpours, it   is impractical to use of a pair of rainboots since they are difficult to maneuver in and are not compact.  To address this issue, a waterproof shoe-cover concepts and product were developed combining circular economy goals and product development process. This manuscript demonstrates how a cost-efficient and environmentally friendly shoe-cover product was developed. It also demonstrates how circular economy concepts were vital for ensuring cheap material cost and to create an eco-friendly shoe cover. The product development process paved an organized path to create a successful product. The findings of this case study reveal how to repurpose an existing product at the maturity of its performance by the application of circular economy principles and the product development process, to develop a new real-life product in a systematic way. The findings of this study enable enhancing circular entrepreneurships and self-sustained business ecosystems in different regions.

Given the increasing concerns with sustainable development, this paper proposes a practical framework to streamline the process of sustainability reporting of small and medium enterprises (SMEs), based on the updated Global Reporting Initiative (GRI) of 2021. The proposed framework consists of a simplified step-by-step guide composed of ten steps divided into two stages. The development of the step-by-step guide followed four phases which included identifying the main stakeholders, understanding the business, analyzing GRI 2021 guidelines, and preparing the company's sustainability report. The proposed framework allows an SME to inform its stakeholders about its economic, social, and environmental impact performance, following the guidelines of the GRI Standards 2021. The use of this framework is illustrated in the case of a Portuguese SME textile company.

This study investigates the potential to apply a five-step framework to re-engineer the specification processes in a logistics service company. Logistics service customers have complex requirements for a lot of different services that are often solved for each customer with a customized solution, which complicates the sales to integration process. The applied framework is originally developed for product and production companies and the implementation of the framework has previously shown potential to decrease lead time and lower errors in quotations. First, the available literature on mass customization, specification processes, and the logistics service industry is evaluated. Afterward, the framework is briefly described, and a case study is conducted to see the potential of re-engineering specification processes in the logistics service industry. The case study investigates potential solutions and develops four scenarios that can automate the specification processes to different degrees. In conclusion, the framework can be applied, and it is a starting point for understanding the problems and developing a potential solution for a more standardized specification process in the case company.

Policy makers are increasingly aware of the need to promote and attract users towards sustainable mobility. The perception that users make of available alternatives, such as the railway system, is gaining relevance. The present work focuses on user perception regarding the need to promote investment to improve social performance of railway travel. This work is based on an open European level survey on railway user satisfaction, applied to Portugal, providing a unique perspective on user’s perceptions, according to their socioeconomic background. Overall, we found strong influence of socioeconomic context towards universal access to railway services. Users from peri-urban areas tend to prevail, and younger age ranges use railway more frequently than older age ranges. The study also presents valuable insights into the need to consider vulnerable population segments in policies for railway use in Portugal. Further research is required on the perception of environmental and energy related issues for railway travel.

International shipping is a crucial focus for decarbonization efforts due to its 2% contribution to global CO2 emissions. While awaiting the readiness of alternative marine fuels, operational approaches like Just-In-Time (JIT) Arrival are expected for emission reduction. However, aligning stakeholder interests and scaling up abatement measures in the complex value chain pose challenges. Despite the recognized benefits from supportive literature, widespread implementation of JIT remains limited. This study provides a comprehensive evaluation framework for port authorities to assess the applicability of JIT strategies and practical insights for major transshipment hubs like Singapore to maximize JIT benefits. Through literature review analysis and a case study on Singapore, historical data on arrival patterns, anchorage usage, berthing windows, and port turnover times are examined for different route types. These insights inform the creation of scenarios for quantifying benefits, and designing incentivizing strategies to align shipping lines under different commercial models. The findings suggest that the success of JIT implementation depend on prioritizing the right issues in the local context, and establishing necessary evaluation metrics with a centralized digital system to maximize achievable benefits.

The rehandling of export containers has become an important factor hindering the efficiency of automated container terminals. As a result, container terminal yard export container management has emerged as a key research area in port management. This article proposes a novel management mode to reduce container rehandling time and improve loading efficiency. Firstly, we predict the order in which export containers leave the yard. Then, we use the predicted results and a heuristic pre-marshalling algorithm to reshuffle the export containers. Finally, we use real data from an automated container terminal to generate a large number of examples to verify the feasibility and effectiveness of this management mode.

It is vital that both the hard-side comprising of tools and techniques as well as the soft side which includes ‘respect for people’ are simultaneously implemented and sustained for successful lean transformations. Operational excellence through such lean transformation can be achieved only when employees are equipped with the necessary knowledge, skills, and attitudes (KSA). This study presents the approach to develop a comprehensive methodology known as People Value Stream Mapping (People-VSM) to enable the evaluation of employee KSA development status. The approach expands on the traditional Value Stream Mapping (VSM) concept to identify and visually communicate KSA achievement across different stages of career development using a variety of new symbols. The application of People-VSM is demonstrated using a hypothetical case study of an entry-level employee who must develop the necessary KSAs to advance problem-solving capabilities. The utility of the tool in assessing training program efficiency, identifying high-potential employees, and optimizing training processes to enhance overall employee development is demonstrated to elaborate its usefulness for true lean transformations.

Recently, there has been a growing interest in smart factory implementation among manufacturing companies. Transforming traditional manufacturing systems into smart factories through digital transformation offers the potential to enhance productivity, optimize resources, and ultimately reduce costs. However, the current existing digital transformation maturity assessment models often fall short in providing clear answers to various questions that arise after the maturity evaluation. While these models do deliver insights on areas that may require improvement for the target companies, they do not offer clear solutions to subsequent inquiries. As a result, this presents significant challenges for resource-limited SMEs, making it particularly difficult for them to comprehend and effectively implement smart factory practices. Therefore, in this study, we aim to establish a digital maturity assessment framework utilizing the company's information systems and derive the essential technological elements required at each maturity stage. By doing so, we expect that SMEs will be better equipped to apply digital transformation to their manufacturing systems and quickly adapt to the era of the Fourth Industrial Revolution.

The construction industry has been a major source of global carbon emissions. Concrete, the most commonly used building material, consumes a large amount of natural resources while also emitting a large amount of carbon dioxide. Continuous overharvesting of natural resources to produce cement and aggregate for concrete would eventually deteriorate the ecosystem. There is an urgent need to switch to a greener production method for concrete. Given the large amount of oyster shell waste generated annually due to poor management, they have become appealing alternatives to the construction industry with numerous benefits, including preventing contamination due to shell piling up at seashores, utilising calcium-r