With the increased popularity of open innovation (OI), we hypothesize that if the epistemology is open, harmonious, expansive, regulated with global - local metrics, positive non-reductionist co-evolutions of (eco)systems may develop. However, in line with the Capability Maturity Model, there is a need to improve absorptive, multiplicative and relational capability/capacity. As such, it is critical to develop sparks for knowledge-transfer, in diverse entrepreneurial channels and ecosystems, simulating pockets of interconnected- multi-channel conventional-augmented-metaverse realities-ecosystems. The goal of the study is to work towards sustainable investments across United Nations Sustainable Development Goals, in relation to the Information Architecture Society’s 5 pillars. Requirements engineering involves reviewing literature on open innovations, generative systems, multi-disciplinary optimization, and sustainable investment. This leads to derivation of a research model. To increase capability and capacity, sustainable investing games, as reflective and analogous mediators/scaffolds are proposed. Hopefully, over time, differentiated criteria and weights can be derived, applicable across co-evolving business models and contexts, for the development of self-adaptive but co-managed socio-economic-technological generativity and sustainability.

In startup companies, it is important to understand and assess the abilities that are required to become the core competencies of the organization. However, startups have limited resources, so they need an easier technique to measure the match with the culture and traits of the company. To do this, it is first necessary to consider what characteristics of the organization's core competencies. After conducting action research and analyzing meetings at a high-tech start-up company managed by Mr. Nakaya as CTO, we found that the ability to tolerate uncertainty, such as the proficiency to create hypotheses without being limited by immediate facts and methodologies, is a skill of great value, especially to the company. We also found the characteristics of those who have these abilities and those who do not appear in conversations by using correspondence analysis, or principal component analysis (PCA). Furthermore, using the PCA results, we aim to create a framework that can be used universally within the company that conducted the analysis.

Customer satisfaction is insufficient. It applies to all service industries including train services. Apart from weather conditions and safety issues, the challenges faced by train services are improving passenger comfort, sense of well-being, and emotional satisfaction. How to understand and satisfy the customer emotional needs is critical. Conventional methods such as survey and interview sometimes bring shortcomings. Hence, this study proposes the integrated approach of text mining, Railqual, Kano model, and Kansei Engineering (KE) in train services. Text mining is inserted in the KE methodology to refine the more representative Kansei words and service attributes experienced. The finding shows that there were 8 final Kansei words, namely, clean, extraordinary, comfortable, spacious, modern, friendly, cool, and cheap. Related to critical train service attributes, there were 3 items i.e., comfortable temperature in train, politeness of staff, and good quality meals served in train. Surely, the continuous scheduled air conditioner maintenance, training “dealing with people” for staff, and food supplier evaluation should be prioritized.

A constant development of modern technologies will gradually affect all areas of human activity. The use of various ICT technologies will be absolutely inevitable in the future. However, a little attention has been paid so far to clarifying the impact of these technologies on the human mental stress, especially due to the difficulty of measuring and evaluating. The purpose of this paper is to review the current literature whether there are studies clarifying the impact of assistive technologies on mental stress or if there is still a gap in understanding the cognitive perception. The paper describes methodological process of literature review where 66 articles from PubMed, Science Direct, Web of Science and Scopus databases were screened. The review showed, that there is only a limited number of relevant papers (3 papers) focused on mental stress in connection with Industry 4.0 and assistive technologies and thus there is still a room for elaborating this area further.

Kitchen appliances are essential to accomplish cooking tasks efficiently. Advancements in technology have led to changes in the needs and expectations of the users of commonly used kitchen appliances. Thus, this qualitative research aims to investigate the user experience and identify the user needs for common kitchen appliances (microwave, cooktop, oven, and dishwasher) by conducting semi-structured interviews with users who frequently use the appliances. Results of the thematic analysis showed that UX factors such as informativeness, comfortability, and safety were prominent sub-themes across all kitchen appliances. Based on the findings, we presented design recommendations on how the product can be improved.

Quality and Innovation play a fundamental role in helping organizations secure sustainable competitive advantage. However, the nature of their relationship has seen contradictory results. This ongoing research project seeks to relaunch the debate on the relationship between TQM and Innovation, doing so from a novel perspective that considers the cultural orientation of an organization as the key success factor in mediating the relationship between TQM and Innovation. It sets to understand if the creation of a cultural orientation to Quality supports the development of Innovation capabilities. In a first report of this project, the outcomes of this work are the review of the literature on the relationship between Quality and Innovation, the proposal of a conceptual model and the establishment of a series of hypothesis to further study their relationship.

The building sector consumes a sizable amount of global energy. Globally, developers are interested in reducing the cost of the operational energy of buildings. Green building certification has been initiated to promote the optimization of operational energy use and sustainable development. The Leadership in Energy and Environmental Design (LEED) certification guidelines provide a rating system for buildings to obtain green certification. This manuscript presents a case study that was carried out to estimate the potential operational energy savings in an office building construction project carried out in a tropical climate. A baseline design for the conventional building was carried out, based on the guidelines given in ASHRAE 90.1. To improve the energy savings, a green design was carried out, by following LEED guidelines, focusing on obtaining the LEED-Gold status. The consumption of energy by each equipment was calculated using Integrated Environmental Solution (IES) simulation software for a period of 365 days for the baseline and the proposed green designs. According to the findings, 23% of the electricity energy can be saved from the baseline design.

With Senate Bill 100, California’s policy goal of 100% zero-carbon energy supply by 2045, solar power has become a growing energy supply for residential and commercial locations. Solar power from photovoltaic systems can aid consumers in lowering their energy bills as well as assist utility operators by decreasing grid demand. The purpose of this paper is to model the benefits of photovoltaic energy generations with energy storage systems considering residential consumer behaviors. For proof of concept, the city of San Jose will be used as the location to demonstrate the expected behavior of this system. Energy storage potential will be analyzed through a variety of lenses such as energy generation, net present worth, and savings to investment ratio.

Nowadays, commercial building sector as one of the major contributors of energy consumption, is seeking for ways to achieve energy efficiency. In particular, solar energy generation system is a feasible option especially in the tropical region due to adequate sunshine hours. This study aims to investigate the technical and economic aspects of a solar energy system installation at a smart commercial building in Malaysia, where the building's lighting system is fully supported by solar energy and its operation managed via an intelligent building automation system. A case study is performed to determine how the installation can contribute towards the energy savings and to determine the return on investment (ROI). The analysis outcome indicates that 50% of energy savings can be achieved for the lighting system that is fully supported by solar generated energy, which translates to a 0.56 Tons of CO₂ reduction. The ROI of the installation is 11.13 years. In conclusion, solar energy generation system is deemed suitable for the reduction of energy consumption in the tropical region. Some indication of future works is also briefly discussed.

This paper presents a framework for portfolio selection using the mean-variance model utilizing Australian fintech company stocks before and during the COVID-19 pandemic. The investment pool consisting of fintech companies undergo a series of criteria to be determined. Mean-variance model is applied to identify the optimal portfolio using equally likely historical return estimates. The market was used as a benchmark to compare the portfolio performance. Back-test pre and during COVID show that most portfolios can outperform the benchmark in terms of returns while most portfolios also have a higher risk than the benchmark. The findings of this study may provide an alternative portfolio selection framework for any investor type considering the financial technology sector.

Manufacturing companies faces increased product variety, which has an impact of the production’s performance in terms of e.g. increased costs, decreased quality and poor delivery performance. In this article we study the impact that product variety has on the costs of quality in flow production by a case study set in the chemical industry. Impact from increased product variety on flow line production has only received little attention in literature, yet the case study reported in this article indicates, that the increased product variety may have a significant impact on the costs of  quality in this type of production, and that the costs of poor quality is higher  for products produced in low volumes (C products) than for products produced in high volumes (A products). In the case company the specific costs of quality for each product was not known before this study, and the information will provide the company a basis for optimizing the product portfolio, which will lead to increased output from production and thus improved contribution margin.

Additive manufacturing (AM) has attracted extensive research attention, due to the flexibility and capability to deal with additive manufactured (AM ‘ed) parts with complicated geometries. Benefiting from the recent development of material technology, manufacturing control tools, and integrated platforms, powder bed fusion (PBF) based AM has been successfully extended to build free form metallic parts. A more comprehensive understanding of the powder processing thermos mechanical metallurgical characteristics correlation is still lacking for the metallic AM. The metallic AM process involves non-uniform temperature distributions and rapid thermal cycles that result in microstructures featured with porosity and anisotropy. The different microstructure features critically affect the mechanical properties of the AM'ed parts. It is necessary to establish optimal parameters’ combination that provides predefined microstructure in an AM process to achieve desired mechanical properties of the AM builds. The AM’ed parts qualification is a requirement to assure accurate and repeatable builds. First, this manuscript demonstrates the use of parameter design approach for simulations and experimental validation to establish optimal parameter combinations to develop a qualification record. Second, it presents a framework illustrating how to perform feedstock metal powder production, AM of parts, qualification, and reuse of material in an integrated circular economy supply chain.

A new type of shared battery cabinet for e-bikes is emerging in China, enabling e-bike users to conveniently replace their low-power battery with a fully charged one outdoors. In such an e-bike battery swapping system, the location of the shared battery cabinet is crucial because it affects the system’s operation and user experience. This paper solves the problem of locating the battery cabinet considering the travel habits of riders and the change of battery status in the cabinet over time. This problem is modeled as an optimization model for a p-median location problem. A genetic algorithm incorporating system simulation is proposed for solving the problem. The algorithm’s performance is verified by comparing it with the results of the commercial solver on small-scale problems. Experiments on a real-world case are also conducted.

Due to the rise of innovative plastic waste management, Producer Responsibility Organizations (PROs) develop environmental and economic policies such as the Extended Producer Responsibility (EPR) to improve efficiency in waste management and contribute to the well-being of the environment while performing such activities. With the various depots for waste collection points, different tools such as the Vehicle Routing Problem (VRP) is frequently applied to generate cost-efficient routes along with other objectives. The study focuses on developing a single-objective single-period VRP model that extends the current developments of VRP by considering the waste collection sites or nodes as decision variables in setting up the system and considering wait times in specific nodes. It aims to provide a cost-efficient route with a system design that indicates which nodes must be opened or closed for operations, providing a flexible and well-rounded model through added node state options, which is not considered by past VRP models. With this, the model is able to provide an optimal route, minimized cost, decision to wait, and decision on which collection points are open during waste collection.

As the demand for transportation increases so does the need for efficiently managing the available transportation resources. Railway transportation in specific provides a tempting alternative for transportation due to its reduced effect on the environment, lower cost and reliability. One of the most important assets to manage in the railway is the rolling stock. The Rolling Stock Rotation Problem (RSRP) aims to the utilization of available rolling stock efficiently to reduce operating costs and satisfy multiple restrictions. The Rolling Stock Assignment Problem (RSAP) is a subset of the RSRP. The RSAP aims to assign the available rolling stock to a specific timetable that is run on predetermined routes. In this paper, a new mathematical model is introduced to solve the Rolling Stock Assignment Problem. The model was implemented on two phases. First the model is tested and verified on a small case study of the Alexandria City Tram El-Raml network. In the second phase, the model is implemented on a total of 65 instances from which 45 were solved to optimality.

The traveling salesman problem (TSP) is widely known as one of the most important NP-hard combinatorial optimization problems. In this study, we consider the robust traveling salesman problem (RTSP) under a min–max regret criterion with interval travel costs. The RTSP aims to find a tour that minimizes the difference between its objective function value and the optimal value when the real scenario is known in advance. We examine four methods, including a Benderslike decomposition method, a branch-and-cut algorithm, a fixedscenario method, and an iterative dual substitution method. For the iterative dual substitution method, we discuss several possible implementations based on different mathematical models for the classical TSP. We further propose a new heuristic approach which we call the edge generation algorithm. The experimental results show that the proposed edge generation algorithm achieved superior performance compared to that of all of several benchmark methods for all the tested instances.

Starting with the first mobile networks developed, the frequency channel assignment has become a significant problem due to the limited number of licensed frequencies and cost-related concerns. The minimization of the number of frequencies assigned has become the main objective of the frequency channel assignment problems, and today this problem is applicable and relevant for wireless networks as well. In this study, we focus on routing and frequency assignment models for wireless mesh networks and propose an integrated approach that combines these two aspects of frequency assignment problems. We modify our approach with respect to different interference models such as protocol-based or SIR-based interference. The integrated model is run for different sizes of randomly generated networks, and the results are compared with the sequential approach proposed in the literature. The impact of the size of the network and the interference model on the number of frequencies assigned are investigated.

This paper addresses a new variant of Pickup and Delivery Problem with Time Windows (PDPTW) for enhancing customer satisfaction. In particular, a huge number of requests is served in the system, where each request includes a pickup node and several delivery nodes instead of a pair of pickup and delivery nodes. It is named Pickup and Multi-Delivery Problem with Time Windows (PMDPTW). A mixed-integer programming model is formulated with the objective of minimizing total travel costs. Computational experiments are conducted to test the correctness of the model with a newly generated benchmark based on the PDPTW benchmark instances. Results show that our proposed model is able to solve small-size instances. Alternative approaches for solving larger problems are proposed for future research.

The complexities of the planning and scheduling activities in a lens manufacturing company lie in considering the monthly forecast demand for thousands of Stock Keeping Unit (SKU), line & track loading preferences, minimum & maximum inventory coverage for the Distribution Center (DC), the lot & formation changes at the line, planned and unplanned downtime, diopter to track configuration, etc., making manual planning & scheduling very tedious and challenging. In this paper, an integrated Distribution Requirements Planning & Scheduling (DRPS) solution was proposed to address these challenges by mapping and decomposing the manual planning & scheduling activities into various integrated modules, consisting of Order Management, Planning & Planning Scheduling Engine, and Report and Data Integration modules. This solution is currently being used in the manufacturing plant and it acts as a platform for ‘what-if’ analysis for the evaluation of the demand fulfillment plan across the distribution centers considering the real operational constraints. It also improves the planner’s productivity by reducing the planning effort from 3 days/week to 2-3 hours/week and eliminating human error in the planning process.

We consider the problem of scheduling jobs on a batching machine to minimize maximum lateness. Two batch processing modes, serial batch (s-batch) and parallel batch (p-batch), are considered. We first describe the exact algorithms for the s-batch and p-batch scheduling problems.We then present a robust extension to classical problems in which uncertain processing times are represented by a budgeted uncertainty set. Given a solution, we demonstrate efficient algorithms to evaluate the solution under its worst-case scenario for both s-batch and p-batch robust scheduling problems. For the s-batch robust scheduling problem, we propose an exact algorithm that has the same time complexity as the classical problem. For the p-batch robust scheduling problem, we design a heuristic algorithm that provides both upper and lower bounds on the optimal value. Computational results show that the proposed heuristic performs satisfactorily.

Market Kurly is the first South Korean online grocery retailer that guarantees same-day, overnight shipping. About 1.6 million customers place 4.7 million orders and add 3 to 14 products into a cart. The company has sold almost 30,000 kinds of various products, including foods, cosmetics, kitchenware, and even flowers. The company is operating multiple dry, cold, and frozen fulfillment centers in order to store and ship these products. Due to the scale and complexity of the fulfillment, pick-pack-ship processes are operated in batches, so the batch planning for the processes is the critical factor in overall productivity. This paper introduces a metaheuristic optimizer based on the genetic algorithm; it aims to group similar orders to minimize the total number of distinct products. The method produces streamlined plans, up to 13.5% less complex than the actual plans carried out. Furthermore, our digital-twin simulations show that the optimized plans can reduce 3% of operation time for packing. The method implements a multithreading design to support the company’s warehouse systems in near real-time, finding a solution within 7 seconds on AWS c5.2xlarge instances.

The printing industry is not exempt from the dynamic challenges facing these demands. Due to these demands, managers are compelled to introduce initiatives to their operations to produce high-quality products and services in order to remain competitive. Lean manufacturing has been found to be a reliable solution in various sectors, even though some companies have been unsuccessful in implementing lean manufacturing system in their operations. This study aimed at investigating the effect of a lean manufacturing system in the printing industry. The study adopted a qualitative research approach. Eighteen employees participated in the study. Purposive sampling was conducted due to the nature of the study. In-depth face-to-face, semi-structured interviews and systems observations were used for data collection purposes. The study found that human factors, poor material management, equipment inefficiency and layout were causes of waste. An average of 50% of overall equipment effectiveness (OEE), 4.5% downtime due to printing machine breakdown and poor layout contributed to waste generated by the printing system. The significance of the study demonstrated the importance of the implementation of lean manufacturing in various industries in order to eliminate waste and bring about operations improvement.

Predicting throughput times is of particular interest to production planners to schedule the production flow or communicate reliable delivery times to customers. Most established prediction methods are based on general assumptions, expert knowledge or simple statistical techniques. With the increasing use of data mining in production management, it is possible to provide more sophisticated predictions of throughput time. However, current research often does not describe the application or locate the particular prediction approach within the time and task structure of Production Planning and Control (PPC). Therefore, this paper aims to develop a systematisation approach to classify prediction models within the PPC task structure. To this end, applications along the order fulfilment process are first defined and then elaborated. A systematic literature review is conducted to classify current throughput time prediction approaches within the previously described application domains. In a case study, the application possibilities of throughput time predictions based on the provided systematisation are demonstrated, and differences in data availability and prediction quality are highlighted.

In practice, manufacturers and retailers jointly make decisions by capitalizing on decision interactions while respecting the carbon emission tax and subsidy determined by local governments. Though studies have been published to address the joint decision-making, they involve only a very few of the important supply chain decisions due to the problem complexities. In this study, we investigate a comprehensive joint decision-making of a manufacturer and his independent retailer with considering both carbon emission tax and subsidy. Per the decision interactions, we analyze the decision-making of the manufacturer and the retailer as a Stackelberg game. The game model developed, by nature, is a mixed 0-1, non-linear, and bilevel programming. In view of its complexity, we further develop a nested genetic algorithm (NGA) to solve the model. Numerical examples demonstrate the applicability of the game model in facilitating supply chain members to jointly make decisions and the robustness of the NGA. With Sensitivity analysis, we shed light on several important managerial implications.

At present, the trend of artificial intelligence (AI) for product innovation is becoming widespread, and many manufacturers enhance their products with AI. In the process of AI-enabled product production and sales, manufacturers must make decisions on not only the retail prices of AI-enabled products but also whether to reprice their original products. This study investigates the optimal decisions for manufacturers, considering the degree of consumers’ AI preference and the substitutability of the two products. We find that manufacturers often need to reprice their regular products after launching AI-enabled products to gain maximum benefits. In addition, this study also explores the impact of price sensitivity and cross-price sensitivity on the manufacturer's optimal pricing decisions. The results of numerical analyses yield managerial implications for product manufacturers engaged in AI enhancement.

The COVID-19 pandemic and trade frictions impact the continuity of supply chain (SC) operations. In the volatile environment, big data analytics (BDA), a key technology for storing data and predictive analytics, has become an important tool for mitigating SC vulnerability. Based on the literature review, this paper identifies six influencing factors and four vulnerability drivers for mitigating vulnerability, and employs Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) to explore the influence pathways that BDA mitigates SC vulnerability. The findings show that BDA can influence knowledge acquisition and strategy formulation by improving the forecasting capability of enterprises, which facilitates strategy implementation and ultimately mitigates vulnerability. Furthermore, with the support of BDA, resource redundancy addresses vulnerability from supply-side, higher production level and efficiency reduce vulnerability from demand-side, and rational SC design alleviates vulnerability from operation-side.

Multiple purchases are necessary for fresh food distribution supply chain management due to the high uncertainties of demand. To optimize the procurement policies of decision-makers (i.e., minimizing the total purchasing cost), we develop a two-stage stochastic programming (TS-SP) formulation in which the purpose of the first stage is to determine the optimal quantity of advance purchase, while the second stage aims at making appropriate replenishment purchase decisions. However, a challenging task in the problem is that the involved scenarios might fail to be generated via the conventional Sampling Average Approximation (SAA) method because the demand may not follow any known probability distribution (or the demand prediction results may be highly inaccurate). To overcome this issue, we propose a novel prediction-model-based scenario generation method that can handle any type of demand uncertainty scheme and incorporate multiple trained demand prediction models. Extensive numerical experiments on real datasets reveal that the TS-SP approach is superior to the 12 considered predict-then-optimize methods in terms of overall purchase cost and stability. Furthermore, the advantage of using the TS-SP approach is gradually enlarged as the number of involved SKUs or considered scenarios increase.

The quality of perishable products, such as fruit and vegetables, will decrease over time. Cargo should be placed in reefer areas where the temperature is close to the optimal product temperature. This research develops a Refrigerated-Container Loading Problem (R-CLP) model for loading ventilated cargo of fruit and vegetables. The model was developed using constructive heuristic method. The researchers conduct a simulation using Computational Fluid Dynamics (CFD) to obtain reefer temperature distribution data. The purpose of this research is to produce a decision support tools to optimize product quality. The numerical experiment results show that the model can minimize costs and quality loss. The numerical experiment results also show that using ventilated cargo have a better results than closed cargo for loading fruit products. The fruit produces heat as a result of respiration which must be removed from the cargo. Ventilation holes facilitate cool air from reefer entering the cargo and hot air from fruit exiting the cargo.

Through the use of a causal loop diagram (CLD), the primary purpose of this study is to understand the contribution of several variables in influencing the performance of the halal industry. Referring to the framework of Forrester’s National Model, five CLDs belong to five different subsystems used to depict the relationship among the different variables in the halal industry. The population and consumption subsystem are the first CLD. The production subsystem, the halal certification process subsystem, the export-import subsystem, and the government subsystem are the second through fifth CLD. The findings presented in this study raise several important questions that should be answered by performing a comprehensive simulation of various scenarios in the future.

The dichotomy between efficiency and effectiveness is ever timely. They are closely related to Occupational Safety and Health (OSH) management, but there is still little awareness. The literature has extensively covered the analysis of interventions for OSH improvement. The focus however is mostly on the efficiency of activities, but less concern is given to the real effectiveness of the processes. This implies that the network of actors developing the related activities is rarely discussed. This work starts analysing OSH actors’ roles and functions by dwelling on the national OSH systems of different European countries, where institutional effects strongly affect OSH practices. Common characteristics of national OSH systems have been identified as minimal requirements to make a national OSH system work properly. Data have been classified into a structured dataset and the resulting database makes available a huge amount of data on national OSH systems, which previously was very hard to retrieve in practice. Being an explorative study, further analyses and more accurate comparisons among countries will surely entail new opportunities for future policy and practice improvement.

This study explores perceived safety of autonomous ferries by public in the Vestfold and Telemark region of Norway. There is a greater focus on automation of ships in this region, largely due the developed of the Yara Birkeland project in the region. The prevalence of existing ferry services in the region makes it natural to knot the technological development together and explore prospects of autonomous ferries. Using structured web-surveys, we found that respondents prefer lower degree of automation, mainly ships with Decision Support System (DSS) and semi-autonomous ships controlled from a Remote Control Center (RCC) with reduced crew on abord. Respondents are skeptical towards fully RCC controlled as well as fully autonomous ferries. Among the respondents, male sample, those very good at swimming as well as technology, and those using ferry once every month are relatively optimistic about safety of autonomous ferries. On the other hand, older people perceive autonomous ferry more risky than younger ones.

Health condition assessment aims to distinguish between the normal condition and various fault types, which plays an important role in machine health management. While, several health condition assessment methods based on statistical features can only provide limited information from some aspects. To address this issue, a novel health condition assessment method based on the zoom permutation entropy is proposed in this paper. Firstly, the multiresolution analysis of the multiple wavelets is applied in zoom permutation entropy to synchronously decompose the original vibrational signal into various time series with multiple resolutions. Then, the dynamic complexity of each time series is calculated by permutation entropy, which can extract features with robustness and high calculation efficiency. On this basis, a health condition assessment has been developed to automatically distinguish between the normal condition and various fault types of the machine. The simulation results show that zoom permutation entropy has better feature extraction ability compared with other methods. The experimental results show that the proposed method outperforms existing methods in assessing the health conditions of the machine.

The increasing volume of ocean traffic, global warming and climate change have raised the urgency of the shipping industry to prioritize their efforts in sustainability. The shipping industry is currently under significant pressure to comply with stricter environmental regulations in order to operate in a cleaner and greener approach. Hence, this study aims to promote a sustainable shipping industry that is able to strike a balance between their economic, social and environmental performance. As such, this research intends to focus on the sustainability of the shipping companies by introducing green shipping practices (GSP) to help shipping companies to achieve sustainability based on the triple bottom line framework. GSP is not only limited to one aspect of the business operations, but it also includes the entire shipping industry. This study conceptualizes GSP for shipping companies within six dimensions, which are company policy and procedure, shipping documentation, shipping equipment, shipping service providers’ cooperation, shipping materials and shipping design for compliance. This research also introduces the role of rational culture as a moderator in facilitating sustainability that yields business benefits.

Dynamic positioning (DP) systems have been an excellent solution for maintaining positions of vessels and heading them in the offshore industries. Many researchers have developed different methods manage the risks related with DP systems. Evidence has shown that the occurrences of position losses of DP vessels are not seldom. This paper the focuses on risk analysis of different DP systems in different applications and uses the statistical methods to estimate DP failure probabilities based on incident data collected by International Marine Contractors Association during 2010-2018. Regression analysis and correlation analysis are conducted for quantifying the risks from different causes, as well as identifying the significant causes and relationships between main- and secondary causes. At the end, the paper also presents some general suggestions for safe operations of DP systems in different applications.

This paper proposes a data-driven framework of resilience evaluation for power systems under typhoon disasters. A typhoon scenario generation model based on the recurrent neural networks (RNNs) and long-short term memory unit (LSTM) using historical typhoon data are presented. Under generated typhoon scenarios, the resilience of different components of power systems and the entire network are evaluated. We apply the proposed framework to an instance of IEEE-13 bus system to demonstrate its feasibility, and the results prove that our method outperforms in accuracy of resilience assessment than the traditional methods that based on hypothetical typhoon scenarios or single historical typhoon scenarios. Our proposed data-driven framework and the resilience evaluation results can provide system managers with guidance on power system planning and resilience enhancement.

We are moving towards an era of automation and technological revolution with Artificial Intelligence (AI) at its core. There is no doubt that AI has created commercial value across various industries such as e-commerce, security, engineering, etc. Thus, the paradigm of AI is understood as something that is making our lives easier, but is it as simple as it looks? This paper looks at some challenges and risks of AI through the lens of ethics and law. The risks are multifaceted and bring about chaos in society if no strict measures are taken. By looking at various ethical and legal concerns we will look at the current ongoing legislation at the European Parliament regarding law and AI.

Small and medium enterprises (SMEs) in Indonesia have played a significant role in absorbing labour, increasing the number of business units and supporting household income. PT XYZ is a medium-sized informal industry engaged in convection services. Convection business is a line of business that produces clothes in bulk that are tailored to market needs. Several factors such as the intensity of lighting or light and the experience of the workforce can be factors that affect the results of sewing production in convection services. In this study, an experimental design was carried out using a three-way ANOVA and analyzed to determine whether there was a relationship between independent variables (light intensity, work experience and shifts) and the dependent variable (sewing production results). Observations were carried out for 6 days with 80 workers with work experience and different lighting conditions for 12 hours of work with 2 shifts. Based on data processing, it can be concluded that there is an effect between light intensity and work experience and there is an interaction between light intensity and work experience on the number of sewing final products.

Automated container terminals are complex systems with multiple interactions and high dynamic characteristics. Integrated scheduling is expected to improve the overall efficiency. However, traditional optimization approaches such as mathematical models and meta-heuristic algorithms failed to tackle high dynamics. A reinforcement learning approach based on the scheduling network method is presented in this paper. Network-based heuristic rules are introduced into the action space, and a novel state definition that integrates local and global information about the scheduling problem is proposed. Group training and group validating strategies are adopted to test the generalization ability. Numerical experiment results reveal that the proposed approach converges to a high level and maintains good performance on unseen instances. Compared to the selected heuristic rules, the proposed method achieves 2.37% and 6.06% better results on training and test instances, respectively.

This study proposes a quantitative evaluation model for comparative analysis of installed solar energy (SE) capacity development in various geographical alternatives. Performance indicators (PIs) are defined to capture SE development status. Then a combination of modified CRITIC and VIKOR methods is used to get the rankings for the selected states based on data collected for the PIs. We divide the states into two clusters based on the improvement or deterioration in the relative ranks and then compare the input-oriented efficiency of the states belonging to the two clusters using data envelopment analysis. The model helps plan the expansion of SE projects by identifying the development trends in areas performing better than others and pinpoints concerns for the areas that are lagging on a relative scale.

Organizations in both academia and industry have struggled with incorporating radical innovation structures within their established frameworks. This paper analyses the practical applications of recently proposed methods of organizational design in highly sophisticated engineering organizations, particularly when introducing a new, disruptive project that requires a different mentality than that of the parent organization. Recent literature has proposed an approach called the ambidextrous organization for these use cases. Therefore, in order to effectively gauge its practical applications, this approach is tested on an empirical case study of an advanced research team developing electric racing vehicles for academic purposes when it decides to expand into autonomous vehicle development.

At the time of pandemics and sustainable disruptions, all carriers struggle with their existing assets. The airline overhead is always a burden specially with the lack of operation; hence, the airlines are always seeking a way to produce earnings from their fleet either by leasing it or by optimizing the utilization. In this paper, the aim is to find the most significant factors affecting the operating cost and the revenue for an airline during a sustained disruption. Through multiple regression analysis, the scheduled flights are categorizing into short-haul and long-haul operations to find the effect of all operating factors on flights assigned to narrow-body or wide-body fleet types. With finding the most significant factors in each type of operation assigned to the different fleet types, airlines shall find the optimum utilization of their fleet with the loss of demand at the times of the sustainable disruption. This way, airlines can allocate the number of aircraft that is needed to be leased and the ones that will bring them a better return during disrupted operations.

Advance selling and resalable return strategies have a very effective effect on increasing sales volume and revenue. In view of low residual value of the upgrading products, we study a new joint strategy of advance selling and resalable return of upgrading products and give the rule of the influence of advance price and resalable return price on product demand and sales revenue. We get the optimal advance price and the optimal resalable return price to make sellers get the maximum revenue. Our paper has also corrected some deficiencies of the current research and makes the research more scientific and practical.

In the age of globalization multi-national companies conduct innovation in a global network. To analyze this phenomenon, this paper uses the international patent application data based on the Patent Cooperation Treaty and focuses Thailand as an overseas innovation site since the second largest number of research and development (R&D) centers of Japanese companies are located in Thailand in Asia after China. The paper has found that Japanese companies, US companies and German companies have different global innovation network patterns. The most frequent innovation network pattern of Japanese companies is Japan-Thailand collaboration; that of US ones is Thailand alone; and the most frequent innovation network patterns of German ones are the network of Germany, Thailand and third countries and Germany-Thailand collaboration.

With the rapid expansion of the science & technology services industry, abundant science and technology re- sources were produced which caused great difficulties for the management of science and technology service platform. Due to the high specialization of science & technology resources, the classification task is not easy. This study aims to propose an efficient classification method for science & technology resources classification such as papers, patents, etc. A science & technology text classification framework based on Word2Vec and consensus clustering-based label propagation algorithm was proposed in this paper. To verify the effectiveness of the method, a dataset of science & technology resources from a Chinese science and technology service platform was used for the demonstration. The study provides a new perspective on science & technology resource classification and the method can also serve many different business scenarios.

Increased urbanization leads to urban traffic challenges such as increased congestion, longer travel times and more transport emissions. Electric Vertical Take-Off and Landing (eVTOL) aircrafts may offer the answer to these challenges as they can act as flying taxis and transport passengers in a fast and eco-friendly way. However, it is still unclear under what conditions consumers will prefer flying taxis over traditional transport modes. Hence, the purpose of this paper is to compare consumer preferences for eVTOLs, public transport and taxis across different travel scenarios. Thus, we perform a choice-based conjoint analysis with travelers in Germany considering price, travel time and CO₂ emissions. Conducting conditional logistic regressions, we show that consumers prefer eVTOLs over traditional taxis, and also over public transport for time-sensitive trips.

Despite lively discourse on the topic, the academic literature still lacks a clear understanding of the processes by which different function-specific “dynamic capabilities” coevolve in a complex organization operating as part of a dynamic and complex adaptive system. Teece’s 2018 proto-synthesis of dynamic capabilities theory and systems theory portrays both theories as adopting a holistic view requiring elements of an organization (as a system) to be in alignment in order to fit the evolving external context and to pursue long-term competitive advantage. In this research we investigate and explain the coevolution of the dynamic capability of the intellectual property (IP) function and that of the technology function by conducting an empirical case study of a European multinational aerospace corporation. The study focuses on the wing technology domain of the commercial aircraft division. We followed an abductive research methodology that requires investigation of existing theory and empirical data simultaneously and iteratively. We collected data from four different sources including interviews, internal documents, patent data, and publicly available information such as annual reports and news.

In-situ monitoring is critical for detecting process anomalies and defect occurrences in additive manufacturing (AM). Traditional vision-based sensing approaches focused on extracting melt pool geometric information, while thermal-based monitoring focused on melt pool temperature measurement. This paper proposes an in-situ melt pool monitoring method utilising infrared thermal imaging for a robot-based direct energy deposition (DED) process. A high-resolution infrared thermal camera is employed to monitor the melt pool region, and a ROS-based multi-nodal software was developed to enable in-situ thermal image processing and melt pool feature extraction. The key contribution of this work is the development of a multi-feature extraction pipeline. Both melt pool geometric and thermal characteristics, such as contour area, centroids, elliptical width, peak temperature, and temperature variance, can be extracted and visualised in real time. The image processing and feature extraction pipeline can work concurrently with the sensor data acquisition. Experiment results are presented to show the effectiveness of the proposed in-situ melt pool monitoring method. It is found that melt pool geometric and thermal features share a similar trend in the temporal domain.

Manufacturing companies are facing growing challenges due to increasing product complexity, shortened product life cycles, rising cost pressures, and changing customer needs. To react quickly and flexibly to these, it is necessary to industrialize the agile product development and start developing with uncertain requirements. A solution is the highly iterative and integrated product and production process development (HIP³D). Putting the HIP³D approach into practice results in a particular challenge analyzing and forecasting the cost due to dynamic changes during develop-ment. Thus, a methodology for the agile and continuous cost analysis and forecasting in the HIP³D is presented.

The design and reconfiguration of Material Handling Systems (MHS) at the factory scale is known to be complex. Various data and analyses are required to define the internal logistics needs the MHS must fulfill. In the literature, the identification of the MHS’ needs is performed through Material Flow Analysis (MFA). The MFA is expressed through charts and diagrams which are manually developed. The manual development of charts and diagrams leads to gathering data in a disseminated way. Additionally, MFA is differently addressed in the literature; each work analyzes a different and restrained set of data. In this paper, we aim to generalize MFA by proposing a Reference Data Model (RDM) using UML (Unified Modeling Language) class diagrams. It allows the listing and structuring of all the data required for the MFA. The RDM can be used to conduct a data-driven MFA which enables data integrity and the reduction of the development time of charts and diagrams. A proof of concept is also given to show the ability to simultaneously generate charts and diagrams while ensuring data integrity.

Automated Production Systems are highly complex, mechatronic systems whose functionality is implemented increasingly via automation software. Achieving and maintaining high software quality for system lifetimes of several decades is thus crucial for machine and plant manufacturing companies to stay globally competitive. However, the multitude of stakeholders involved in the software development workflow ranging from library module standardization up to commissioning at the customer’s site, leads to different perspectives on code quality and target conflicts in the software optimization. This paper introduces a metric-based approach substantiated by structural analysis of dependency graphs to systematically identify target conflicts between library developers interested in high maintainability to keep the software evolvable for years, and application engineers interested in intuitive ease of use to integrate the library’s functionality into machine-specific projects. The approach is evaluated with an industrial automation software library.

In order to optimize the rework strategy of manufacturing system, improve the manufacturing system reliability and control the cost, a rework strategy evaluation method based on stochastic-flow network is proposed. Firstly, a stochastic-flow manufacturing network is established for the target manufacturing system, and the minimum processing capacity of each equipment in the system is determined according to the output constraints, and then the reliability of the manufacturing system is calculated. Secondly, the method of calculating the manufacturing cost of a single product is given, and the decision values of different rework strategies are calculated by considering the manufacturing system reliability and the manufacturing cost of a single product, and then the rework strategy is evaluated. Finally, the above rework decision method is applied to a certain type of slide valve manufacturing system of servo valve. The results show that the optimal rework strategy for the slide valve manufacturing system is to rework each work-in-process (WIP) at most twice.

In a labor-intensive cell production system, the skill of each operator has a significant impact on productivity. Therefore, from the viewpoint of skill improvement through work experience, it is necessary to plan work in such a way that operators can efficiently share the tasks they are not good at. However, there has been no research on robust operator allocation that considers the sudden absence of a operator in the planning stage and suppresses the risk of work replacement. The number of sudden operator absences is increasing due to the prolonged Covid-19. Therefore, this study proposes a robust operator assignment against the occurrence of operator absences. A skill index is used as an index for optimizing staffing and work allocation, and its effectiveness is verified through computer experiments.

In user interface design, the dark mode has attracted increasing interest in offering a more comfortable and flexible reading environment for readers. This study aims to investigate whether different color modes and the design of the textbox will affect users’ reading efficiency and accuracy. The study used a 3×6 experimental design to test 3 different textbox designs and 6 color interfaces, formed by 2 color modes and 3 pairs of luminance contrast. To perform the experiments, the eye tracker was used to collect participants’ fixation rate and saccade rate. The Likert scale was adopted to collect participants’ subjective reading experiences, and the objective understanding scale was deployed to analyze their real reading accuracy. The data analysis results showed that light mode and low background luminance contrast has a significant advantage in word proofreading. The proper textbox design helps participants to focus on text content and search for information quickly. This research may have reference value for design practice in reaching a user-friendly interface for reading and paperwork.

University portals have evolved into an important channel for universities to release campus information and publicize themselves to the outside world, with website design playing a significant role in enhancing user experience and office efficiency. The purpose of this study is to seek the optimal combination of design elements for university portals under different user perception needs, so that a reasonable optimization reference based on user perceptions can be provided. Through Kansei Engineering method and neural network modeling, this study quantifies and predicts users' perceptual evaluation of different forms of university portal design, and derives the optimal combination of design elements for university portals. Overall, this study provides conceptual and methodological support for the research on emotional design of human-computer interaction interfaces, as well as complements existing theories of website design for university portals. The outcome of this study can be fed back to the university information construction departments and related design practitioners to help them improve the website design of university portals.

Manufacturing companies face challenges such as shorter product life cycles, higher demand for front-line employees and demographic change in their workforce. One solution to address these challenges is the introduction of Operator Assistance Systems (OAS) to empower the workforce. To ensure a successful implementation and usage of these OAS, however, it is crucial to engage operators in the implementation process. While previous work has shown that operator involvement is an essential driver for success, many companies struggle to manage the engagement of their front-line employees appropriately, which risks unnecessary project failures. Thus, our paper focuses on the Voice of the Workforce as a form of employee engagement, and analyzes how organizations can successfully and systematically aggregate and integrate the voice of their front-line operators into human-centric production systems. In doing so, we make two contributions: first, the design of a conceptual framework consisting of 8 practical guidelines for practitioners. Second, we provide suggestions on how these principles can be implemented by stakeholders in manufacturing.

For wholesale and Small Medium Enterprise (SME) segments, the credit decision-making process in banking mainly uses a human approach. Although there is already automation in determining the credit system at the bank, the human side still dominates the final decisions. An analyst from the bank needs to understand the background and factors which influence effective decisions.This study aims to review the literature to identify determinants of credit decision-making. The authors used a systematic article search for empirical studies conducted from 1990 to 2021 about the topic. The results suggest three conclusions about factors in credit decision-making: decision-maker's external factors, decision-maker's internal factors, and related research gaps in Indonesia.

This research work aims at improving collective decision-making and learning through a digital twin of the organization in the context of a complex industrial activity such as helicopter engine maintenance. Field and bibliographic studies allowed to determine that the digital twin should be based on a multi-agent system model for reasons of flexibility and modularity necessary in this constantly changing environment. The digital twin is intended to adapt to the organization but also to enhance it by including missing information flows. This paper presents the agent model chosen and inspired from reinforcement learning and how it allowed to identify these missing flows. The importance of interfaces in the digital twin and what they should contain to integrate agents is shown, as well as the psychosocial aspects to be considered for humans to handle their design.

Distribution transformers (DTs) play a central role in assuring the delivery of crucial functions in electric power distribution systems. To sustain the reliability and availability of an electricity distribution network, it is important to minimize the risk of potential failures of DTs. The risk-based prioritization of inspection, maintenance, and repair tasks enables expensive repairs/replacements, loss of efficiency, loss of revenue, and power loss to consumers to be avoided, by optimizing the utilization of resources. This manuscript demonstrates the use of a fuzzy inference system that enables potential failures of DTs to be prioritized, to prevent the potential failure risk of DTs. The suggested approach enables the risk based on likelihood and the consequence of such failures (i.e., the severity and effects of potential failures) to be calculated. The calculated risks of potential failures enable prioritization of the inspection, maintenance, and repair tasks for DTs at optimal resource utilization. The findings from this study are useful for electric power distribution-related inspection, maintenance, and repair personnel, as well as for asset management professionals.

In the future, the environment of network computing will change from the cloud computing to the edge one. In the past, a large number of software reliability growth models have been energetically developed by researchers of software reliability. At present, the software is used under the environment based network operation such as cloud computing and edge one. Then, the method of operation assessment is required under the edge computing. However, it is hard to apply the typical methods of software reliability assessment to such environment, because the edge computing has various structures of server, software, and hardware, etc. We propose a cyclic jump diffusion processes model for effort assessment based on different effort consumption scenarios for network-oriented OSS multi up-gradation. Moreover, several numerical examples based on the cyclic jump diffusion processes model are shown in this paper.

Distribution transformers (DTs) deliver a core role in electrical power distribution systems. It is mandatory to carry out timely inspection and maintenance of DTs, to achieve the anticipated reliability in power distribution systems. Risk-based inspection and maintenance analysis (RBI&MA) enable the optimum usage of available resources, to ensure the reliability and availability of power distribution systems. It is necessary to carry out risk-based screening of DTs to classify them, in order to perform detailed RBI&MA. This manuscript demonstrates the use of a supervised machine learning approach for the risk screening of DTs to classify them into low, medium, and high-risk groups. The suggested approach enables inspection and maintenance engineers and asset managers to recognize those DTs that qualify for detailed risk analysis. To illustrate the suggested approach, a risk screening is performed, using 40 units of DTs in a power distribution network located in a highly dense area. The approach developed in this study enables asset management capabilities in electric power distribution systems to be enhanced, by replicating a similar approach in other key components in power distribution systems.

E/E/PE safety-related systems attract a lot of attention in developing safety-critical systems, such as automotive and chemical plant control systems. In the operation phase, the proof-testing, which is known as a scheduled inspection or maintenance activities for the E/E/PE safety-related system, plays an important role for maintaining designed level of safety integrity and for preventing harmful event occurrences in the operation. However, the proof-testing needs a lot of time and cost for their maintenance to ensure that the system still satisfies the designed safety requirement. This means that it is impossible to conduct the proof-testing frequently in the operation phase from the point of view of the availability and maintenance cost for the E/E/PE safety-related system. We discuss a mathematical approach for obtaining optimal proof-testing intervals by considering the trade-off relationship between the maintenance cost and the risk at harmful event occurrences. Further, we derive an optimal policy for supporting decision making on when to conduct proof-testing from the view point of minimizing simultaneous the expected maintenance cost and risk at harmful event occurrences.

Mixed reality and digital twins offer two prominent technological approaches that have the potential to revolutionize chemical industries. However, there are some hurdles that need to be overcome before these technologies can be widely adopted. One major challenge can be found in cooperation of interdisciplinary teams. In order to create an effective practical solution, mixed reality researchers need to be aware of the heterogeneity of data, scale and requirements of the real-life scenes. Moreover, engineers in process industries need to understand the functional requirements of mixed reality solutions. To close this existing gap, this paper presents an exemplary use case model with market requirements and technological potentials, system interfaces, and role models to serve as a reference model for future trans-disciplinary research on this use case.

Revolution in manufacturing and service industries brings a considerable change in the quality of the products and services. Most systems produce near-zero defects; therefore, data related to defects has many zeros. For estimation, the traditional Poisson model cannot deal with the excess number of zeros. Hence, a possible alternative solution is to use the zero-inflated Poisson model. From a quality control perspective, many control charts monitor zero-inflated Poisson processes. However, very few have considered covariates along with the zero-inflated Poisson variable in monitoring and termed model-based monitoring. This study is designed to propose the model-based Homogenous Weighted Moving Average (HWMA) and Double Homogenous Weighted Moving Average (DHWMA) control charts based on the Pearson residuals of ZIP models. In addition, a simulation-based comparative study is designed where findings are reported using run-length metrics. The findings revealed that the PR-DHWMA chart performs relatively better than the PR-HWMA chart.

Degradation is a phenomenon which necessarily accompanies the operation of every mechanical system. The monitoring of the degradation level is not simple, since it is not always possible to track the wear directly. Therefore the degradation is sometimes examined by applying indirect measures. In our article we study the degradation of a mining drilling machine. We focus on the wear level of cutting tools. We have the data records about the operation of this machine. Using selected stochastic growth models, we study the trend in the development of cutting tools wear. These models provide us with the key parameters of the trend – a mean value, a variance, and standard deviation, which we later use in specific diffusion models. Applying these diffusion models, we examine the trajectories of the degradation wear, up to the possible moment of the first hitting time (FHT). This moment is the point when a cutting tool reaches its critical level. Although this does not necessarily lead to the occurrence of a hard failure but a soft failure, it significantly aggravates the operation properties of a system.

Extending the vehicle routing problem (VRP), the crowdshipping VRP (CVRP) considers crowdsourcing logistics. Crowdsourcing is flexible and convenient to reduce transportation costs and carbon emissions. However, crowdshipping requires to adapt to real-time changes such as road conditions and customer demands, which heuristic algorithms are not suitable for addressing these issues. Therefore, this study proposes a deep reinforcement learning (DRL) approach to react to real-time environmental changes to solve the CVRP. The CVRP considers a single depot and multiple transfer points to serve multiple customers, in which cargos can be delivered by either the vehicle directly, or crowdworkers after the vehicle stores cargos at transfer points. In the proposed DRL, the agent explores feasible decisions, and revises the path that it should take based on feedbacks. The cost effectiveness that affects crowdshipping includes the vehicle routing, and whether the concerned customer is suitable for crowdshipping. The experimental results show the efficiency and accuracy of the trained model for medium-sized VRPs are much higher than classical heuristic algorithms.

The generic variant of the school bus routing problem requires finding a set of routes that cover a predefined set of student pickup locations in order to minimize travel time while satisfying various constraints. In this paper, we study a new variant of the problem that was motivated by a real-world application in Doha, Qatar. In this variant, two objectives are considered: minimizing the number of buses and achieving a goal of not exceeding a predefined maximum travel time. We propose a compact mixed-integer programming problem and present how it can be solved heuristically to derive high-quality solutions for large-size instances.

All models are wrong, some are useful. One of the usefulness criteria of the model is to ensure whether the users of the model understand and obtain the insight and transfer of learning of the model. This study attempts to evaluate insight and transfer of learning of Discrete Event Simulation (DES) user using the usability approach and a visual display-based method, i.e., eye tracking. Participants are undergraduate students who have no experience using the DES model and Flexsim software. The experimental conditions are simulation with animation and statistics display. Participants were instructed to work on the simulation model. The result shows that there is no significant difference between statistics and animation display in generating insight and transfer of learning. This is supported by the result of an eye-tracking study and usability testing.

Nowadays, energy saving is one of the main concerns of companies. Therefore, different studies have developed and integrated methods to measure the energy consumption of machine tools. In this paper, an integrated economic production quantity (EPQ) model that considers energy consumption is analyzed. The concept of specific energy consumption (SEC) is used to evaluate the energy consumption during the production time of the machine. Three types of SEC, depending on the production rate, were considered. By minimizing the total cost, the status of the machine in the non-production phase, the optimal production rate, the optimal cycle time, and the influence of these different methods of measuring energy consumption are defined. The results of the study show that the difference in using the SEC -types model does not affect the state selection of the machine during the non-production phase but can change the optimal solutions of the overall system. From the manager's point of view, the choice of different models to represent the energy consumption of the machine leads to different optimal decisions. Numerical analysis are performed, and results are discussed.

Multi-hole drilling is one of the frequently used manufacturing processes in industries. Further, in mass production, multi-hole drilling path optimization plays a crucial role to remain cost-competitive. Usually, the multi-hole drilling problems are modeled as traveling salesman problems (TSP), and solving them is quite difficult as they fall in the category of NP-hard problems. Researchers have used various evolutionary optimization algorithms like a Genetic algorithm (GA), Particle swarm optimization (PSO), and Ant colony optimization (ACO) to optimize the multi-hole drill path sequences. Literature reveals that Simulated Annealing (SA) is one of the least used standalone algorithms in multi-hole drilling path optimization even though it is one of the promising algorithms in the presence of multiple local minima. This paper aims to study the performance of SA and compare it with GA, PSO, and, ACO using multi-hole drill path optimization test problems reported in the literature. The outcome reveals that the SA performs quite well.

Measuring impact of corporate actions on corporate social performance (CSP) is traditionally recognized to be notoriously difficult. This manuscript reports results of a study using Net Impact Method to quantify CSP and approximates corporate financial performance (CFP). We report findings on the CFP-CSP relationship and consider temporal lags between CFP measures and CSP impacts. Our findings are mostly supporting existing research but contrary to earlier research we find non-linear dynamics between CFP and CSP measures on some accounts. Although we used a limited dataset, our findings shed additional light to the CFP-CSP relationship.

In the realm of manufacturing, Industry 4.0 is gaining increasing importance. Academic literature has highlighted the technologies part of Industry 4.0 mainly. However, there are very few studies that have discussed the Industry 4.0 technologies, services, and production resources at the industrial context. In particular, a research gap exists to an academic discussion encompassing the impact of Industry 4.0 technologies on its’ services and production resources. This paper aims to present a preliminary framework showing the impact of Industry 4.0 technologies & services on production resources. The framework is applied at two different types of industries. This study can be used to best support and select suitable Industry 4.0 technologies for industrial decision-making purposes.

Today, manufacturing companies are surrounded by constantly changing external influences. These entail the risk of inefficiencies in the operational process, especially when existing plans have to be adapted. A common trigger of such modifications are customer order changes. They cause unplanned costs, which endanger the profitability of the company. However, the estimation of these additional costs is complex and therefore missing in today's operation. This paper therefore defines customer order change types and determines their effects based on the Aachener PPS-Model. The individual processing areas of a customer order are described and mathematically formulated. Agent-based simulation is used to evaluate the critical change dimensions. Simulation experiments showed that the additional costs generated due to customer order changes decrease if the average relative date and quantity change are reduced. If both change scopes are reduced equally, in the case of reducing quantity, the additional costs are a slightly higher than in the case of reducing date change.

Product lifecycle management (PLM) is the strategic process of managing all the data of the product from the design up to its disposal or recycling stage. In product development, the PLM data is the key source for better decision-making at different stages of the product lifecycle. To this aim, several PLMs have been implemented in industrial sectors. Among these PLMs, each carries some common as well as certain distinct features and characteristics over its counterparts. However, due to the complex and decentralized nature of today’s product lifecycles, all of the currently implemented PLMs face certain challenges. In this context, various software reviews and selection websites have been explored to investigate the core functionalities as well as the challenges associated with the currently implemented PLMs. Moreover, this study analyzed that most of the existing PLMs do not contain all the required features and therefore, industries usually integrate different software to make a full-fledged PLM system. However, this practice results in reducing the overall system efficiency. Therefore, this study emphasizes that it is the need of the day to adopt a novel technology i.e. blockchain to overcome the PLM challenges and its consequences in the production industries.

In many regions of the world, energy providers attempt to smooth energy demand during the day by employing time-of-use tariffs. This study considers dispatching rules for scheduling operations in a multi-machine job shop that operates under time-of-use tariffs, with dual performance objectives of minimizing the mean tardiness of completed jobs, as well as total energy cost. A number of new dispatching rules designed for the energy objectives are tested alongside other traditional dispatching rules on test problem instances. Results show that the new energy-oriented rules tend to perform better in balancing satisfaction of the two conflicting objectives, in comparison to rules such as the shortest processing time (SPT) and cost-over-time (COVERT), which generally exhibit good performance for mean tardiness in job shop environments.

Sustainability is becoming important in today's competitive world as a strategy for many industries to boost longevity and remain competitive. When making strategic decisions like risk mitigation, resource restrictions, cost reductions, and tax incentives, businesses take sustainability into consideration. A key action in advancing this trend is the choice of sustainable suppliers. This method uses a lot of contradictory criteria and relies on decision-makers frequently ill-defined and non-quantifiable expertise. The study offers a framework for selecting sustainable suppliers in India's automobile industry using the fuzzy-AHP-TOPSIS method. The economic sustainability factors were concluded to be the most important criteria, followed by factors/criteria related to social sustainability.

The three-dimensional bin packing problem(3D-BPP) for deformable items is quite common and essential in real-world city logistics and manufacturing environments, for example, fresh food supply chain. However, no published literature has addressed this problem from an exact point of view. In order to further increase loading efficiency and reduce operating costs for related firms, we examine a 3D-BPP variation in this work that takes into account the compressibility factor of deformable items. We formulate the studied problem as a mixed integer programming model in which several groups of unique decisions and constraints are introduced. A series of numerical experiments are conducted over several randomly generated benchmarks with various parameter settings. Results reveal that the total volume of packed items and the space utilization are significantly enhanced after considering the compressibility factor since the actual room occupied by packed items are estimated in a more accurate and practical way. In particular, the improvement in the space utilization reaches 8%∼28% compared with the case without compressibility issue. These benefits show that there is a large potential in reducing packaging and delivering costs by estimating the actual occupied space under the dynamic volume change assumption.

Disruption is one of the causes of supply chain instability that may affect supply chain performance. This paper proposes a Mixed Integer Nonlinear Programming (MINLP) model to analyze a location-allocation problem with supply disruption in a three-echelon network. This study uses (s,S) and (s,Q) inventory policies to manage the inventory. Sourcing strategy is evaluated to create supply chain resiliency. The sourcing policy is conducted by considering single-sourcing and multiple-sourcing. Furthermore, the model also considers environmental effects by calculating carbon emissions resulting from transportation and manufacturing processes. Numerical analysis is evaluated using a Lingo solver. According to the result, implementing multi-sourcing under supply disruption can reduce the total cost of the supply chain by 4 %.

E-procurement is a popular enterprise information systems (EISs) that were implementing by many companies and governments in digital transformation era. E-procurement for the tendering process increases transparency and organizational performance. The objective of this paper is to utilize the web mining to gain insight from data patterns in e-procurement systems regarding procurement performance. Construction tender data from two central provinces in Indonesia: East Java and DKI Jakarta, are case studies to be absorbed from tender applications via web mining. The case studies data are processed using the multiple linear regression method to produce a predictive model for one of the effectiveness performance indicators: bidder appointment time. Four independent variables: contract price, number of participants, number of bidders, and number of revisions were proven to predict bidder appointment time significantly. The number of revisions has the most influence on bidder appointment time in terms of its coefficient value. The model can be used for tender scheduling, setting procurement targets, to resource planning.

The advance of digital technologies such as big data, cloud computing, and artificial intelligence ushers in the digital era for modern societies. Digital IT innovation plays an increasingly important role in helping supply chains recover from disruptions due to disastrous events like the COVID-19 outbreak. Nevertheless, there is a lack of systematic literature review on the phenomenon. As such an attempt, this paper explores the role of digital technology innovation in enhancing supply chain resilience and answers this question through a literature review and summarizes six dimensions of supply chain resilience, which provides some theoretical guidance for subsequent studies.

The truck parking problem is currently a major challenge for transport logistics. This problem arises from the discrepancy between available and required truck parking spaces. In order to be able to intervene actively, approaches are needed that allow the derivation of regions with increased parking space requirements based on non-personal data. We show that an algorithmic approach can be used to gain basic knowledge about the regions in which truck parking spaces are needed. These can be the basis for comparisons. The procedure is based on a data set of a German ERP software manufacturer and includes about 57475 trips that took place within one month in the European area. It could be found that the algorithmic approach is suitable and can be the basis for further research findings based on it. Furthermore, the first findings of the approach clarify in which regions of Europe an increased truck parking demand exists based on a K-Means clustering. Central to this is also the creation of comparability with the existing infrastructure (parking possibilities, charging possibilities electric/hydrogen, emissions).

Innovative solutions are often crafted through synergizing contributions from a group of diverse entities with complementary strengths and weaknesses. The same is expected to persist with the advent of artificial intelligence (AI). Thus, this work aims to investigate whether the synergistic interaction of human decision makers and optimization (AI) algorithms can significantly improve the solving of challenging, multi-criteria order planning problems. To this end, a Human-AI complementarity framework leveraging on emerging transfer optimization methods is first put forward, enabling the adaptive reuse of experiential priors to inform search. Next, empirical analysis on a carefully designed multi-criteria order planning problem is conducted. Finally, 3 key insights arising from situations where the human supplied prior is perfect, imperfect, or flawed are discussed to adequately address the research question posed.

Air conditioning systems consume almost 40% of energy in buildings. They contribute to global warming both indirectly from their fossil fuel use of electricity and directly through their refrigerant releases. The use of natural refrigerants reduces energy consumption and GHG emission, however, their flammability poses risk in their implementation. This paper discusses the risk assessment of flammable refrigerant applications in air conditioning systems in Indonesia. There are two main hazards in the application: fire and explosion. A semi-quantitative risk assessment is performed. The study combines primary data from accident reports in transportation, installation, operation, service and maintenance of air conditioning systems with flammable refrigerants. A case study of risk assessment in an air-cooled chiller with a refrigerant leakage scenario using computational fluid dynamics is presented. The results indicate that significantly high risk occurs during service and maintenance activity, while lower risk occurs during operation. Several risk mitigation and reduction efforts are recommended that include technical and regulatory approaches.

Personal data is used to define customer requirements. Organizations should securely collect and process such data, using data protection policies aligned with the applicable regulations. The General Data Protection Regulation (GDPR), an EU data protection law, has include a data protection assessment method called Data Protection Impact Assessment (DPIA) to ensure personal data security. The maritime industry is also concerned about personal data protection. However, there is a still a lack of practical methods to assess data protection risks. This article aims to introduce the conceptual framework for a new method for risk assessment in maritime systems, using DPIA and various systems-theoretic risk approaches as a conceptual basis. The ICT system is a central system in which personal data is utilized in the architecture of maritime systems. In this article, this system will be taken as a basis for illustrating the newly proposed method for personal data security risk assessment in a DPIA context. The conceptual framework will be further concretized and tested in follow-up research.

Traffic injuries and accidents are the ninth leading cause of death worldwide. Motorcyclists account for approximately 14% of all traffic fatalities in the world. Non-using safety equipment is an essential factor that threatens motorcyclists’ safety while riding. The purpose of this study was to utilize the motorcycle rider behavior questionnaire (MRBQ) approach for several objectives; (a) To study and analyze the behavior of unsafe motorcycle driving while providing delivery service, (b) to study and analyze the relationship of unsafe driving behavior with personal background information, including related factors, and (c) to present a strategic plan to develop effective prevention of loss from accidents that may occur in the future in the context of motorcycle food delivery professionals in Chiang Rai Province, Thailand. This study conducted recent outcomes considering behavioral, exposure, and operational conditions in a group of motorcycle riders. Moreover, it advises some practical senses for the well-being of motorcyclists and road safety. Findings show that the overall often level of risk riding behavior, sample riders violated the speed limit on both interstates and local streets.

This study is conducted to develop a comprehensive ship emission inventory in Strait of Malacca and Singapore (SOMAS) based on Automatic Identification System (AIS) data using the bottom-up method. With spatiotemporal analysis on the maritime traffic in SOMAS, it is implied that limited space and dense, complex shipping routes had created hindrances to the local traffic. Gaussian approach Kernel Density Estimation (KDE) is adopted to find the hotspot of the traffic and emission in SOMAS. Among all emitted pollutants estimated, nitrogen oxides shared the most proportion among pollutants by maritime. With the argumentation of more emission generated under slow engine speed, the environment in SOMAS had favoured its generation. With over 100,000 ship trajectories compacted within the port waters and turning points between Johor and Singapore strait, it was found that the highest contributor of emissions came from containership as a result of slow steaming. Other cargo ships also contributed substantially on the emissions as a result of long period of manoeuvring or hotelling. The result presented draws the attention about the environmental impact caused by ship emissions.

As social robots become more imminent in today’s “technology-centered” world, cyber risks related to such robots also increase. To address these risks, appropriate risk mitigation strategies should be developed by considering potential threats, attack method, vulnerabilities, and assets. However, it remains a challenge hindering the sharing of standardized cyber threat information across cyber risk management organizations and authorities. Furthermore, since cyber security threat landscape is in constant changes, static risk mitigation strategies would be obsolete. To deal with the inadequacies of current risk mitigation approaches, developing a collaborative and dynamic cyber risk mitigation methodology, for various cyber threats associated to social robots in public space domain would be the way forward. The purpose of this paper is thus to propose a dynamic cyber risk mitigation methodology by depicting elements that lead to a secure, robust, dynamic, and resilient risk mitigation strategy to provide a range of cyber risk and threat landscape information. The application of the methodology is demonstrated by a case study of a social robot providing daily assistance to the passengers taking the city ferry in Fredrikstad, Norway.

Cyber-physical systems (CPSs) have revolutionized the supply chains by making them more responsive to network disruptions. At the same time, these systems expose supply chain capabilities to new vulnerabilities and cyber threats. It is important for business organizations to consider the cyber security of the entire network while outsourcing any activity. In view of this, the paper identifies the factors and cyber resilience measures to evaluate the supply chain partners on their network resilience using a hybrid multi-criteria decision-making model. The paper also proposes a mathematical model to estimate and maximize the cyber resilience in a supply chain network. The findings of the model can help business organizations to identify secure supply partners and identify and fix the vulnerable links in the chain.

Machine failure often leads to unplanned downtime in industrial manufacturing, which could result in a significant loss in the manufacturer’s revenue. Several machine learning-based approaches have been proposed to alleviate the problem by instantly detecting occurring failures or predicting any potential breakdowns. However, there still exist limitations and issues that require attention. These include the difficulty of collecting real-world industrial data, especially big data, the challenge of feature selections and the under-representation of machine failure events in the data. In this paper, we present the use of a small predictive maintenance dataset with basic supervised learning algorithms for industrial machine failure detection. Moreover, we show the need of handling the imbalanced class distribution in such data for more accurate detection. Several non-deep learning algorithms were used for the classification task, and data resampling methods were applied to improve the model performance. Results show that decision tree could provide promising classification results, and with an under sampling method, the detection accuracy of 91% could be achieved.

Multifaceted advantages are reported for the application of Virtual Reality (VR) technologies in engineering design. However, there are different challenges for productive use of VR in practice, caused by a lack of systematic information about potentials, technical and organizational requirements, limited support to guide the implementation process and additional efforts for preparation and post-processing activities. In this paper the usefulness of structured reflection to explore benefits and challenges on long- and short-term  levels and with regard to social, process and goal aspects in highlighted. Therefore, a framework and guiding questions are presented and a case study of reflection on VR implementation in a lecture setting is reported. Based on the findings essential fields for further research are formulated.   

Sustainable development aims to achieve prosperity through economic improvement, social welfare, and environmental sustainability to meet the demands of stakeholders. Thus, stakeholder demands must be the basis for designing the company's strategic objectives and business processes. For this reason, it is necessary to formulate stakeholder values. This study aims to identify stakeholders value​​based on the SBSC perspective. The research method includes exploring stakeholders value​​based on a literature study validated through an interview process with five division heads at the three best SOPEs in Indonesia. Interviews were conducted to obtain the relevance of stakeholders value​​implementation to stakeholders value​​by literature. Stakeholders include shareholders, consumers, the community, management, business partners, and employees. The study results show that the value is relevant and very relevant to the stakeholder value that has been formulated based on the theory.

DRAM (dynamic random access memory) is one of the most important memory chips in consumer products. In recent years, Samsung, Hynix, and Micron, have been aggressively expanding production capacities to capture emerging markets. However, forecasting sales revenues and market shares are still challenging because of demand uncertainties and price variations. In this research, a novel framework is presented to highlight the following issues: (1) the prediction of sales revenues for DRAM vendors is based on global shipments of consumer products, (2) commercial competition between global leaders is analyzed to reveal managerial insights and estimate stable equilibriums, and (3) transition dynamics between the top three vendors are captured to predict market shares. Compared to considering historical data, the inclusion of consumer products can significantly enhance the performances of sales forecasting. The market leader (Samsung) can benefit market followers (Hynix and Micron) but the followers are independent with each other. At stable equilibriums, Micron’s sales are expected to increase the most (+22%) but market shares between the three vendors seem to be very stable in terms of global shipments.

Early internationalization of firms and their success in foreign markets play an important role in both their  growth and the resultant impact on the global economy. We conducted a study to investigate the factors that lead to early internationalization of firms that operate in countries with a large market. We tested a hypothesis that a firm will have less incentive to internationalize if there are sufficient demands. Data was collected from Japanese high-tech startups using qualitative and quantitative methods. Our study found that the size of the home market is not a deciding factor in a firm’s decision to internationalize; instead, international entrepreneurship and the condition of the home market affect early internationalization. This study adds to the current discussion of the internationalization processes put forth by Kudiana et al. (2008), and highlights geographic considerations and types of the technology as major factors affecting early internationalization. Our findings could provide insights and practical guidelines for entrepreneurs who want to explore ways to globalize their business operations and help deepen our understanding of the specific mechanism of the firm's globalization process.

To minimize waste and maintain efficient resource utilization, companies bring back their used products, which have reached their end of useful life, also called EOL (End of Life) products and materials from the customers, to the proper disposal point. The Reverse Logistics (RL) practiced by developed countries are still far from developing countries. However, the population and industrial development has rapidly increased, and the challenges in handling End of Life (EOL) products and materials are immense. With global competition, sustainable development, thrust on efficient resource utilization, and environmental concerns, industries in developing countries are forced to adopt RL practices. In the Indian context, studies on RL barriers are confined to electronics, pharmaceuticals, automobiles, manufacturing, and retail chains. This paper identifies the RL barriers in the lubricant oil industry using MCDM methods and ranks the barriers found in the literature in the Indian context.  This study identified 20 barriers in 11 groups and ranked the criteria along with the Sub-criteria with the help of the expert panel. The final ranking of the barriers indicates that infrastructural barriers, including storage and transportation, are the most important in implementing RL in the lubricant industry, followed by economic barriers. Lack of motivation and reward, a cultural barrier, comes out as the third most significant barrier. The findings have academic, managerial implication, and policy implications.

In contemporary world, people’s and companies’ expectations of the city environment have changed. Attention is increasingly being paid to the wide variety of aspects in city life, starting from effective and creative solutions for talents, public places, energy, water, and waste disposal. To meet these demands, real-estate developers have focused on opportunities provided by the smart cities concept. However, smart cities can be managed only when they are measured. This paper utilizes bibliometric analysis, based on the most well-known indexes for smart cities, to develop a measurement model for developers, which should adequately provide evidence-based support for city development and appropriately consider context-specific factors. This research focuses on the privately owned Ülemiste City (ÜC), a suburb of the Estonian capital Tallinn with more than 500 companies and 13,000 employees. ÜC’s economic indicators, such as turnover, average salary, and exports, have increased year on year. ÜC is strongly oriented toward well-being and the engagement of talents and tech-rich companies. As a result, a unique Northern-European-specific smart city measurement model is constructed that is used by developers on a daily basis.

Digitalization and technological progress have made radical innovations increasingly important for incumbent firms to maintain competitive advantages. However, unlike startups, incumbent firms struggle with radical innovations when using their regular research and development departments. These departments are optimized for developing incremental innovations due to their highly structured processes. Startups with more flexible approaches were found to be better able to respond to uncertain market conditions for radical innovation. Recently, researchers and practitioners have focused attention on corporate venturing to overcome this lag of incumbent firms by using flexible startup structures for innovation units. However, practical attempts differ in their innovation performance. Despite numerous studies in the field, there is no best practice guide to be followed by practitioners in corporate venturing. Therefore, the objective of this study is to examine the current state of knowledge through a literature review. Based on this, we explore current research gaps and develop a research agenda. This aims to support researchers to expand current knowledge to reliably improve innovation performance in corporate venturing.

Pharmaceutical firms are persistently among the top lobbying spenders in the United States (US). While the potential strategic benefits of lobbying are well recognized in the literature, the criticism against unethical aspects of lobbying warrants pharmaceutical firms to carefully strategize their lobbying activities. Our study addresses the question “To what extent can signals sent through lobbying be substituted with other, non-controversial, signaling mechanisms?” Specifically, we investigate whether rhetoric signals sent through lobbying can be substituted with substantive signals sent through other mechanisms such as the intellectual capital (human, structural, and social capital) of firms. Studying a sample of the largest US-listed pharmaceutical firms between 1997 and 2017, our analysis suggests a substitutive relationship between a firm’s intellectual capital and lobbying – and thus its market and non-market strategies. These findings have important implications for academics and policy makers.

With the high pace of digitalization, numerous information technology projects are initiated, but few are finished in time or at all. One reason is the inability to clarify the benefits of finishing the project from a perspective other than economic utility. The paper assesses a benefit analysis model within public-sector organizations. The evaluation is performed during three workshops with five representatives from five regions, municipalities, or cities in Sweden. The results reveal how the participants experienced difficulties quantifying qualitative information, focusing on economic utility, translating qualitative benefits into monetary value, finding representative measures for qualitative benefits, and defining a benefit from different perspectives.

Performant business processes constitute decisive advantages for companies on highly competitive markets. However, conventional approaches to business process improvement are prone to subjectivity and high manual efforts. Latest approaches address these challenges by semi-automating the inherent phases of process analysis and process design with data-based weakness detection and measure derivation. What is still missing is their integration into a holistic and data-based decision support that helps users to design a to-be process based on performance information about existing process weaknesses and potential improvement measures. This aim is pursued with this paper’s approach. First, a business process performance indicator is developed that serves as the central optimization variable for all decisions in process analysis and design. Using event logs, it can indicate performance losses caused by process weaknesses and performance potentials of improvement measures. Next, a calculation model for calculating the business process performance indicator is derived. By prioritizing process weaknesses and potential improvement measures according to their impacts magnitude, a decision support for process design is provided that can enhance the effectiveness and efficiency of business process improvement.

This study aims to look at the bibliometric features and trends of publications on SMEs' digital readiness indexed in Scopus by authors worldwide and to identify the most critical dimensions and indicators for the assessment. A systematic literature review resulted in 92 publications on SMEs' digital readiness, published between 2003 and 2022. Finally, based on inclusion/exclusion criteria, this study only used six out of 92 articles published between 2019 and 2022 by four journals to find the dominant dimensions and its indicator. Then, based on the selected articles, this study found ten different dimensions and 59 indicators for assessing the digital readiness in SMEs.

In the actual worldwide context of manufacturing, the capacity to introduce, adapt and produce a new product quickly represents an important strategic objective. This is also a remarkable challenge for the managers, specifically due to the instability in demand and supply. COVID -19 and Russia-Ukraine war have amplified this instability. The flexibility in several parts of a manufacturing system is one of the main pillars of industry 4.0 and could be a great initiative to be investigated. However, there are several types of flexibility, and prioritizing them remains a difficult decision. The definition of several types of flexibility, the opportunity offered by each one, the complexity to implement them, and the way to evaluate their levels are not well developed in the literature. This paper proposes several types of flexibility, the challenges, and some key indicators to measure them. Then a model to identify the appropriate flexibility is proposed. To illustrate the complexity of the problem, a use case of launching a new product in the automotive industry is presented and the necessity to identify the appropriate flexibilities is discussed.

Printed circuit board (PCB) is a critical component of electrical products, and its quality control during the manufacturing process cannot be overemphasized. This work proposes a model for early PCB defect discovery in the molded-interconnect-device manufacturing process. Based on transfer learning and data augmentation, a one-stage deep object detection network is built for defect detection, which is trained with data directly obtained from the production line. To demonstrate the effectiveness of the proposed method, the manual inspection process and its statistical data from a real PCB plant are used as a basis for comparison. In addition, a 10-fold cross-validation is performed to provide a more concise evaluation. The result shows that the proposed model possesses the ability to detect six types of subtle defects and achieves an identification accuracy of 83.75%. Moreover, the model provides a significant reduction in manufacturing cost, with 84% of the total inspection time being saved. With the advantages of accurate multiclass detection ability and low establishment cost, the proposed model is shown to have great potential for industrial implementation.

A multi-objective optimization algorithm is proposed in this paper for motion planning and layout design in robotic cellular manufacturing systems. The sequence-pair is used to represent the layout of a robotic cell, which can avoid the overlapping of modules. For each layout, the robot motion planning using Rapidly exploring Random Trees (RRT) is conducted to compute the total operation time. A non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to minimize the layout area and operation time. The proposed method is applied to a 6-DOF (Degree of Freedom) robot manipulator, Niryo Ned. In the experiments, a Pareto set is obtained. The experimental results suggest the tradeoff relationship between the operation time and layout area. The findings show that the proposed method can efficiently solve the optimal motion planning and layout design problem in robotic cellular manufacturing systems.

This work strives to support companies overcoming their challenges in the New Product Development (NPD) by enhancing the co-development of product and production with the integration of Design and Process Failure Mode and Effects Analysis (respectively DFMEA and PFMEA). A literature review and a case study with two Swedish manufacturing companies help identifying challenges and opportunities to integrate DFMEA and PFMEA reviewing companies’ templates and guidelines and performing two workshops. The results contribute to the knowledge in the field of co-development of product and production.

The importance of the metalworking sector in Peru is reflected in the high demand for machinery, equipment and structures in various economic sectors such as industry, construction, mining, transportation, among others. This makes it a generator of large productive links and employment. Today, the struggle to follow a quality standard that guarantees the production of these goods persists, as well as the effort to improve the customer's perception of the service offered. This study proposes an integrated model to increase the service level of a metalworking company through the use of Lean Service and Inventory Management tools. The results show a 10.13% increase in the service level, thanks to the implementation of engineering concepts such as 5s, Kaizen and Inventory Management.

IoT devices are widely adopted in various scenarios to realize real-time data collection. However, it is challenging to efficiently collect the sensing data when IoT devices are physically distributed in remote areas. This paper proposes to use Unmanned Aerial Vehicles (UAVs) to realize efficient data collection from these IoT devices. Specifically, the heterogeneity of IoT devices in terms of data generation rates is considered, which is modelled as different time windows and service time requirements into the UAV routing problem. Besides, a multi-UAV multi-trip route planning model is proposed and a Tabu-Search based solution algorithm is developed. Finally, an experimental case study is conducted that verifies the effectiveness and performance of the proposed method.

It is stated that the main challenge of companies is not technology, it is organizational culture. Organizational culture is the glue that keeps a company together; it determines both the overall performance and the implementation of ideas. Organizational health (OH) is a modern phenomenon with roots in organizational culture. The corporate world of start-ups is characterized by fierce competition, where being healthy is a necessary precondition to achieving long-term success. A qualitative multiple-case study of three Estonian large-scale start-up companies (with a valuation of over USD 90 million) is used to discover how OH influences employees’ performance.[1]Altogether, 11 interviews were carried out. The results of the content and cross-case analysis revealed that the healthy organization sub-factors of leadership and work environment most likely influence employees’ performance, while accountability, motivation, and external orientation are the OH factors that seldom influence employees’ performance.

As more and more companies adopt lean management as a system to continuously develop people, leaders are increasingly intent on conducting gemba walks. This means going to the workplace, be it production, engineering, or supply chain - to explore and discover important business challenges - often under the guidance of a sensei. As a result of the COVID-19 pandemic and the associated social distancing measures and travel restrictions, physical meetings in the workplace have been hampered, especially those involving outsiders. This has since led organizations to consider reducing travels and physical gatherings in general. Therefore, in this paper, we present assisted reality technology as a means of digitalizing gemba walks, allowing managers and executives to benefit from working with external sensei – albeit on a remote basis. We explore the use of RealWear HMT-1 technology as an enabler of the virtual sensei concept, comparing it with the more traditional face-to-face approach to gemba walks and offering insights from multiple interventions between external sensei offsite and local management representatives onsite. The assisted reality approach has been tested both within and across international borders. In general, we find that the digitalization of gemba walks using assisted reality offers multiple benefits over more traditional means. However, there are also several limitations. As such, this work has implications for both research and practice, in that we contribute towards the growing literature on digital lean manufacturing as well as offer practical guidelines for managers and executives embarking on a lean transformation.

The purpose of this research was to analyze how aviation maintenance workers' work characteristics and work demands affect the development of work-related musculoskeletal disorders (WMSDs). The data were collected from 396 aviation maintenance workers of Pakistan Airlines and were analyzed through descriptive and inferential statistics. It has been found that work characteristics have a significant positive effect on WMSDs, and an increase in tasks performed by aviation maintenance workers led to increasing in WMSDs. Work demand also has a significant effect on WMSDs. The Work characteristics of aviation maintenance workers moderated the relationship between their work demand and WMSDs. The work demand of aviation maintenance workers influenced the development of WMSDs. To improve the intensity of work-related musculoskeletal disorders (WMSDs), the Pakistan Civil Aviation Authority should reduce the intensity and frequency of duties allocated to aviation maintenance staff by using work dynamism like task sharing, job rotation, and teleworking. During the recruiting process, the H.R. department must assess potential aircraft maintenance workers' ability and fitness levels and deploy training, physical exercises, and ergonomic rules to lower the occurrences of WMSDs.

Accessibility is a crucial concept in the study of social inclusion, justice, equity and security as well as the reliability of supply in societies. Research has examined factors that objectively measure accessibility, such as land-use and socioeconomics. However, such approaches often fail to include individual perceptions of accessibility. Thus, this study proposes a multi-linear regression model that focuses on both objective and subjective factors to assess municipal accessibility. In addition, principal component analysis is applied to reduce dimensionality and eliminate the problem of multi-collinearity. The paper contributes a novel model with a higher prediction rate (70.5%) than that of a traditional multi-linear regression model (58%) during an evaluation of accessibility in the Swedish context.

Simulator training is an integral part of seafarer education and training. Maritime Virtual Reality (VR) simulators have added a new dimension to the range of available state-of-the-art training tools in recent years. The lack of appropriate pedagogical intervention including inadequate performance assessment frameworks for the trainees are few of the limitations of maritime VR simulators. In this study, a performance assessment framework utilizing Artificial Neural Network (ANN) in VR training from the healthcare domain is adapted through literature review. This framework could be operationalized in maritime training for aiding the performance assessment of seafarers and in turn increasing the pedagogical efficiency of maritime VR simulators. The implication of such adaption is also discussed considering the human factors and the technical dimensions of maritime training.

While technology is an important catalyst in manufacturing, people are expected to remain integral contributors on future shop floors. For example, it is not always effective to assign tasks such as error diagnosis and repair to autonomous systems. Even more, where total automation is not the preferred option, augmentation technology and Operator Assistance Systems (OAS) provide opportunities to realize the best combinations of people and technological capabilities. However, there is a limited understanding of how to systematically integrate OAS into production systems from a human-centric, value-driven perspective. This is crucial in so far as the successful adoption of OAS often depends on the way it was co-developed and deployed. This paper explores how to integrate OAS into a complex repair process that involves up to 162 diagnosis items. This is realised by applying the Augmented Workforce Canvas – a framework for guiding human-technology integration – as part of a case study in the repair center for display panels of an electronics manufacturer. A result of the study is that OAS can decrease technicians’ perceived stress level during the error diagnosis.

It is very important to analyze and predict the fatigue life of gear, which is the key part of transmission. Due to the small amount of bending fatigue life data, two sample expansion methods, intermediate interpolation method and Lagrange interpolation method, are used to expand the amount of data, establish equal spacing and non-equal spacing grey GM (1,1) prediction models respectively, and test the models. The results show that the most accurate prediction results can be obtained without interpolation for non-equal spacing models, while the most accurate prediction results can be obtained by Lagrange interpolation for non-equal spacing models. Compare the gray GM (1,1) prediction model with three traditional prediction methods, the results show that the gray GM(1,1) prediction model can obtain the most accurate prediction results for small data. It provides the manufacturer with the processing method of small data and the prediction method of gear bending fatigue life under unknown stress.

In manufacturing operations, bearings are crucial for the reliable operation of rotating machines. Accurate bearing remaining useful life (RUL) predictions are increasingly in demand. The data-driven bearing RUL prediction method has shown remarkable potential for enabling intelligent prognostics. The empirical mode decomposition, random forest, and Bayesian optimization are all integrated into this paper's innovative data-driven prediction framework for bearing RUL. The two fundamental stages of the proposed framework are feature extraction and RUL prediction. The empirical input signals were divided into separate frequency bands in the first phase of this framework in order to filter out any unnecessary frequencies and identify the fault features of the signals. The RUL prediction is then performed in the second phase using an RFs-based model, with the hyperparameters adjusted via Bayesian optimization. Datasets gathered from an actual run-to-failure experiment of roller bearings are used to validate the suggested approach. Compared to the conventional data-driven and stochastic approaches, the experiment findings demonstrate a significant improvement.

Specific Fuel Consumption (SFC) is an indicator to measure the performance of power plants. Its value must be monitored and also predicted so preventive actions and maintenance can be formulated accordingly. To develop SFC prediction model, it is important to evaluate which factors are influential toward SFC values, how data collections are made, and which models are the best to use to predict SFC values with high accuracy. This paper provides a preliminary study to support the development of SFC prediction model. The results show that engine loading and type of fuel are the two major factors affecting the SFC values. Data collection to calculate SFC can be obtained through either controlled experiments or direct observations in power plants, each has its own pros and cons. SFC modeling can be done using regressions (linear, polynomial or SVR) and artificial neural network (ANN). Each method can be applied to get the modeling that produces the highest accuracy. However, the accuracy is also highly dependent on the validity of the input data.

In the oil and gas industry, the process shut down (PSD) and emergency shut down (ESD) system are two of the most commonly installed SIS. The final elements of these SISs may be regarded as the most vital subsystems as they interact directly with the process. To meet required safety standards, it is required to demonstrate that the reliability of the SIS is within the assigned integrity level for the safety instrumented function of the SIS. This is done using the average probability of failure on demand (PFD_avg) for the SIS. Common methods for finding the PFD_avg assumes constant failure rate for all components of the SIS. This assumption may not be so realistic for the final elements which are subject to degradation. In this work, we consider a degrading final element of a SIS having multiple failure modes. We assume that the time to failure of the valve with respect to these failure modes follows a Weibull distribution. We approximate the Weibull distribution using a Phase Type Distribution and consider different testing and maintenance strategies for the SIS.

High Temperature Hydrogen Attack (HTHA) is a damage mechanism affecting carbon and low alloy steels in high-temperature hydrogen gas environments. This has led to major incidences in the past, notably Tesoro refinery in 2010. In 2016, a US CSB Alert was issued, questioning the efficacy of Nelson Curves in API 941, which were used for the material selection of equipment in high temperature hydrogen service, and providing guidelines on identifying at-risk equipment. This paper discusses the challenges faced in the inspection of HTHA on carbon and low alloy steel piping system in a refinery diesel hydrotreater unit and the various NDT techniques that were explored. The unit operates in the range of 400°C and 100 barg, and was found to be at-risk according to the CSB guidelines. Various NDT techniques were deployed to assess the onset and progression of HTHA, which proved to be challenging due to the surface temperature and difficulties in the detection of early-stage damage morphology. The piping material was upgraded as the long-term solution for mitigating HTHA risk.

Most of the electric distribution companies in the Philippines are interested in analyzing customer load profile, they are concerned in classifying their customer’s profile into different categories based on the energy consumption, Also the user’s profile will help to understand how the consumption of energy may affect the electric distribution grid. In the current condition right now, facing the COVID-19 pandemic, most Filipinos are inclined to work at home, thus the consumption of energy increased. In this paper, residential data were collected in one of the electric distribution companies in the Philippines amidst the COVID-19 pandemic conditions. The data consist of 1,048,575 customer profiles from the year 2021. This study aims to use clustering methods such as the K-means algorithm in grouping customers' profiles and validate the suitable amount of clusters using the proposed method, such as the multi-criteria model and elbow method. Results show that 2 and 7 clusters, respectively, were fitted in the data.

We consider the two-machine job-shop scheduling problem with the makespan criterion when one job is joint and the others are non-joint, where a joint (resp., non-joint) job is defined as a job whose operations are to be processed by different machines (resp., same machine). In this research, the machine repetition and transportation times between machines were considered. This problem is associated with real-world applications in the production planning and supply chains. We demonstrate that this problem is NP-hard when the joint job has more than two operations. We propose polynomial-time algorithms based on dynamic programming for cases with a fixed number of jobs. We also propose lemmas to reduce the number of states in dynamic programming without loss of optimality, so that the time complexity is improved. Other methodologies, including preprocessing and the two-pointers method, are also embedded. Our algorithm has better time complexity than a well-known algorithm that can be applied to our problem.

This work presents an end-to-end framework for solving online stochastic optimization problems based on deep reinforcement learning. In particular, we focus on an online stochastic version of the profitable tour problem (PTP), which is a variant of the TSP with profits. The goal is to pick a subset of customers and maximize the total profits made from these customers, from which the total travel costs have to be subtracted. Profits are modeled through time-dependent random variables, whose realizations become available online. Most classical heuristic solution methods for combinatorial optimization problems require in-depth knowledge and expertise about the respective problem. In contrast to this, a deep reinforcement learning algorithm, called AlphaZero, has recently achieved state-of-the-art performance in combinatorial games, such as chess or Go, solely through self-play. We adapt this methodology to apply it to problems of online stochastic optimization, in particular a version of the PTP. Training is performed on a set of scenarios on a per-instance basis. First computational studies have shown promising results, improving the solution quality significantly through training.

The on-demand meal delivery business is getting very competitive and customer-centric day-to-day. This paper presents an optimization model for delivering orders with priority. The proposed model minimizes the Order to Delivery (O2D) time to provide a better Customer Experience (CX). A simulation model is developed to implement the priority delivery model along with standard delivery orders. The priority delivery model is simulated with actual food order data and performed a sensitivity analysis to derive a few key managerial insights. The simulation result showed that the standard delivery orders’ CX and overall Cost Per Delivery (CPD) get impacted by an increase in the percentage of priority orders.

With the recent trends in energy transition, fuel retail businesses need to reposition themselves to meet the challenges and seize the opportunities for growth. The investment needs to be made in outlets, technology and infrastructure to enhance customer experience and cater to the new fuel landscape. In this work, we will assess the suitability of a location for a retail outlet in oil and gas industry. Multiple factors such as CNG sales potential, EV infrastructure, proximity to other markets will be considered to assess the resilience of a site in times of energy transition. The factors determining the suitability of a site will be potential of traditional fuels and non-traditional fuels such as CNG and renewables, EV Charging Infrastructure etc. The sites need to be assessed for their contribution in network enhancement in current times and in future. We have used MCDM method TOPSIS to arrive at the holistic evaluation of a site. This input can be further utilized in an optimization problem to design an optimal network.

Hotel guests’ experience and satisfaction are important aspects of the hospitality industry. It is influenced by several hotel attributes. With the emergence of Covid-19 pandemic, changes in important attributes for customers need to be studied. This study utilized textual reviews and ratings from Tripadvisor for hotels in Indonesia in 2019 and 2021. Topic modeling and opporunity algorithm were applied to identify important attributes, calculate each attribute’s level of importance and satisfaction, and conduct opportunity. This study found there are changes in the important attributes before and during the pandemic. Finally, the opportunity algorithm was computed to find attributes that need to be improved. This study found that the latest service opportunities in Indonesia arising from the Covid-19 pandemic are “health protocol” and hotel facilities such as fitness/gym centers and internet connections to improve room comfort.

Nowadays, convenience store becomes more important to urban life. It is also a competitive industry for retailers who would like to gain attention from customers and grow their profit. To achieve their goals, doing promotion is a way to go. However, promotion has two sides where the gain is also coming with the possibility of loss, so a thoughtful decision is concerned when doing the promotion. This study aims to develop agent-based simulation, an approach to deal with complex and high-dimension problems, to find how each strategy works on different price elasticities for strategic insights for the company’s future planning. The agent-based simulation is able to reach many possibilities of combinations between strategy and price elasticity instead of testing or gathering data in the real world. To achieve our purpose, we vary strategies while fixing the price elasticity and see how customers react in each situation. The customer’s decision is initially based two factors (advertisement effectiveness, and word of mouth), and then it is based on price reduction rate when they come to the store. The strategy is designed on two dimensions, percentage of price reduction and frequency of the promotion. The result shows that different strategies work on different price elasticity values where high price reduction rate strategy works well on high price elasticity, and vice versa. This study provides an insight about promotional strategy selection and future vision for a new method to approach complex problems.

Software debugging is complex and challenging task for developers and testers. To increase the active debugging performance, several fault localization techniques have been widely proposed. Deep learning techniques have recently shown the promising potentials of many kinds of neural network architectures and applied them to fields of fault localization. In practice, it is known that the hyper-parameters of deep learning model are not easy to locate for every program or dataset. Therefore, hidden information of coverage data may not be extracted accurately and incur performance degradation. In addition, as program scale becomes larger, do not directly use whole program as input to deep learning model by removing some unnecessary statements of program is very essential and useful. In this paper, we present an effective preprocessing and post processing weight method for deep learning-based fault localization to identify the location of faulty statement. The proposed methods are evaluated on well-known two open source linux utility programs (gzip and grep). The experimental results demonstrate that our proposed methods significantly improve fault localization performance compared to the past methods.

Over the years, false news has polluted the online media landscape across the world. In this “post-truth” era, the narratives created by false news have now come into fruition through dismantled democracies, disbelief in science, and hyper-polarized societies. Despite increased efforts in fact-checking & labeling, strengthening detection systems, de- platforming powerful users, promoting media literacy and awareness of the issue, false news continues to be spread exponentially. This study models the behaviors of both the victims of false news and the platform in which it is spread— through the system dynamics methodology. The model was used to develop a policy design by evaluating existing and proposed solutions. The results recommended actively countering confirmation bias, restructuring social networking sites’ recommendation algorithms, and increasing public trust in news organizations.

Companies strive to be more efficient and constantly increase manufacturing productivity to stay competitive. The Overall Equipment Effectiveness (OEE) is a relevant performance measurement that companies use to monitor efficiency, quality, costs, and the capacity of their production lines. A case study in a pharmaceutical company was conducted to see if additional methods alongside the OEE could help improve the production planning, capacity utilization, and output of a packaging manufacturing line regarding production speed, demand size, and cost per item. Therefore, the study utilized theoretical concepts from the literature with empirical data to develop a simulation model for this specific manufacturing system. A time study and a discrete event simulation were used, and the solution showed acceptable and coherent to real numbers. In addition to bottlenecks identification, the simulation enabled the estimation of an optimal number of operators and the gains achieved by implementing changes in the manufacturing processes. It was concluded that the simulation model could help to improve the production planning and, subsequently, the capacity utilization and output of the manufacturing line.

Substitution is known as an efficient strategy to mitigate the supply chain risk in dealing with demand uncertainty. If efficiently designed, it can reduce shortage and holding costs. A well-known example of substitution practice is in blood transfusion of different compatible blood types at hospitals or emergency departments. Among different blood types, O-negative is most commonly used for substitution, due to its compatibility property. This research focuses on ordering policy with consideration of effective substitution decisions for red blood cells at hospitals and emergency requisition from the blood service with consideration of the optimal substitution policy. We consider demand and supply as stochastic. The mathematical modelling approach to the problem is by considering a stochastic optimisation model under substitutions, uncertain demand, stochastic supply, perishable items with fixed shelf life and the age of items in inventory. To improve the performance of blood supply chain, the outdates and shortages and the age of transfused items should be minimised.

Mitigating climate change requires workable solutions that meet the agreements of the Paris Climate Agreement. The basis for this is the creation of a common understanding of the processes of individual industries. Transport logistics in particular has a key role to play here, as it is responsible for a large proportion of direct and indirect emissions both nationally and globally. The combination of digitization and sustainability is of central importance here. In particular, with regard to the data generated in transport logistics. A common scientific understanding can be achieved through the creation of a systematic literature analysis. In order to meet the requirements of scientific knowledge acquisition, it is necessary to create a protocol that represents the framework of the systematic literature analysis to be created. The present publication creates a systematic literature analysis protocol to investigate the current scientific discussion regarding the intersection between digitization and sustainability, especially with regard to the resulting data.

Making effective and efficient decisions for the supply chain system is essential. The Location Routing Problem (LRP), which simultaneously identifies the center of facilities and vehicle routes, is one of the models that could be utilized to make wise selections. The Multi-objective Multi-Compartment Split Delivery Location Routing Problem With Time Windows (MOMCSDLRPTW) is the LRP model developed in this study. Multi-compartment vehicles, split deliveries, time windows, and capacitated DC are extra factors from conventional LRP. Minimizing the overall cost and maximizing the service level are the goals. The non-dominated sorting genetic algorithm (NSGA) II is used to solve the model. In the subject case, the findings reveal that the Split Delivery (SD) model is suitable for nodes' proximity, the retailer's longer tolerance time windows and higher demand.

Electric vehicles have become an important topic for researchers and practitioners. Unlike a general vehicle routing problem, the electric vehicle routing problem has problems of charging station. A challenging problem in the electric vehicle routing problem is to find optimal locations of charging stations and routes of vehicles called the location routing problem. In this research, we investigate the location routing problem of electric vehicles. In the last mile delivery, parcel lockers are used to reduce the complexity or the costs of transportations. Thus, we also consider the parcel lockers in the location routing problem. Numerical examples present the viability of the optimal strategies for the location routing problem with parcel lockers.

This research proposes a framework of key enabling factors for improving supply chain resilience (SCR) capability from the perspective of supply chain integration. The research methodology consists of three stages: (i) the enabling factors of SCR are identified through a comprehensive literature review of case studies of major disruptions and SCR, (ii) the Q-sort method is applied to classify those factors according to the experts’ opinions, and (iii) the causal relationships among the factors are determined using the fuzzy DEMATEL approach. Of all activities associated with supply chain integration, information sharing with suppliers emerges as the most prominent factor for SCR. The influential enabling factors and their causal relationships can be used to prioritise those factors and develop guidelines for improving SCR.

Supply chains have evolved into vast ecosystems and have grown more dynamic in recent decades, yet they still lack flexibility and scalability. Supply chain management (SCM) applications benefit greatly from the capabilities of blockchain technology (BCT), which enables the creation of a distributed ecosystem. Since the need for blockchain rises, so does the desire for chains that are scalable, flexible, efficient, and economical. Sharding technology has recently been developed to address the issues associated with blockchains. In this work, we propose a blockchain architecture, a scalable and efficient supply chain framework that keeps all information locally, to allow businesses absolute control over data. We are proposing a sharded design, which divides the ledger into multiple chains to enable scalability for processing transaction load. A qualitative analysis has been performed to prove that a scalable network can maintain maximum throughput in a dynamic environment.

In view of eroding market shares of inland waterway transportation, innovative waterborne transport service concepts and new business models are needed. In order to safeguard the successful adoption of the new IWT services, it is of utmost importance to prove their economic viability and present the individual gain for all parties and stakeholders involved. One of the parties involved are the inland waterway hubs that connect the new services to the existing service landscape. By examining the effects of the service on hubs and proving its viability, an important hurdle is taken on the way to real-world application.With the help of discrete-event simulation, the performance of the envisioned decentralized waterborne container transportation service can be examined. Numerous scenarios have been developed and examined in order to develop an understanding of the service under miscellaneous conditions. The scenario results can be used for a closer look at the performance results of the IWT hubs and a comparison with one another. By ensuring a good economic performance of the IWT hubs involved, the realization of the service concepts appears more likely.

At present, disasters frequently occur throughout the world. Due to different cultural backgrounds and organisational structures, most countries adopt network governance, hierarchical organization, and centralised management. However, the effect of management is often not satisfactory. Therefore, this paper takes the outbreak of COVID-19 in 2019 as a case to explore whether complex systems management can provide ideas to disaster response. The study demonstrates the need for complex systems in disaster response by conducting an in-depth analysis of response data in China and Australia, using the case study of the 2019 pandemic outbreak.

During the last decade, the need for an effective and efficient public system has become increasingly important to many countries. For endemic reasons, private entities have always been deemed smarter and faster than public organizations. The present study reflects a first specific investigation of the so-called “public efficiency” related to the Public Project Management, collocating itself in the ground of the New Public Management. Given the nature of the subject, which is highly complex and considers various factors, the proposed scientific approach is the use of Fuzzy Cognitive Maps, a soft computing strategy for causal information acquisition supporting the reasoning process. The case study selected to accomplish this preliminary analysis concerns a public university in the center of Italy, focusing on the administrative process of approving of an R&D project proposal.

This paper presents the development of a strengths, weaknesses, opportunities, and threats (SWOT) analysis for implementation of a Lean management - Agile methodology (Lean-Agile) mindset in an engineering-to-order (ETO) organisation. The Lean-Agile mindset enables the improvement of the workplace culture, quality, and productivity of the project teams. The SWOT analysis provides a framework for identifying challenges associated with implementation of a Lean-Agile mindset in the enterprise such as the deployment of a new digital tool. A SWOT analysis was performed in a Norwegian ETO organisation that delivers products and solutions aiming to cut emissions and limit global warming via smart power generation and energy storage. This manuscript first describes the concepts of Lean management (LM), Agile methodology (AM), and the use of SWOT analysis based on the available literature. Next, it presents the case study performed in the ETO organisation. The study findings demonstrate how SWOT analysis can support the decision- making process related to the implementation of the Lean-Agile approach in an ETO organisation.

With the development of cities, more and more countries around the world are facing the problem of insufficient land resources. In the context of environmental protection and arable land resource protection, facing the conflict between land and development, this paper introduces an intensive construction land use policy from China - the construction land reduction (CLR) policy. At the same time, considering the special and complex nature of such projects, to ensure that the CLR projects can achieve the expected quality objectives, this paper constructs a quality evaluation index system and improves it with the Delphi method. Finally, the weights of each index were determined by using a combination of the DEMATEL and entropy weight methods. Through this paper, we aim to provide a solution for other countries with similar land challenges, provide a systematic system for quality evaluation of reduction projects to help project quality control and grasp, and also provide a reference for similar projects to build quality evaluation systems.

In this paper, we consider the RRP/max which takes procurement costs and time-dependent renting costs for resources, used in a project, into consideration. The objective of the RRP/max is to determine a time-feasible schedule for a project while minimizing the total resource costs to execute a project with general temporal constraints. Applications for the RRP/max arise e.g. in planning the operations of heavy machinery at construction sites. The renting of resources, rather than buying them for a project, has increased in relevance in recent times. We propose a MIP-based heuristic approach to solve the RRP/max. By adapting a fix-and-optimize heuristic to the structural properties of the RRP/max, we were able to find promising solutions for the problem. In a computational study, we investigate the performance of the heuristic for instances from the literature, by comparing the results to those of a MILP model solved by CPLEX.

Negotiation is widely known as the most effective way to resolve disputes. Negotiators’ intention to settle (ITS) is considered the prerequisite of a negotiated settlement. If one or both engineering negotiating parties lose their settlement intention, the negotiation is expected to fail. To enhance the success chance of negotiated settlement, it is imperative for the disputing parties to maintain certain level of settlement intention. This study puts forward ways to identify ITS. Potential indicators are first longlisted from a thorough literature review. A conceptual ITS framework was proposed. The ITS framework has six elements: preparation, integration, goodwill, continuity, commitment, and self-efficacy. To test the proposed framework, quantitative data were collected from 171 experienced practitioners. A partial least squares structural equation modeling (PLS-SEM) approach was performed, and the results showed the statistical significance of the framework, thus confirming that negotiators’ intention can be represented by the six factors. The study theoretically contributes to dispute management by explaining the underlying formations of negotiators’ settlement intention. In practice, the identified ITS elements could serve as negotiation practice guidelines if a settlement is desired.

The value of a new product is determined based on its conventional functional value, such as its usability, and its added design value. The product design should match the Kansei of a customer and the product concept. A design has a low value if it fits the concept but creates a new product similar to other products. This paper proposes a method to create novel designs that match the concept of a new product. The proposed method is developed by adding an index to attribute values using the rough set theory. We develop a technique to create stylish and unconventional leather shoes. Furthermore, the relevant design is created using 3D computer graphics. Through verification, we create stylish and uncommon leather shoe designs at 62.5%.

Increased competitive pressure requires companies to increase their own innovative capacity. In particular, innovation management must identify and analyze the external dimensions of influence that affect the product. Although large amounts of text data are available for this purpose, there is yet no possibility of evaluating them in a structured way and linking the information obtained with each other in order to generate knowledge about the product environment. The presented method enables the derivation of innovation potentials from these text data. First, the external dimensions of influence that affect the product and are thus relevant for the innovation process are identified. Each influence dimension also defines data sources that can be used for the analysis. When analyzing the document collection from the data sources, applications from the field of text mining extract the topic areas of the available documents. Finally, a semantic network links the knowledge gained and shows the dependency of the identified topics in order to generate knowledge about future innovation potentials. The method was validated with a use-case from the sports equipment industry.

Availability of spares is essential, particularly for capital-intensive businesses, to retain the cost as a competitive advantage (order winner) with high service levels. Its unavailability may result in obsolesce of the machine. Additive Manufacturing (AM) or 3D printing has become mature enough to be utilized in manufacturing functional spares. Even though there are enough case examples, there has been limited research in identifying the most suitable spare parts for AM. Thus, there is a need to develop a methodology suited to various industries without discrimination by assessing all parts together. In identifying compatible spare parts for AM, the initial step is to determine the generic criteria used across various sectors that influence the decision of part identification for AM. These criteria are clustered into Business Impact (BI) and Technical Compatibility (TC). The BI includes 30 criteria, and TC includes 21 criteria. Further, the Delphi approach is employed in determining the generic and essential criteria used across various industries. The findings result in the retention of eight BI and seven TC criteria using the consistency validity ratio (CVR) score.

Like the development of new products, the training of new engineers is also subject to constant change. The transfer of knowledge is increasingly taking place digitally, for example with the help of virtual reality. The research work presented here investigated the learning effect of using VR-supported teaching methods in engineering education. For this purpose, a VR-supported teaching scenario was designed and tested by a student test group. The learning effect was then checked with the help of a survey. It was shown that VR makes it possible to link theoretical knowledge and practical application. VR is therefore a good alternative to purely online teaching, but cannot replace face-to-face teaching. Future research activities should answer the question of how existing didactic methods can be implemented well and efficiently in VR.

The objective of this article is to develop a model for predicting marbling scores of Brangus beef fattening cattle breed using the Logistic Regression technique. Using 2014-2021 data (8 years) from cow fattening cattle of Kamphaeng Saen Beef Cooperative Ltd. with a total number of 1,040 cattle. The data include breeder name, fattening period (months), age of cattle from shedding, tooth wear, live weight when processing, cold carcass weight, and Beef Marbling Score (BMS). Using the percentage split method to test the predictive model. By setting, the size of the training set to 10 to 90 percent with an incremental of 10. It found that the highest accuracy was 59.62%, and the RMSE was 0.34. Due to a lack of main factors such as feed and the season of the cows, enter the beef meat processing. The beef marbling score forecasted by the logistic regression technique can use as a guideline for predicting fat insertion scores, farm management, and selection of cattle breeders.

The chicken meat supply chain in Indonesia is fraught with halal risks due to a large number of involved actors. Unhandled halal risks can lead to problems that chicken status changes into non-halal. It is important to identify and prioritize the halal risks in order to identify which risks should be handled first as a preventive measure.This paper aims to assess the halal risks of the chicken supply chain using the Risk Priority Number (RPN). Fuzzy logic is implemented to reduce uncertainties and ambiguities in all degrees of halal risks. Total respondents representing halal chicken supply chain entities completed a questionnaire to assess severity, occurrence, and detection. Then the overall fuzzy RPN is calculated using Mamdani's method. The findings reveal ten critical halal risks, with halal logo apathy among consumers being the most critical risk in the halal chicken supply chain. This paper also offers recommendations for consumers, slaughterhouses, distributors, and regulators to ensure halal chicken meat circulation.

The advent of the Fourth Industrial Revolution has induced radical and abrupt changes to existing business models, challenged established business rules and in some instances brought discontinuities to many industries. The complexity of the Fourth Industrial Revolution can be illustrated by the technology trends and agility associated with it. In addition, companies require new “clockspeed” and flexibility to deal with dynamic Fourth Industrial Revolution technological innovations.Thus, there arise a need to understand organizational capabilities for coping with brought about by Fourth Industrial Revolution technologies and formulate frameworks and guidelines for creating new organizational spaces where those capabilities can be developed. The main objective of this paper is to conduct a hybrid of integrative literature review and exploratory study to determine why organizational capabilities are important for the Fourth Industrial Revolution adoption and implementation in the manufacturing industries. This paper illustrates possible benefits and impacts of reconfiguring the organizational capabilities viewed from the J-curve methodology as well as a proposed approach for managing organizational capabilities for the Fourth Industrial Revolution technological innovations.

Startups are under constant pressure to raise capital and resources to achieve their development objectives. Deep technology (DT) startups in particular are often associated with uncertainties for investors and pose special requirements for financing due to, for example, long development cycles, the need for extensive expert knowledge or markets that have yet to be developed. A major problem in raising capital for startups is the information asymmetries and the lack of understanding between them and investors. These challenges often result in a lack of the necessary resource support for promising ideas through equity financing with investors. Consequently, the full market potential cannot be realized, or the startup fails as a consequence of missing capital. Against this background, this paper aims to conceptualize a methodology to provide startups with lifecycle-dependent recommendations for their financial marketing towards investors. To do so, existing approaches in literature are discussed and analyzed regarding their deficits. Based on these findings, a model is conceptualized combining both the – lifecycle-dependent perspectives of startups and different investor types to derive optimal investment scenarios for both parties.

During the recent years, manufacturing companies were forced to overthink their technology strategy more frequently and outside of their formal planning and budgeting cycles as severe disruptions (e.g., the Corona pandemic, the war in Ukraine or the global semiconductor crisis) exogenously changed the rules for competition. As volatility of global markets and value creation is expected to stay or increase in our digitally hyperconnected world, turbulence-induced strategy processes need to complement the formal planning cycles. Therefore, the present paper seeks to conceptualize a turbulence-induced initiation phase, which is either left out or not emphasized in existing approaches for strategy development. For this, a model concept is elaborated based requirements derived from deficits in theory and a reference modelling for the initiation phase of strategy development. As a result, five component models are presented in a three-layer structure to guide practitioners with the turbulence-induced initiation of their technology strategy development.

This manuscript investigates the role of the preventive quality of photovoltaic (PV) systems in willingness to pay (WTP). We build on existing studies to investigate which factors influence WTP behavior through a survey with 284 respondents in Finland. We find that higher WTP is associated with higher concerns for preventing emissions and climate change, the need for more public talk on environmental issues, and an overall favorable attitude towards PV technologies. Since WTP behavior concerning PV panels is highly context-specific, we discuss our findings with earlier WTP studies of different contexts and provide future research avenues.

The additive manufacturing (AM) technology Fused Filament Fabrication (FFF) and topology optimization (TO) for the manufacturing of robot grippers shows high potential regarding material efficiency and weight reduction, leading to CO2-emission reduction. For conventional series production, steel or aluminium gripper elements are machined by milling, drilling, and turning. In determining the ecological efficiency of a circular economy concept with topologically optimized additive manufactured gripper, a CO2 assessment is presented considering the cycles of raw material, production, usage, and reuse or recycling of the gripper. The CO2-emissions of both a conventionally designed and a lightweight gripper with application of TO and FFF is compared within this assessment.

The implementation of challenge-led innovation programs is intrinsically a systems engineering problem. Technology roadmap is a visual chronology of strategic intent and is mathematically a network of dependencies among technologies and products with timestamps. To analyze complex technological systems, intermediary layers with hierarchical decompositions are added between technology and product layers. Roadmapping for innovation challenges constitute a special case because they are implemented bottom-up; roadmapping architecture can be configured to explore, ab initio, multiple potential technology-agnostic pathways that would resolve innovation challenges. We analyze two innovation programs in the roadmapping view: A same roadmap is analyzed in Gantt chart, dependency structure matrix (DSM), and directed acyclic graph (DAG) to interrogate dependencies among basic, applied, proprietary knowledge, manufacturing, and commercialization at increasing granularity. We compare task parallelization informed by critical path, clustering, and longest path methods. Using this result, we propose a roadmapping architecture for innovation missions. Those interested in our findings would include innovation managers who wish to apply graph theory to quantify relationships and find bottlenecks within complex technology roadmaps.

Since the outbreak of the Covid-19 pandemic, masks have been widely used as a personal protective equipment (PPE) to prevent respiratory infection. A major type of masks used is non-woven fabric mask (NFM), which is currently classified as domestic waste and mostly disposed to general rubbish bins then eventually sent to the already saturating landfills. Moreover, the contaminated NFM is not disinfected properly during the disposal, which increases the risks of viral transmission and pollutes the environment. To alleviate the existing pressure to the environment, the amount of used NFM being disposed to landfills should be reduced. This paper studied the feasibility of recycling the used NFM and developed a prototype of disposal machine as the primary recycling process. By inserting the used NFM into the disposal machine, the masks can be shredded, disinfected and packed for further recycling processes.

Preventive maintenance (PM) widely exists in the operation process of the production system, which affects the transient and steady-state performance analysis of the production system. Production system modeling is the process of revealing the relationship between component characteristics and performance measures. In this paper, a new modeling method for serial production systems with exponential machines considering periodic PM is proposed. By using this modeling method, the transient and steady-state analysis of the production system can be performed. This model can be solved without using the traditional iterative procedure. The numerical case studies demonstrate the accuracy of this method compared to the simulation results. According to the analysis results in this paper, PM can significantly improve the production rate of the production system, so it should be taken into account when modeling and optimizing the production system.

The objective of this research is to develop and validate leanness assessment methodology of New Product Development (NPD) process. This methodology will facilitate machine tool designers to verify performance of NPD process carried out in the context of evolving Smart Manufacturing Systems (SMS). The methodology is based on identification of key parameters which define an SMS and mapping these key parameters to five lean principles. A lean assessment score is proposed to evaluate New Product Development process based on three indices namely Requirement Satisfaction Index, SMS Design Index and Data Analytics Capability Index. The proposed methodology is validated by inputs from 57 experts and stakeholders. The effectiveness of proposed methodology has been established by evaluating proposed lean performance scores and is demonstrated and validated using six industrial cases of NPD in the context of Indian machine tool industries. Leanness assessment of NPD process facilitates designers to carry out product development faster, cheaper, better and smarter. Research literature is abundant with tools and technologies used in an SMS environment. But, how to leverage these tools and technologies derive value is lacking. The assessment methodology proposed here based on three lean assessment indices will immensely help machine tool designers to evaluate the efficiency and effectiveness of their NPD Process when it is carried out in an environment of smart manufacturing system and Industry 4.0 paradigm.

In our contribution, we consider how the Digital Twin of the production system can be combined with Artificial Intelligence methods. The result of these considerations should be a hybrid production management system, which can be used for decision support as well as for self-control. On the one hand, the Digital Twin is providing information and simulation for the optimal decision-making to the decision support system (human interaction). On the other hand, Artificial Intelligence is taking over some elements and tasks with automatic control (independent of human interaction). If the result is out of tolerance or if a random sample of the result is audited, the decision support system is validating the Artificial Intelligence result for execution (by human interaction). The hybrid production management system is to be used to control production processes and generates learning effects. The extension by methods of Artificial Intelligence opens possibilities to process and to master certain tasks independently of human interaction. The presented approach should relieve and support decision makers in production. By performing various actions automatically and achieving partial auto-control, the reactivity of linked processes can increase. The provision of decisive information favors decision quality and time-effectiveness.

Due to complex geometries, filigree structures, and difficult clamping conditions, the manufacturing of implants in single-part or small-batch production is challenging. The intelligent manufacturing cell presented here can minimize the effort for process setup and ensures constant product quality through process control. The cell independently evaluates the quality of the machined implants using tactile and optical measurements and, if necessary, adapts the tool paths. This allows for reducing shape deviations up to 69% while reducing certification effort.

Reliability analysis is one of the most important aspect when conduct performance evaluation of reconfigurable manufacturing system (RMS). Due to the characteristic of such system, its failure rate is piecewise defined, which causes complexity of reliability analysis. There are different kinds of reconfigurability characteristics and configuration order is one of the reconfigurability characteristics in such system paradigms. This paper propose a framework to assess reliability performance of manufacturing system with different configuration orders. In the framework, Markov transfer state of Markov chain is applied in presenting configuration order during processing. Then, different reliability assessment methods with different fault management policies are illustrated.

The production and delivery of ready-mixed concrete consist of several activities which expose employees to considerable safety hazards that lead to occupational accidents. The study aimed to determine the significant factors contributing to non-fatal occupational accidents in the ready-mixed concrete industry in Qatar. A total of 84 non-fatal occupational accidents were collected from a ready-mixed concrete company in Qatar. A coding scheme was developed based on available literature and the company’s existing classification to analyze each accident in terms of employee, injury, and accident characteristics. Cross-tabulation analysis was used to determine the associations between categorical factors. Frequency distribution showed that regular employees aged between 25 and 44 years old were mostly involved in accidents, with hand/arm and leg/foot being mostly injured. The most common accident types were struck by an object (44%), slip, trip or fall on the same level (23.8%), and caught in between objects (17.9%). The majority of work-related accidents (71%) could be attributed to unsafe behavior, particularly due to improper use of PPE. Strong significant associations were observed between activity type and accident root cause, as well as injured body type and accident root cause. The significant factors contributing to non-fatal occupational accidents were determined through the study. The insights obtained from this study could be used by the company in designing a comprehensive safety management program with the goal of driving occupational injuries to a lower level.

This research is the initial phase of an extensive study that will verify the relationship between interiors and increasing positive emotions of users in health facilities. The stages reported in this journal are the stages of clarifying virtual reality tools, intending to know the safety of virtual reality tools for interior visualization trials in children. The next stage reported was three stages of visualization of the chemotherapy room. First, it is with the original condition, then changing the color of the walls to a bright and striking color, and finally with calm color. This analysis also wants to prove the readiness of children to determine design preferences. This experiment is conducted on children over nine years and under nine to determine the preferences of colored children. Based on the experimental results, children aged nine years and over are known to be more able to decide on a clear preference for color than children aged under nine years. The test method uses virtual reality to experiment with visual stimulation in the form of color, accompanied by in-depth interviews.

This article illustrates the transformation of a traditional car manufacturer (Volkswagen) into an IT driven company. In order to succeed in this change process, a substantial transformation of workforce and workspace is necessary. Compared to manufacturing workforces, IT workers require a very different culture when it comes to operations, internal mindset, and office design. It causes a radical change from single desks and offices to open, inclusive, collaborative, multifunctional, and intuitive workspaces. Furthermore, considering the company's rapid growth over the last two years to its current scale of 5000+ employees, its achievements are remarkable. They represent a major break from habits, cultural norms, and working methods. In addition to Volkswagen, this applies to the entire automotive industry as a whole in this article, a review process with employee involvement is discussed, as well as the guidelines for successfully introducing the new work and collaboration model called _spheres®. All phases of concept development should be conducted with a high degree of employee involvement and transparency.

This study examines which phase of the lab has more errors than the others. Errors at the research laboratories have three phases named Pre-Analytical, Analytical and Post-Analytical. The objectives of this research include investigating the principal causes of laboratory errors, the types, frequency and magnitude of the errors. This is the reason why Pre and Post Analytical examination are equally important for assessment. It further proves how the errors can impact the performance of laboratories, and if the quality is a mutually inclusive, dependent or independent variable from performance. The study used a quantitative method to gather the data using questionnaires. The results revealed that other types of lab errors contribute a lesser impact in lab testing more than human errors. However, every error has an impact on the overall performance of the laboratories thus the quality standards it sets for a specific organization that practices lab testing.

Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas turbine component Product Lifecycle Management (PLM), repairing components in frequent intervals can extend the lifetime expectation of the product, provide higher cost efficiency compared to newly produced components, and even improve the part design during the repair cycle. Another aspect of repair concerns sustainability, as products often contain rare materials. The emissions produced by the repair process are usually smaller than mining materials and casting new components. To optimize the repair process further, we propose the Smart Expert System (SES), which assists engineering experts with machine learning-based decision support throughout the repair process. We elaborate on its IT architecture and present machine learning models employed for representative MRO use cases. The SES is evaluated using actual industry data from a leading gas turbine company and demonstrably fulfills formulated requirements concerning the suitability of the overall decision support and the stability of the enclosing IT architecture.

Water losses and maintenance difficulties are some of the common problems faced by the current water pumping infrastructure systems globally. In South Africa, water pumping infrastructures have significant management challenges. A centralised smart digital water management system could be a solution, not only for improving the old water infrastructure but also to solve the global water crisis. The implementation of data-centric water pumping infrastructure makes it easy to disseminate an assets’ data. Organisations have access to data on-demand, in real-time, even if the system is disrupted. Integrated business processes through systems enablement, such as Enterprise Resources Planning (ERP) is adopted in conjunction with having an effective maintenance management system in place. The adoption of an Integrated System Architecture Model allows data security, control, digitalisation, monitoring in real-time, improve the overall productivity, lower operating costs, and integrate all business operations, including maintenance, engineering services, management and create an environment that is driven by innovative strategies to monitor assets remotely.

Globalization is increasing the level of market competition and companies are reviewing every aspect of their business to gain a sustainable competitive edge. Maintenance is one of the aspects gaining more attention. This is not true for developing countries as maintenance is still considered a necessary evil and is only done when there is a breakdown with little to no planning. Due to this reason, a significant amount of money and time is wasted doing tasks that do not improve the condition of the plant. Good maintenance strategies improve plant availability and reliability. The current research proposes the use of decision-making grid (DMG) to select maintenance strategies. We used DMG to select maintenance strategies in a ferrochrome plant and found that there were opportunities to use more aggressive maintenance strategies as this will enable plant technicians to focus on core maintenance activities and give production personnel an opportunity to fix simple plant breakdowns reducing downtimes. It was also noted that data capturing needed attention for the ferrochrome to be able to use available data to make more informed decisions.

South African (SA) municipalities are unable to sustainably manage stormwater assets because of the inability to incorporate Artificial intelligence (AI) and Best Management Practices (BMPs) into the existing stormwater asset management (SAM) frameworks. The research aims to develop a framework that interconnect conventional and BMPs into a single system that reduces the risk of stormwater asset failures. The study focused on quantitative and qualitative data analysis techniques. This resulted in the performance of desktop study and visual condition assessment of stormwater assets, focusing in the inner city of Johannesburg in Fordsburg. Risk-based asset management (RBAM) methodology is conducted, focusing on the asset management cycle's core components; asset inventory, level of service (LOS), criticality; life cycle costing (LCC) and long-term finance. The assessment indicates that most of the stormwater assets are slightly in good conditions. The performed risk analysis indicates moderate POF and consequences of failure (COF). There is however procrastination by the council in adaptation of stormwater BMPs. Stormwater infrastructure require extensive re-investment in capital and methods of management to ensure world class infrastructure.

Nowadays, processes are equipped with advanced tools; therefore, they produce near zero-defect items and are termed as high-quality processes. The high-quality data often follows the Zero-Inflated Poisson or Negative Binomial (ZIP or ZINB) distributions. In literature, most surveillance methods are designed to monitor ZIP and ZINB distributed quality characteristics. However, some covariates are also available along with the count-based quality characteristics of a process. Therefore, this study is intended to propose moving average (MA) and double MA (DMA) based surveillance methods designed on the ZIP and ZINB residuals (i.e., Pearson). A simulation-based study is carried out to evaluate the performance of proposed methods and their comparative results with an existing method using run-length properties. The findings reveal that the proposed MA and DMA methods outperformed the existing Shewhart chart. Moreover, a real-life example is presented, which supports the simulated results.

Prognostics of Small Bore Piping (SBP) degrading due to fatigue deals with estimating its remnant useful life (RUL). This manuscript elaborates the RUL prediction procedure for SBP. Physics-based model is utilized to estimate the RUL, and the uncertainty in the different parameters of the Paris law are quantified and propagated. According to Paris law, crack growth per cycle is proportional to Stress Intensity Factor (SIF) which in turn depends upon initial crack size (ICS) and stress range. ICS is generally estimated using the Non-Destructive Examination techniques while the stress acting at the interface of SBP and the mainline piping is determined using Fluid Structure Interaction (FSI) performed using ANSYS software to couple CFD and FEA analysis. Finally, the predicted RUL is employed to estimate reliability and frame inspection interval for SBP which shall ensure mitigation of hydrocarbon leak at the process facilities.

Overhead Track Equipment (OHTE) is regarded as the least reliable subsystem of the railway electric traction system. The purpose of this paper was to investigate the OHTE system failure pattern in the South African railway sector and predict future failures, as well as the failure modes responsible for system failures. The AC OHTE failures were found to be random, and they were predicted to remain constant for the next four years if no changes were made to the system. Broken parts and loose connections were the primary contributors to system failures on the AC OHTE. If no changes are made to the system, the DC OHTE will resemble an increasing failure pattern and is expected to increase over the next four years. The main causes of DC OHTE failures were missing parts and loose connections. The study relied on secondary data and to gain a comprehensive understanding of the causes of OHTE failures, future research should rely on primary data and interviews.

The design of supply networks that are resilient to disruptions has recently attracted considerable attention. We consider supply networks where a set of clients are served from a set of facilities. The cost of serving a client from a facility is proportional to the distance between the client and the facility. When a facility becomes unavailable due to a disruption, its clients are reassigned to the closest facility that is still operating. The network is resilient when disruptions cause only moderate reassignment costs. One way to design a resilient network is to solve the fault-tolerant k-median problem. Under this problem, a set of k facilities (medians) must be located such that the sum of distances from clients to their r nearest facilities is minimized. This paper introduces a new algorithm for large-scale instances of this problem. Using a benchmark instance with close to 10,000 clients, we demonstrate that our heuristic consistently devises better solutions than the state-of-the-art approach in much shorter running times.

This study addresses the problem of academic timetabling for short-term revenue generating online training programs. The goal of the problem is to find an assignment of faculty to various modules of the training program by incorporating faculty preferences for lecture topics without violating the precedence relation among lecture topics and by assigning the lectures as per faculty preference. We proposed an Integer Programming model and tested the applicability for a real short-term certification/Executive program in one of the premier institutes in India (IIT/IIM). We analyzed the importance of the fair distribution of modules among the faculties and provided the optimal faculty-module assignment for each slot.

Rail freight is essential to the economic growth of every country because it offers reliable and affordable freight services. Developing appropriate and integrated operational research models is essential for rail freight operators (RFOs) when it comes to strategic decision-making and operational policy creation. This study focuses on overcoming fleet planning issues for RFOs when transporting rakes across the country via express-ordinary transportation at record transit times. First, we developed a mathematical model (MILP) that combines optimal fleet size, rake assignment, and scheduling for RFOs to maximize revenue. In addition, we recommend a two-phase greedy search heuristic for solving larger and more complex issues. The model attempts to explain how to accomplish the optimal trade-off between freight prices, turnaround time, and selection of ordinary-express service. The proposed heuristic gives results for small and medium-sized cases that are nearly as good as those produced by an exact technique (CPLEX) and also produces optimal answers to all large problems in a very few minutes. Models like this can help RFOs better manage their daily operations and their potential for growth in the future.

The vehicle routing problem is the problem of minimizing the traveling distance of vehicles under the condition that every customer must be serviced by a vehicle. In some cases, a solution consists of not only vehicle routes but also the schedules of the vehicles along the routes. The scheduling problem for a vehicle route can often be formulated as a linear programming problem. In this paper, we propose the vehicle routing problem with linear constraints that a vehicle route can be evaluated as a linear programming problem. Many heuristic algorithms for vehicle routing problems use local search methods and the 2-opt∗ neighborhood, the cross exchange neighborhood and the Or-opt neighborhood are often used. We call them the standard neighborhoods. In this paper, we propose an efficient evaluation method for those neighborhoods for the vehicle routing problem with linear constraints. The computational results for randomly generated instances showed the effect of the proposed method.

The execution of a project requires resources which are often distributed among multiple sites, and therefore transportation times must be considered for moving some mobile resource units or the output of some precedence-related activities. Example applications arise in hospital clusters that are sharing pools of medical personnel and medical devices, and in a make-to-order production that is carried out by several partners in a supply chain. The project scheduling problem then consists in selecting a site and a start time for each activity such that the project duration is minimized subject to completion-start precedence and renewable-resource constraints. We present a matheuristic based on a continuous-time MILP formulation.

Nowadays, sustainable reconfigurable manufacturing systems (SRMS) are attracting interest from both the academic and industrial communities. This paper addresses the multi-objective process and production planning problem in the sustainable reconfigurable manufacturing environment. A multi-objective linear mixed-integer model is presented first. Second, to solve the problem, the e-constraint and the weighted sum based-approaches are developed for the case of small and medium scenarios. To demonstrate the efficiency of the two approaches, several instances of the problem are experimented and the obtained results are analyzed using two metrics, namely cardinality of the Pareto fronts (CPF) and inverted generational distance (IGD). Finally, to help the decision-maker evaluating the best process and production plans, the TOPSIS method is applied.

Waste management start-ups require collaboration for developing circular business models to reduce the dependencies of landfills in Indonesia. This manuscript aims to describe the role of collaboration within the development of a circular economy business model. Data were collected from Instagram and the included start-ups' websites from 2 March to 11 August 2021 and analyzed using the soft system methodology framework. Based on the analysis results, this study developed a rich presentation of the collaborative relationships that the start-ups engaged in. The underlying challenges of implementing a circular business model were identified, and the findings showed that start-ups collaborated to expand their service areas and the variety of waste types that could be managed. This collaboration involved start-ups working with the government, other companies not engaged in the solid waste sector, and other start-up businesses. Collaboration between start-ups and the government for providing waste collection and treatment facilities is vital for implementing such a circular business model. This study also presents several recommendations for the government, waste management start-ups, and other industries concerning the promotion of a circular business model.

The rapid development of E-commerce has forced warehouse operations to develop towards a robotics-based system named Robotic Mobile Fulfillment System (RMFS), in which shortest path planning and conflict recognition play a vital role in enhancing the operational efficiency under multiple mobile robots movement. Compared to the traditional double-deep layout in Automatic Guided Vehicle (AGV) system, this paper proposes multi-deep based layouts in RMFS, including the modification of Flying-V, Fishbone and Chevron layouts. Under these circumstances, this paper further adopts the shortest path graph attention network in RMFS. This paper considers the Dijkstra algorithm as a baseline and compares it with Biased Cost Pathfinding methods, Anytime Repairing A-star and Flow-Annotation Re-planning methods. The shortest path graph attention network adoption in RMFS should enhance the overall operational efficiency and effectiveness under different layouts scenarios with different path planning methods.

Using discrete-event simulation (DES) in the operational phase of an automated production system (APS) places high demands on the accuracy of a simulation model to obtain valid results. A continuous adaptation of the model's parameters is necessary to reduce the deviation between the simulation and the real production system. In a preliminary study, we presented the WMS4SimPar system that uses a hybrid approach with production data and knowledge from workers on the shop floor. This paper presents a detailed presentation of the use and integration of an ontology within the system and the recommender system that uses the ontology to adapt the simulation parameters. We implemented the recommender system for a use case from a research project. The evaluation of the recommender system shows that 13 of 16 domain experts were satisfied with the recommendations.

Lead-time hedging provides a means of protection against wasting barge space in inland cargo transport. This paper studies the lead-time hedging problem in an inland transport system with one transport service provider and a shipper. The transport service provider offers barge services to the shipper for transporting products from an inland terminal to a sea terminal. The service provider determines the lead-time hedging to maximize its own profit while the shipper decides the production time to minimize its total cost. This problem is modelled as a Stackelberg game with the transport service provider as the leader and the shipper as the follower. The optimal properties of the proposed game-theoretic model are analyzed, from which the best reaction function of the shipper and the optimal lead-time hedging decision is derived analytically. We find that when the lead-time hedging is longer, the shipper’s products tend to arrive earlier. We also find that the tardiness penalty price has significant impact on both parties’ optimal decisions. A higher tardiness penalty leads to shorter lead-time hedging period, and earlier arrival of shipper’s cargoes.

From a commercialization point of view, offshore renewable energies need to be cost competitive to achieve similar scales of development as compared to fossil-based energies. In relation to keeping the costs down while minimizing environmental impacts, a combined concept that integrates wind and wave energy systems emerges as a viable solution. The concept allows for the sharing of a supporting platform and infrastructures such as mooring systems, power cables and substations while taking advantage of the potential synergy between different technologies and energy sources. However, as compared to single energy systems, the combined concept presents an additional set of challenges and design concerns due to a higher level of system complexity. This work presents a review on combined wind and wave energy systems, starting with the classification of the combined systems into different sub-categories according to their features. The mechanism of energy conversion is described followed by a brief description of the numerical modelling of combined systems using state-of-the-art tools.

This research aims to study the processing of Eureka lemon shampoo products throughout the production line and reduce production costs by using simulations using the Flexsim software. The data was obtained from Pasutara Farm located in Suanphueng district, one of the famous places in Ratchaburi Province, Thailand. The research simulated the situation by verifying the accuracy of the imported data to be accurate and true to the system in all respects from the results. Research results show that by implementing three stages of production improvement on the nine-workstation production line, the time cost was reduced by 39.58%. Therefore, based on such simulations, this establishment should improve its processes according to the results of the research.

To improve or re-engineer business processes, business processes need to be defined, modelled, optimised and managed. Thus, the aim of this study is to develop a framework that organisations can use as a blueprint to conduct business process modelling effectively. A survey was used to collect qualitative data from various business process engineers, process analysts, and process practitioners. Questionnaires were administered and structured interviews were conducted to gain an in-depth knowledge of how process experts conduct business process modelling and the tools used. The goal is to consolidate the data and use it as input in developing a business process modelling framework. Individuals were selected using a non-probability sampling procedure based on a non-random criterion. It was found that most of the selected process experts are knowledgeable about business process management and the modelling of business processes. Therefore, this knowledge of Business Process Management (BPM) was taken as input to the development of the conceptual business process modelling framework. This study may promote business profitability and customer satisfaction through the implementation of the developed framework. Further studies are required to test the implementation and performance evaluation of the business-process-modelling framework developed.

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees’ destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT*. The set-del/1-ins operation consists of three neighborhood operations, set-del, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT* is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt*, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations.

This study addresses the block layout problem that contributes to allocate the driving lanes (defined as the paths) within available yard space, in order to optimize the block size consisting multiple containers in a container terminal with an arbitrary configuration. This container block layout problem is positioned as one of the application study in the facility layout problem. A mixed-integer programming (MIP) model is developed to characterize this problem. From the computational results, the optimal location of paths has a tendency to be same interval in a rectangular yard configuration. The proposed model can reproduce the current situation of path positioning.

The cannabis industry is facing challenges with the traceability of product standards and regulations, including lack of trust, visibility, and tracking system immutability. Blockchain technology (BCT) is a potential driver of cannabis supply chain traceability for improving the tracking system process, credibility, immutability, and decentralized application of the development process. This research focuses on exploring blockchain-based traceability in supply chain management (SCM) and developing an appropriate model. The findings of this study highlight the various elements involved in cannabis supply chain traceability. A conceptual framework for blockchain-based cannabis traceability in SCM is proposed in this paper, comprising four layers: (I) cannabis supply chain, (II) data interface, (III) traceability system, and (IV) blockchain. This approach can be integrated into the smart contract, consensus algorithm, blockchain application, and digital storage.

The development of information, communication, and technology (ICT) provides opportunities for traditional grocery retailers to expand their potential market share and competitiveness. The objective of omnichannel implementation in retail is to integrate the supply chain and sales channels. However, the approach has not been widely implemented yet. This paper presents enablers and barriers of omnichannel in traditional grocery retailers based on a narrative literature review. The main enabling factors are a customer-centric approach, balancing between agility and efficiency, and developments, while the barriers are a lack of limited resources and significant technological investment. At the end, a framework for implementing omnichannel in traditional grocery stores is suggested and discussed.

Air pollution results in the deaths of nearly seven million people per year worldwide. A significant concern to every individual's health is the amount of hazardous gaseous consumed by them, leading to heart strokes or chronic obstructive pulmonary diseases resulting in premature deaths. One of the significant causes is fuel combustion by motor vehicles which poses an inevitable threat to climate and health. The research focuses on building an extensive network of battery swapping stations for zero-emission scooters. Supply chain strategies were used to determine the profitable location for a distribution, production, and assembly facility to be set up globally. The Gravity Location model and Decision Tree analysis method were performed to establish a responsive and cost-efficient network.

We consider a two-echelon capacitated supply chain consisting of one distributor and multiple retailers, and explore the optimal inventory strategies for each firm. The distributor periodically checks her inventory level and each retailer continuously checks his inventory level. We construct a dual-objective programming model to minimize the inventory costs and the inventory turnover time of the supply chain, and employ a genetic algorithm to solve the model. Subsequently, we numerically analyze the optimal inventory strategies for one distributor and three retailers, and examine the impact of limited inventory capacity on the inventory costs and the inventory turnover time. We show that the limited inventory capacity has a similar impact on the inventory costs and the inventory turnover time. Moreover, we find that the inventory costs and the inventory turnover time of the supply chain are a pair of contradictory optimization goals. Specifically, if the inventory turnover time decreases, the inventory costs will increase, which coincides with the law of antinomies. Firm managers should pay attention to inventory capacity and turnover time when they optimize inventory costs.

Checking construction specification in every construction phase is critical to ensure proper construction quality and to avoid contractual problems. However, manual review is inefficient, expensive, and error-prone. There have been efforts to automatically review specifications, but these studies are limited in their practical applicability. As a solution, the use of retrieval-based user interface (as known as a chatbot) can extract specific information from construction specifications as a user wants. For the development of an information retrieval chatbot for construction specifications, this paper tested the application feasibility of a question answering methodology using Bidirectional Encoder Representations from Transformers (BERT). By taking advantages of the pre-trained BERT, user-wanted information was successfully extracted from construction specifications. With this approach, variety of questions can be responded flexibly without time-consuming manual tasks such as labeling.

This paper focuses on 18 well-known recommended practices for information system (IS) development projects to assess their relative dissemination and effectiveness and to examine the role of standardization frameworks in promoting them. Findings from a survey of 97 information technology (IT) professionals show that most of these practices tend to be adopted, that they are associated with higher chances of success in IS development projects and that software process improvement models, project management maturity models and formal development methodologies tend to promote such practices. Findings show however some notable exceptions that may require to refine this existing set of recommended practices. Implications for research and practice are discussed.

The significant discrepancies in annual bridge inspection data in Indonesia intrigue a question about the reliability of the data. This concern suggests that there is room for improvement in the current bridge inspection practice in Indonesia. This study reviews the latest Indonesian bridge inspection manual and compares it with the current global practice in six different countries including the U.S., U.K., Australia, China, Japan, and Korea to find the gaps in the domestic bridge inspection practices. The result shows that domestic practices can be enhanced through the supervisor's involvement during the survey, extending the inspection period or increasing capacity building, improving the bridge rating system, and actively adopting advanced technologies for accurate data collection.

Change management is fundamental for engineering management and creating a lasting change within organisations; without effective CM initiatives, improvements to optimise or enhance business value through change could be challenging. This article aims to identify the literature and themes that link Change Management (CM) and Multinational Corporations (MNCs). This article follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach supported by bibliometric analysis. Considering the vast amount of literature on CM and MNCs as individual topics, the number of publications regarding CM and MNC as a combined research focus is less researched. This article forms a foundation for identifying relevant thematic topics, trends, and patterns regarding CM for MNCs.

Wood construction differs from traditional concrete materials in technical and organizational requirements for which it can be studied as an innovation, and thanks to health and climate change mitigation and prevention capabilities, wood construction can be categorized as a preventive innovation. The purpose of this study is to explore incumbent actions in the adoption of wood materials. The context of this paper is an interview study that analyzes public procurement of school buildings that illustrate the role of incumbent actions in the adoption of wood materials. We study the actions of incumbent organizations and identify how these actions relate to the preventive innovation’s elements of probability, severity, and time-lapse to see benefits. Findings indicate that the probability and severity of an unwanted event make incumbents more likely to select wood materials and future-oriented benefits are not a deterrent for adoption but instead, are often utilized to argue potentially larger investments. This study provides an overview of prevention-related benefits derived from building materials and highlights what construction sector incumbents ponder when adopting innovations.

This study evaluates three different types of data envelopment analysis (DEA) models by applying them to measure China’s energy efficiency. The efficacy of DEA in efficiency measurement is the primary reason why DEA has gained significant attentions from researchers across the world. The primary benefits of DEA include its ability to provide both
efficiency scores and improvement targets for decision making units (DMUs) under measurement. The improvement targets suggest several ways to improve inefficient DMUs’ efficiency. An improvement target that is close to the DMU under measurement is considered to be easy-to-achieve in DEA. However, in previous studies, most conventional DEA models used for China’s efficiency measurement provided a far improvement targets that cannot be achieved immediately and would require several years. Thus, a least-distance DEA model that can provide a closer improvement target is used in this study. Furthermore, a conventional DEA model and a ratio type DEA model are used to study and compare the performances. All three DEA models are applied to the measurement of China’s energy efficiency in 1997, 2002, 2007, and 2012. The differences in the efficiency scores and improvement targets provided by the three models have been reviewed in this paper. Although the results show different improvement targets, it can be inferred that reducing the overall energy consumption and increasing the GDP are still two effective measures for inefficient provinces, districts, and cities according to the experimental results.

In order to remain competitive in a rapidly evolving, digital and connected world, production companies are faced with the task of delivering high quality individual products in shorter production times and fewer costs. In this sense, the major obstacle in front of the companies is the occurring problems in production. Shop floor management (SFM) is a central concept of the lean methodology, which aims to empower employees with the responsibility for the effective and long-term solution of problems and supports this point of view with daily meetings. However, due to the increasing diversity of variants in production, process activities and requirements have become more complex for employees. In this framework, the problem-solving methods have become a cause of extra stress for the employees and, as a consequence, the implementation of failure management systems is not feasible. In this context, it is aimed in this paper to present a digital SFM that can use machine learning algorithms to reduce the workload on workers, to make problem-solving methods efficient, to achieve the production of quality products in a short time while reducing production costs, and to implement a smart failure management system in companies.

Gelatin’s supply chain encompasses multiple actors before arriving at the end destination. However, costs related to distribution and carbon emissions along the supply chain could be high and motivates the exploration of efficient management solutions. This paper presents a mixed-integer linear programming model for a halal gelatin supply chain and multi-echelon problem. The echelons consist of multiple marine fisheries, multi-aquaculture, fish, and aquaculture processing plants, a halal gelatin plant, and customers. The purpose of this research is to maximize the total profit. The model considers carbon emissions resulting from production and transportation processes. A study case in East Java, Indonesia, is demonstrated to verify the proposed model. The results showed that the total profit and carbon emission produced were IDR 896,395,593,934 and 64,015.34 kg.CO₂-eq, respectively.

This paper discusses how continuous improvement activities can be supported by augmenting the operators in production. After a brief literature background, real life case examples from manufacturing companies are provided and discussed. Enabling technologies, specifically AR and embedded sensors, can guide the operators in execution of their tasks, quality verification of work done step by step, and data collection from both manual and automated operations in much higher levels of details. Collected data provides an empirical foundation for data-driven analysis and improvement potentials in production and quality operations. The paper contributes to theory and practice by providing research-based innovation experiences on this emerging topic of interest for manufacturing companies.

Quality management practices (QMP) is critical for the food industry, and many quality programs were implemented to comply with the food regulations and create a competitive advantage. In order to compete in a rapidly changing market, embarking on the digital transformation (DT) journey has become critical for the food businesses. Thus, this study investigates the influence of QMP towards DT readiness in the food industry. An explanatory questionnaire from 129 quality managers and executives in the food industry were analysed using Smart-PLS. DT readiness in the food industry identified at intermediate level, with large companies having a higher DT readiness level compared to SME companies. The findings show QMP has a strong potential for supporting digitalisation initiative in the food industry. This study able to provide new perspectives to help businesses plan and carry out their digital transformations, a topic that is frequently discussed but not as frequently empirically investigated.

Sampling plans are expected to adapt to manufacturing dynamics as well as to real-time changes in a metrology workshop. To address this problem, we model sampling plans to reflect the three typical transition states of sampling contexts – normal, high risk and capacity crisis. To allow the inference of a capacity crisis state, a novel indicator called the Metrology Situation Indicator (MSI) is proposed. Skip or sampling rates can be modified across all sampling contexts in a metrology workshop using the MSI via crisis factors. This allows the real-time adaptation of metrology WIP (Work In Progress) to metrology capacity. The design and industrial deployment of this indicator is discussed. Results show that using the MSI improves the metrology Overall Equipment Efficiency (OEE) by up to 5% without any significant impact on the overall Fab cycle time as the waiting time is incorporated in the MSI.

In manufacturing companies, quality costs can be quantified and reduced. Nevertheless, there are obstacles to their determination, restricting the ability of organisations to establish quality cost reduction programmes to become more competitive. The paper's objective is to propose a simulation approach to determine the quality cost of 100% inspection strategy. The proposed approach is developed in SIMIO and presents advantages over analytical solutions, such as the lower modelling effort and broader statistics than the expected quality cost. Results suggest that the graphic animation of the simulation model developed allows assessing the coherence of the model and adjusted to reality, thus contributing to greater acceptance of the model and consequent involvement of managers, contributing to a better estimation of quality costs.

Evaluation is often connected to the use of quantitative data. However, in some contexts, quantitative data is not entirely comprehensive. Municipalities offer government services to citizens but have difficulty evaluating the value citizens experience from the services. Therefore, this paper presents the results of a project involving Mid-Sweden University and the Sundsvall municipality in central Sweden by presenting a model for citizen value collection. The value collection targets municipal workers (service providers) and citizens (service receivers). The model is constructed using design science and builds upon the recursive quality management methodology of Plan-Do-Study-Act. The results provide a valuable model for practice (municipalities), allowing citizen and worker values to be collected from municipal services and comparing them to evaluate whether any value gaps must be addressed.

The railway sector in South Africa has a history of slow technology adoption, which has resulted in a lack of support from technology developers and obsolescence. As a strategy to stay current with technological trends in asset management, South Africa's railway company implemented a web-based asset management system in 2013 and a major upgrade in 2016. Despite the benefits associated with the organization's web-based asset management, the innovation has not received the expected usage and adoption. The primary goal of this study was to evaluate the effects of individual characteristics on the use of web-based asset management technology in the South African railway sector. The study targeted a population of 105 engineering practitioners in the South African railway sector who were using the web-based asset management system. Men were found to be the most frequent users of web-based asset management technology. Overall, the findings show that this technology is not widely used in the South African railway sector. Future research should attempt to investigate the underlying factors affecting the adoption of web-based asset management technology.

With the rise of digitization in industrial applications and business processes, awareness for the value of data has increased in recent years. At the core of a digitized development are master data, which represent the metadata of information objects relevant to business processes. Reliable master data enable dynamic business processes and well-informed decision making along the value chain, starting at the ideation and continuing through engineering and production to service and disposal. Companies face challenges when maintaining increasingly complex master data, with manual data management leading to unreliable data sets while binding considerable and highly skilled resources. Artificial Intelligence (AI) encompasses a variety of tools, among which natural language processing (NLP) and machine learning (ML) stand out as having strong potential to assist in master data management. In this paper, we present an approach of how AI can assist companies in automating master data management to facilitate large scale data maintenance, which enables fast and reliable digital business process and increases the competitiveness of companies.

Decisions in development management shape the prospects of companies. However, while interdependencies and therefore the complexity of decisions is strongly increasing, decisions remain mostly experience-based. Humans are good at finding patterns, occasionally even if they are invalid. This poses a danger to companies, as it emphasizes how our intuition can be flawed. A way of addressing this challenge is the usage of the increasing amount of available data in development projects. This paper presents an exploratory approach to use project metadata to create transparency regarding decision-relevant impact factors and make these insights available to decision makers in the form of heuristics, supplementing existing intuition with databased intuition. The result of applying the approach are company-specific heuristics to enhance decision-making in development management.

Strengthening basic research development is one of the important strategies for various countries. Therefore, it is of great theoretical and practical significance to explore the influence path of the innovative behavior of basic research project members. Compared with ordinary innovation, basic innovation has a longer period and stronger uncertainty, so it has some special requirements for the characteristics of members engaged in such projects. Goal orientation is one of the important aspects of individual characteristics. The impact of different goal orientations on the innovation behavior and innovation performance of basic research project members needs to be examined. Based on goal orientation theory, creative self-efficacy theory and individual creative action theory, this paper studies the impact path of three goal orientations on basic research project members' innovative behavior through a questionnaire survey of 225 basic research project members. The results show that the learning goal orientation and prove goal orientation have significant positive impacts on innovation behavior, and creative self-efficacy has mediating effects between the learning/prove goal orientations and innovation behavior.

Dynamic market environments, increasing global competitions as well as requirements from more individual and specific customer wishes promote that manufacturing companies have to focus on continuous innovation in order to build up competitive advantages and to address customer needs. Traditional innovation approaches cannot address these challenges sufficiently. However, the concept of continuous innovation focuses on the ability of a company to continuously improve or renew products or product features during the product usage phase. Currently, manufacturing companies do not have a systematic procedure for implementing continuous innovations. At this point, the publication presents a concept to design an implementation process for continuous innovations.

Industry 4.0 brought a new revolution in industries by making them fully automated via innovative technologies, without considering human-power. Industry 4.0 aims to establish “smart manufacturing industry” by emphasizing on Information Technology (IT), Internet of Things (IOT), Cyber Physical System (CPS), Industrial Internet of Things (IIOT), Artificial Intelligence (AI), Big Data, and Robotics. This highly automated industry neglected human’s intellectual and cognitive skills, causing an increase in unemployment rate and devastation of ecosystem. In this paper, we proposed a framework of emerging technologies of Industry 5.0. Here, we examined how Industry 5.0 will further extend the development of Industry 4.0 and how humans can contribute to its manufacturing process. In addition, prestigious and significant skills for workforce in manufacturing industry are also explored. We also investigated how the Covid-19 epidemic was associated to Industry 5.0 and the idea of sustainable development goals (SDGs). Finally, we highlighted some of the challenges facing the industrial sector as research direction of Industry 5.0.

With the growing demand for customization, many industries are changing from inventory-oriented mass production to make-to-order strategies to meet multi-variety and small-batch demands. Manufacturers build intelligent flexible flow shops using automated production equipment and automated guided vehicles (AGVs) to organize production and material delivery. To meet the fast takt of the flow shop with multiple workstations for the production of multi-variety orders, it is critical for manufacturers to coordinate production and material delivery with limited AGV resources. This paper studies the integrated scheduling of production and material delivery problem motivated by our collaboration with an air conditioning compressor manufacturer by considering the features of the production of various types of products and heterogeneous material requirements by different products and workstations. A mixed-integer linear programming model is proposed to minimize the total production delay in the planning horizon. Numerical experiments are performed to demonstrate the effectiveness of our proposed integrated scheduling method by comparing with two separate scheduling methods.

Machine learning plays a vital role in the manufacturing supply chain because it is key to solving sustainability problems and managing the massive quantity of data produced by various industrial activities. Therefore, the current study's objective is to provide a systematic and bibliometrically based overview of how machine learning (ML) methods are applied to the sustainability of the manufacturing supply chain. In the current study, the authors employ a bibliometric review methodology that focuses on the statistical analysis of published scientific documents with a neutral objective of the current status and potential for future research in machine learning applications in the sustainable manufacturing supply chain. The present study demonstrates how the industrial sector might resolve supply chain challenges using ML approaches. A framework for ML-Supply chains is suggested in light of the results. Researchers, decision-makers, and practitioners will find the framework helpful in guiding the effective management of industrial supply chain practices. The body of research that is currently accessible does not offer a thorough and bibliometric analysis of the prospects for ML approaches in industrial supply chains with a framework. The bibliometric examination of machine learning applications in the industrial supply chain is covered in this paper, which further enhances its novelty.

To remain competitive in a highly internationalised market, clothing retailers continuously face challenges related to sales output. With the introduction of online retailers, especially during the Covid period, consumers are able to shop from the comfort of their homes. This means that retailers are required to fulfil orders within a very short space of time, which has cost, quality and delivery implications. This research study focuses on the application of business process reengineering at a manufacturing facility, to determine the current challenges. The objective of the research is to investigate the causes of defects and delays from pre-production and online production, and to make recommendations for improving customer delivery. Data were gathered using a qualitative approach through a case study methodology, to gain insight into the fundamental causes of inefficiency in this sector.

The advances in additive manufacturing (AM) help the recycling, redesign, and reuse of waste plastics, enabling circular economy supply chains and business ecosystems. Distributed Recycling through Additive Manufacturing (DRAM) is a technique for recycling waste plastics using mechanical processes for AM. Even though these integrated concepts enable a new path to recycle waste plastics, there are numerous barriers to their implementation, especially in developing economies. The ten barriers to implementing DRAM for waste plastic recycling were finalized through the use of a literature review and the Delphi technique. To determine the contextual relationship between the barriers, the Interpretative Structural Modeling (ISM) technique was used. A case study was carried out to investigate the barriers to implementing AM in plastic waste management in a developing economy. The interrelationship among barriers was investigated and barrier prioritization was performed. The findings from the case study reveal that the lack of flexibility to implement circular economy (CE) goals is one of the main barriers to implementing AM in plastic waste management. Findings from this study provide insights for industry practitioners and policymakers in developing a strategy to implement AM in plastic waste management.

In the ever-growing field of technological advancements the organizations are moving towards more advanced methods of working. Industry 4.0 and its allied tools are the tools which are growing with rapid pace. While the adoption of I4.0 are greatly embraced by Multinational enterprises, the SME sector are still into infantry stages for its adoption. With the presence of a lot of espousal challenges in front this study is aimed for enumerating those key roadblocks and ranking them with the application of AHP approach. The study highlights the developing economy perspective for Indian subcontinent. The study has highlighted that the technological challenges are the most important barriers which is followed by the organizational barriers. The most important barrier is lack of ICT facilities which hinders the implications of I4.0 across the Indian SMEs.

Research results from the Program for International Student Assessment (PISA) in 2019 showed that Indonesian high school students were ranked 62nd out of 70 member countries of the Organization for Economic Co-operation and Development (OECD). One of the problems is that financial literacy education is not optimal. Effective financial literacy education must involve students doing practical work so that financial literacy knowledge and experience will become part of the habits of high school students. Therefore, a board game was chosen as a supporting medium to complement traditional learning. This study uses the Delphi Method to determine the appropriate financial-themed board game as a medium to support financial literacy education for high school students in Indonesia based on expert judgment. This research identifies the board game with the highest relevance score in the financial literacy domain.

This study aims to investigate the mental health of female workers in the SME sector in Malaysia based on DASS such as depression, anxiety, and stress. To collect data, this study employs a survey research design and the DASS inventory as a research instrument. The study sample consisted of 500 female workers from SMEs throughout Malaysia. The study's findings indicate that the mental health of female workers in Malaysia's SME sector is generally normal. Concurrently, socio-demographics are a major contributor to mental health problems. In conclusion, various parties, particularly employers, must take the best approach in dealing with female workers.

Augmented Reality (AR) experiences are more and more used in engineering education. 3D-based content is digitally connected to the real world. Powerful mobile devices and easy-to-use AR software give a good opportunity to implement virtual learning content into face-to-face teaching. This paper describes the application of thirteen AR experiences in the lecture machine elements throughout a complete semester. Instead of using AR just as a singular event this approach analyses the effect of continuous use. A accompanying evaluation determines the effect size for each AR experience. Both the subjective impression of learning success and motivation, as well as an objective monitoring of learning success were made.

The manufacturing skills gap, demographic change, and advancing digital transformation are imposing major challenges on production systems and their workforce. These challenges require increased systematic up- and re-skilling of manufacturing employees. Traditional, off-the-job trainings may be insufficient to address changing learning needs – often requiring people to intermit their work, and struggling with low engagement, effectiveness, and scalability. This gives rise to technology-mediated learning concepts, such as microlearning, which promise to bridge the gap between lifelong learning demands and operational limitations on the shop floor. However, empirical studies on the effects of industrial microlearning remain rare. This paper addresses this gap by a) investigating a systematic, human-centric approach to conceptualizing, implementing, and evaluating microlearning, and b) assessing feasibility, acceptance, and effectiveness of on-the-job microlearning in a mixed methods study, combining workshops, interviews, questionnaires, observations, and an experimental pilot study. The study conducted with 10 technicians confirms the feasibility, acceptance, and effectiveness of microlearning for lean methods in a low-volume, high-complexity electronics plant compared to classroom training. This paper indicates a high potential for industrial microlearning as an avenue for future research.

Engineering educators have acknowledged the importance and need for career training and development programs as a one way to be relevant in this current education transformation and growth. Various numbers of career development programs have been successfully planned and introduced. However, most educators face limitations in choosing the right program that meets their career requirements and preferences. Therefore, this paper is aimed to discuss the preference profiling proposal for career development service design. This paper has discussed the proposed methodology to determine the educator’s preferences to develop a career development program’s preference model based on the educator’s profile. Besides, this paper has presented the preliminary results on the aspects that educators will consider when choosing these programs. A total of 28 experts have participated in this preliminary survey and determined the most aspects of choosing career development. This research proposal is helpful as a reference for designing the career development programs.

Recently, almost all the quality inspection work in the manufacturing industry has become automated. However, there are many products for which inspection cannot be automated. For example, because the tip of a dental treatment rotating tool (Diamond bar) is attached to diamond particles, all parts are slightly different. However, the judgments of the inspection are different by the operator. In previous studies, we have developed and improved operator training tools and created machine learning models to improve inspection accuracy by operator judgment and machine learning image judgment. As a result, it was found that there were images that operator judged differently after several training sessions. In addition, the previous tools did not provide a sufficient sample size of the data, so it was not possible to judge whether the students were proficient or not. This study investigated the improvement and proficiency of an inspection training tool with many sample images to reclassify samples that are judged differently by operators. We analyzed the results and extracted images that were prone to errors in judgment. We conducted experiments to see if reflecting the results in the machine learning model effectively improved the model's accuracy. The results showed that, in the case of the workers, no correlation was observed between the correct response rate and the time to answer in training, but the machine learning model using the training results showed an improvement in the correct response rate.

The construction of "double first-class" and "four New" has put forward new requirements for undergraduate talent cultivation in colleges and universities. As a critical link affecting the quality of undergraduate talent cultivation, the undergraduate graduation project (thesis) needs to improve the management quality and strict evaluation standards. It can achieve by constructing the management quality evaluation model of the undergraduate graduation project (thesis). Specifically, the weights of each index in the management quality evaluation model are determined based on AHP and entropy method. Then the gray fuzzy comprehensive evaluation method is used to evaluate the management quality of  the undergraduate graduation project (thesis). The case study of Northwestern Polytechnical University was carried out, and finally, the innovation experience and management evaluation suggestions were put forward.

The main purpose of this paper is to identify relationship between typology of human resource analytics (HR analytics) used in different types of strategic firms (cost leadership and differentiator) with or without use of enterprise social media (ESM), during talent management (TM) in the organization. Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While, studies suggest that business analytics is positively related to organizational strategy. Contingency theory holds that HR management methods are selected in accordance with the types of business strategy. This paper shows alignment of business strategy to HR strategy to HR analytics for organization’s long term success and performance. More specifically, we present a conceptual framework, which illustrates how cost leadership (CL) and differentiator (DIFF) strategy firms differ regarding their use of different HR analytics types (descriptive, diagnostic, predictive, prescriptive) with or without use of Enterprise social media (ESM). Chi-square analysis was used to test the interdependence.

Customer relationship management is a process in which enterprises or other organizations manage their interactions with customers, normally using data analysis to research enormous amounts of data. Successful customer relationship management needs organizations to interact flexibly with their customers. Customer value is the basis of customer relationship management. Organizations can improve customer relationship management by obtaining helpful information from large quantities of data using big data analytical technologies to help understand customer value and interact with customers using suitable marketing strategies. Accordingly, this work develops a model for constructing decision support systems, based on customer value analysis of patient registration data using fuzzy c-means and decision tree methods. The results provide a valuable reference for managers in the healthcare industry.

This pilot study investigates the impact of emotion display and shot scales on gender representation in cross-cultural TV series adaptations using a deep learning approach. We seek recurring patterns of shot scales in conjunction with gender and characters' display of emotions by comparing eight episodes of the American TV series Law & Order Criminal Intent and their equivalent French adaptation, Paris Enquêtes Criminelles, amounting to 44,602 frames. To conduct our research, we introduce our AI toolkit, The Möbius Trip, a multimodal analysis engine based on machine learning techniques. The Möbius Trip accurately labels, classifies, and measures gender, emotions, and shot scales. We propose a new shot scale model based on strict conventions that provides a steady rationale for big data film analysis. Our results reveal a stark underrepresentation of women and a notable difference in the display of emotions between French and American characters. Correlating shot scale distribution and characters' display of emotions, we find that intense emotions are associated with closer shots. Based on these elements, we contend that the American and French versions follow different patterns of cultural representation.

Machine learning (ML) algorithms are techniques that allow computers to learn from the data without being explicitly programmed. ML techniques consist of hyperparameters that typically influence prediction accuracy, hence requiring tuning. In this study, we systematically evaluate the performance of the genetic algorithm (GA) technique in tuning ML hyperparameters compared to three other common tuning techniques i.e. grid search (GS), random search (RS), and bayesian optimization (BO). While previous studies explored the potential of metaheuristics techniques such as GA in tuning ML models, a systematic comparison with other commonly mentioned techniques is currently lacking. Results indicate that GA slightly outperformed other methods in terms of optimality due to its ability to pick any continuous value within the range. However, apart from GS which took the longest, it was observed that GA is quite a time inefficient compared to RS and BO which were able to find a solution close to the GA within a shorter time (GA – 149 minutes, RS – 88 minutes, BO – 105 minutes, GS – 756 minutes).

This paper explores the data collection and mining of association rules to generate over 8,500 association rules which improve an existing recommendation engine used in an eCommerce platform between traditional trade wholesalers and traditional trade retailers. This improved recommendation engine allows traditional trade retailers to receive personalized recommendations based on items in their cart, and improves the current recommendation engine which only recommends most sold products. The improved recommendation engine helps traditional trade retailers purchase the right products for their stores and allows traditional trade wholesalers to increase the revenue of their stores, thereby providing both traditional trade wholesalers and traditional trade retailers with tools to help compete against modern trade outlets.

Serious game has been introduced as an interactive tool to support teaching and learning and to increase awareness on a certain issue. Although various research has identified the benefits of using serious game for teaching and learning, there are very limited research on the benefits or the impacts of serious game for enhancing awareness. This paper focuses on evaluating the benefits of using serious game for increasing awareness about a particular diploma in a polytechnic in Singapore. The serious game was designed and developed to capture the players understanding about the diploma through series of questions that the players would need to answer in the game environment. To evaluate the benefits of the serious game in an open house, data analytics techniques were used to evaluate the players’ sentiment and perception.  Two different sentiment analytics techniques were used and compared. Based on the results, we found that most of the players had positive sentiment on the game. It is aligned with their good understanding on the diploma. These two can indicate that the game was well received by the players.

Drone delivery can significantly solve issues faced in last-mile delivery operations. However, drone routing is known for its complexity due to its numerous operational characteristics, such as multi-trip operations, recharge planning, and energy consumption estimation. Thus, there is a need to create a sustainable ecosystem that can provide an optimal drone deployment plan. This study focuses on overcoming a delivery problem that involves multi-trip drone routing, energy optimization, and travel time optimization problems. Therefore, we propose a Mixed Integer Non-Linear Programming Model as an integrated optimization model. The objectives of this model are- to fulfil maximum demand without leaving any orphaned drones, to ensure the optimal consumption of energy by drones, and to minimize the number of drones required to create the optimal plan. The proposed model is solved using the exact method with the Gurobi solver. Results obtained indicates the satisfactory performance of the model. This model can help industries better manage their daily last-mile delivery operations using drones and their potential for growth in the future.

Portfolio selection maximizes investment returns with acceptable risk. Mean-variance (MV) and Safety-first (SF) are two methods to achieve this goal. MV explains that an investor will choose an investment with a high return over another if it has the same risk. In contrast, SF focuses on minimizing the investment loss by establishing a loss threshold for the portfolio. This study presents a framework for selecting the portfolio that could outperform the benchmark using MV and SF methods and utilizing the historical returns of Forbes Asia's Fab50 companies. Back-test shows that portfolios have the potential to earn twice as much as the benchmark using MV, but these have high standard deviation or risk. Compared to MV models, SF models are observed with lower risk. Both MV and SF models were found to have exceeded the p-value criterion, indicating that these were unable to outperform the benchmark. Nevertheless, this study found an acceptable portfolio with a marginal p-value but high investment return using one of the MV models. This study serves as a reference for Operation Research application in Finance.

Multiple criteria decision making and clustering are topics that have been developed separately for decades. More recently researchers have investigated how to apply clustering techniques to multiple criteria decision aid. As in unsupervised classification, the goal is to obtain homogenous clusters that are well-separated. The distinctive feature comes from the fact that objects are compared based on preference relations (which are most of the time not symmetric unlike a traditional distance measure). In this contribution, we address the opposite problem. We want to find a partition of objects evaluated on multiple criteria such that groups obtained exhibit a high intra-group heterogeneity and good inter-group homogeneity. We present an extension of the PROMETHEE method to address this issue. This is illustrated on the problem of the creation of groups of students where fairness between the obtained clusters must be ensured.

In recent years, robots have been introduced to production sites due to the shortage of manpower. As a result, there is a need to reduce the costs of robot systems. Conventional research has focused on the optimization of robot configuration and motion planning, In this study, we propose a decision support system for the selection of equipment for robots, hands, and workpieces in a transfer system. Given information on the length and payload of each element, the problem of selecting equipment that minimizes the sum of the costs of these three elements is formulated as an integer programming problem. The system considers various conditions necessary for equipment selection, such as physical constraints and reachability of the robot end-effector to the workpiece, and expresses the grasp availability of the workpiece, payload, and reachability of the robot as inequality constraints. Using the proposed system, the optimal solution was obtained in the computational experiments.

Supply Chain 4.0 (SC4.0) is an enhanced account of the supply chain that consists of artificial intelligence, cloud, and data analysis, whereas Supply Chain 5.0 (SC5.0) is a visionary aspect of the supply chain to succeed SC4.0 by customizing consumer requirements by combining machine proficiency and human efforts. Irrespective of abundant growth in SC4.0 technologies, SC5.0 weighs on human interface with technological advancements for the betterment of supply chain activities, which in turn benefits society and reflects the importance of the concept of this research. The literature review and methodology provide further understanding of the subject by explaining software use on the surveys to accumulate responses based on automation, demand, societal requirements, and so on. Barriers to this transformation result in a clarification of the challenges that the world will face for the successful transformation from SC4.0 to SC5.0. Overall, this study focuses on providing various factors on the transition of SC4.0 to SC5.0 that could combine human brain and technology for improved results.

This paper presents a comparative analysis of Mean-Variance Theory (MVT) and Safety – First model with SP/A criterion utilizing Non-Fungible Tokens and Collectibles in the crypto market. The criterion used to determine the investment pool is the mean volume price of the Top 100 NFTs and collectibles. Historical data are gathered and computed for return estimation with equal probability. Also, different portfolio weight threshold parameters were used for the mean-variance (risk-return threshold) and safety first (relative value for fear and hope). Using backtesting, safety-first portfolios show higher cumulative returns from the US dollar benchmark. Overall, this study offers portfolio optimization and an alternative portfolio selection model as references for generic investment procedures for digital asset investors, and for educational purposes.

We present a new flow-based formulation for identical parallel machine scheduling with a regular objective function and without idle time. The formulation is constructed with the help of a decision diagram that represents all job sequences that respect specific ordering rules. These rules rely on a partition of the planning horizon into, generally non-uniform, periods and do not exclude all optimal solutions, but they constrain solutions to adhere to a canonical form. The new formulation has numerous variables and constraints, and hence we apply a Dantzig-Wolfe decomposition in order to compute the linear programming relaxation in reasonable time. The resulting lower bound is stronger than the bound from the classical time-indexed formulation. We develop a branch-and-price framework that solves several instances from the literature for the first time. We compare our procedure with the state-of-the-art methods.

Statistical Process Control (SPC) and Automatic Process Control (APC) are based on different strategies for process quality improvement and process adjustment. SPC is often used to monitor long-term process quality and removing causes of process disturbance by the implementation of appropriate control charts. APC is used to compensate for short-term variations due to the common causes and attempts to continuously adjust the process level close to the target or set points. Different techniques are proposed in the literature to integrate SPC and APC, based on different criteria. Many researchers have studied economic cost models to develop the economic design of SPC only. Models for the economic design of the integration of SPC and APC have been limited but it has gained significant interest in recent years. In this paper we will highlight the available methods that propose the integration of these two techniques. Then the economic design methods available in the literature will be summarized, stating the pros and cons will be stated. Based on this study, we propose some extensions regarding economic cost models, quality cost, and related topics.

Burn Centers are emergency departments that play a critical role in patient care. The goal of this research is to develop a model of the burn center to identify bottlenecks and evaluate performance improvement approaches. We have used the discrete event simulation (DES) approach to model a regional burn center, and its current processes based on empirical data. Sensitivity analysis is carried out to study the effect of perturbing control parameters such as the number of beds (B) and a number of arrivals (A) at the burn center on key performance metrics which include utilization, availability of beds, average waiting time for a bed, and average time spent at the burn center. By reducing the number of beds by 2 and unnecessary arrivals (referrals) at the burn center by 10%, there will be a significant improvement in performance measures and revenue of the burn center. The 10% reduction in arrivals is achieved after proper diagnosis. This leads to a 33.33 % savings in the burn center revenue.

With the prevalence of COVID-19 infection, the use of mathematical models for infectious diseases has attracted considerable attention. In a previous study, human behavioral strategies are represented using evolutionary game theory and integrated with the SIR model of the COVID-19 epidemic. However, actual COVID-19 infection has an incubation period. In addition, due to viral mutations, the number of infected people is higher in the second and subsequent epidemics than in the first one. In this study, the previous study that uses evolutionary game theory to represent human behavioral selection in the SIR model is extended to the SEIR model. Then, considering the viral mutations, the relationship between the number of infected people and the risk of infection is formulated. The simulation results indicate that, by increasing the infection rate as the infection spread, the maximum number of infected people at each infection peak continued to increase until the maximum number of simultaneously infected people is reached. This finding indicates that the number of infected people is affected by the higher infection rate caused by the virus mutation.

An Emission-based Production and Inventory Control System (EPICS) is proposed, as an extension of the classical Automatic Pipeline, Inventory and Order Based Production Control System (APIOBPCS) model. EPICS assumes an emission permit, along with customer demand (as in classical case) as exogenous variable inputs, creating a Multiple Input Single Output (MISO) system. Adjustment to ordering (replenishment) also involves weightage to system emission-level as feedback. Stability Analysis is performed on EPICS model using Bounded Input Bounded Output (BIBO) stability, and the system is solved for specific scenarios. Simulations are performed to understand dynamics. Higher weightage to WIP-adjustment helps in emission reduction significantly. Initial analysis suggests that operational adjustments are not sufficiently enough to reduce the system emissions.

Owning a long coast with sea wind, Vietnam is paying attention to the development of offshore wind turbines, for using more clean and sustainable energy. Mooring systems are used to keep floating wind turbines stable, and so as to ensure the safety and economic efficiency of wind power production. The appropriate selection of mooring types and mooring lines material helps reduce the risks due to motions of wind turbines. In this study, the authors focus on the analysis and comparisons of different mooring models (taut, semi-taut, catenary), and mooring lines materials such as chains, polyester, and nylon, to make the motion risk minimized choices for a floating wind turbine. Then, the authors evaluate the effectiveness of the selected solutions above by calculating the potential motion risk of a semi-submersible floating wind turbine with a capacity of 2MW in Vietnam.

This paper analyses energy losses in complex electrical grid systems in Australia. It discusses the issue emerges from the lack of leverages between end-users and suppliers of electricity. The fundamental of this research is quantifying the energy losses at the individual levels of houses based on the perception of peak, off-peak and average demands. The computational simulation in this probabilistic study incorporate nondeterministic methods of PERT and Monte Carlo to unpack the correlation among the individual residential houses demands of electricity. Using data of electricity demand for houses, the results are compared against the perception of energy losses occurred at individual levels of residential houses. Iterative annual demands were covered in the probabilistic simulation process of energy losses.

Environmental problems have sparked intense discussions in both academics and practice. This paper constructs a two-stage green supply chain (GSC) composed of a socially responsible manufacturer and a retailer. The manufacturer carries out green technology innovation but faces the uncertainty of results. If green technology R&D is successful, the government will provide carbon emission reduction cost subsidy for manufacturer or green consumption subsidy for consumer. This study compares the impact of different subsidies on GSC decision-making operation. Then, we get the following conclusions: (1) the optimal retail price, wholesale price and green marketing level of the products under the green consumption subsidy are higher than those under the former subsidy. (2) For the green technology R&D effort level, when the coefficient of two subsidies meets certain conditions, the R&D effort level is more optimal under green consumption subsidy. (3) No matter whether R&D is successful, only when green consumption subsidy coefficient reaches a high level, the profit of the manufacturer under the green consumption subsidy will be higher than that under the former subsidy.

The health sector contributes to a significant amount of non-hazardous and hazardous waste generation. The revitalization of healthcare supply chains is vital to providing healthcare-related supplies to satisfy the current demands without compromising on the ability to fulfil the needs of future generations. The circular economy (CE) initiative enables eliminating waste in supply chains by using eco-friendly materials, optimizing resource yields, and circulating products at their highest value. The adaptation of CE goals with a suitable supply chain strategy in the healthcare sector is challenging. This study reviews the barriers within healthcare supply chains towards fulfilling CE goals. These barriers were identified by means of a literature review and a qualitative approach involving semi-structured interviews. The categories of the barriers were then analyzed using the analytic hierarchy process (AHP) method. The AHP approach enables the identification of the barriers in a descending order of importance as financial, technological, management, policy, social, and cultural barriers. The findings of this study can provide valuable guidance for decision-makers in healthcare supply chains to focus on the barriers in each category and accordingly formulate strategies for CE adoption.

The semiconductors manufacturing involves complex supply chains. Because of highly dynamic market demand and limited available production capacity, the operations plan of semiconductors industry must well consider the capacity collaboration between multiple production plants in terms of front-end wafer fabrication and back-end packaging and testing. Based on the Advanced Planning Systems (APS) framework, this study develops a two-phase decision method to support the capacity planning in the semiconductors manufacturing supply chain. In it, we first use the method of linear programming to get a baseline solution, and then use computer simulation to approximate a detailed solution for the production capacity planning of supply chain. The solution method is validated by a global semiconductors manufacturing company and shows satisfactory results from the viewpoint of capacity utilization.

This paper mainly intends to discuss the significant barriers faced by current industrial systems in transitional to Supply Chain 5.0 from Supply Chain 4.0. A systemic literature review methodology was conducted for highlighting top ten barriers which would be the most significantly challenging for an existing Supply Chain 4.0 industry to embrace but are also critical to transition to a Supply Chain 5.0. Barriers to Supply Chain 5.0 transition are only paradigms of the Industry 5.0. The concept of a Supply Chain 5.0 is still in the budding stages of an idea and requires monumental amount of research, innovation, technology and industrial strategy along with improvements in terms of socio-economic and sustainable impacts. Nevertheless, this paper gives valuable insights to researchers and industrialists alike by exploring ideas of Industry 5.0 focusing on supply chain perspectives required to support it to develop into the implementation phase from its preceding version.

This study investigates the effect of electric trucks and drones on single-manufacturer, multi-retailer, and multi-product. This study aims to propose a mathematical model that minimizes the total cost of the supply chain. The emissions come from the production process, and inventory carried out by the manufacturer and retailers. The lead times depend on production, transportation, courier, charging, loading-unloading, and in-transit activities. The decision variables of this study are the customer’s lot size, number of packages, number of batches, and safety factors. A numerical example is given to illustrate the benefit of the model.

The importance of supply chain resilience or, the ability of a network of entities to proactively plan and operate in the wake of disruptive events to maintain expected performance or, emerge by gaining competitive advantage, became clearly evident in recent years due to the numerous geopolitical uncertainties, natural disasters, and more recently, the COVID-19 pandemic. At the same time, market and regulatory forces are increasingly drawing emphasis to the need for designing and managing supply chains more sustainably, mitigating negative environmental impacts and increasing benefits to all stakeholders. While next generation supply chains must be both resilient and sustainable, strategies to enhance one aspect could sometimes conflict with those chosen to optimize the other. This presentation will first separately examine key requirements for enhancing resilience and sustainability in supply chains for transitioning to a circular economy and closed-loop material flow. Secondly, comparisons between metrics for evaluating both resilience and sustainability will be drawn to examine commonalities and contradictions. Directions for future research to identify design practices and operational strategies for resilient and sustainable supply chains will also be presented.

This paper studies the optimal ordering problem of a buyer who sources from an unreliable supplier under uncertain demand. The supplier is subject to a random disruption. If the supply disruption occurs during the observation period, the buyer will proactively invest a fixed setup cost and adjust the unit wholesale price to incentivize the supplier to restore production and fulfill the regular order. Otherwise, the buyer does not adopt any risk handling measurements. We derive the buyer's optimal ordering decision. It is found that the incentive strategy is not always profitable, and the conditions where the incentive strategy is useful are provided. The results of numerical experiments indicate that the value of incentive strategy is more significant when the disruption probability is moderate, or the demand variability is low.

Organizational resilience is about combination of operational and strategic resilience to sustain company’s performance. In the time of turbulence like COVID-19 pandemic era, this resilience is extremely needed for any organizations not only to bounce back from its disruption but also ensures its sustained performance. However, knowledge related to this ability is still lacking and developing to cover especially factors needed to develop resilience for organization. Thus, this paper is proposing an organizational resilience model that covers what factors known so far. For this research, 100 organizations were invited for the research using questionnaires as its instrument. Among these invited organizations, only 47 organizations have given their responses at rate of 47%. Partial Least Square Structural Equation Modeling (PLS-SEM) method using SmartPLS 3.0 was utilized for data processing. This confirms that organization should promote its resilience through operational dan strategic resilience to sustain its performance especially in times of turbulence especially during COVID-19 pandemic era.

Delivering a high service quality under the safety protocol of COVID-19 is very essential for the sustainable airport operations. The study was intended to determine the impact of COVID-19 on Airport Service Quality (ASQ), customer satisfaction, and travel intention by utilizing a structural equation modeling (SEM) approach. A total of 517 Filipinos voluntarily answered an online questionnaire that consists of 92 questions. SEM indicated that the security check, terminal facilities, and services had significant effects on perceived value which subsequently led to customer satisfaction. In addition, travel safety measures had direct effects on Filipinos’ travel intention and customer satisfaction. Interestingly, service innovations had no significant impact on customer satisfaction but directly affected travel intention. By understanding the relationship between these factors, airport management could have better decision-making while efficiently and effectively utilizing the resources in these times of uncertainty.

The occurrence of the COVID-19 pandemic has only affected the death and human health of almost all countries in the world, including Indonesia, but has also affected the business and industrial sectors. In this case, the general trading sector is one of the sectors affected and experiencing supply chain disruptions, namely general trading companies which experienced a high decline in demand and hampered supply of traded goods from suppliers abroad, especially in China. This study investigates supply chain disruptions in Indonesian general trading companies using a qualitative exploratory approach. 2 case studies on companies with heavy equipment trading are used in this study. The results indicate that almost every side of the supply chain occurs disruptions such as purchase price and delivery time in the supply side. On the operation side, the factor of supply chain disruptions such as health fees and productivity and demand orders, visits to customers,s and others are factors in the demand side.

Most construction contracts make voluntary mediation a condition precedent to arbitration or litigation because of the need to have alternative ways to resolve construction disputes. This study aims to examine if voluntary participation is necessary for its wider adoption. The principal-agent relationship between developers and contractors is subject their asymmetric conditions and opportunism due to high asset specificity and risks uncertainty. In these contexts, this study examines the notion of voluntary mediation for disputants having inherent diverse interests. Construction professionals were invited to provide one mediation case and evaluate the extent of asset specificity, uncertainty, and asymmetric conditions between the disputing parties. Furthermore, the respondents were asked to assess the level of willingness, mediating behaviors and the mediation outcome. Regression analyses were performed and it shows that higher voluntary participation will promote collaborative mediating behaviors and better settlement. Moreover, power asymmetry was found to have no discernible effect on the mediation. Another interesting finding is mediation may only be effective for disputes of lower quantum. The study therefore sheds light on the versatility of voluntary construction dispute mediation.  

Systemic and society-changing technological developments (Deep Tech, like battery cells or microchips) have predominantly emerged in the US and the Asian region in recent years, whereas European countries, such as Germany, have become technologically dependent in many areas. The development of Deep Tech is accompanied by high financial requirements over a long period of time. In addition, bureaucratic as well as legal hurdles are often associated with its development. To improve this situation, a fundamental adjustment of the state’s intervention as an actor within an innovation system appears necessary. Through a targeted use of support measures, the development and transfer of Deep Tech can be supported by the state, thus contributing to secure Europe’s long-term competitiveness and independence as an industrial region.The present paper presents a conceptual approach for the development of Deep Tech-related governmental technology transfer support options. It presents requirements for a model, which enables the identification of necessities and challenges of technology transfer and the development-phase-specific identification of options. Based on these findings, a concept for the development of a Deep Tech-specific identification of support options is outlined.

This study attempts to explore and interrogate a potential strategy by developing a relationship between experience marketing and customer loyalty on customer purchase intention for digital services in the banking sector. This study examines the significance of the experience marketing strategy for the Indian banking sector from the viewpoint of the customer. Here, a mixed-method study design has been chosen, a method that is becoming more popular in social science research. The exploratory analysis used in this research design is followed by quantitative analysis methods. Multiple regression analysis has been used to give the interpretive foundation for the evaluation. In the banking sector, where feel, think, and sense experiences have a significant impact on customer purchase intention, this study demonstrates how all structural experimental models are affecting customer buy intention. This study is crucial for figuring out how experience marketing affects customers' intentions to buy in the banking sector. The study's findings can help banks create more efficient marketing strategies in light of the present economic slowdown, which is affecting the Indian banking sector's sales.

Higher Education Institutions or universities are attempting to manage their visual identity in the current climate of competition among universities to achieve the best reputation. Previous studies show that visual identity is an invaluable asset that, when managed strategically, can help increase university excellence. Visibility, distinctiveness, transparency, authenticity, and consistency are the five reputation dimensions to analyze the relationship between reputation and visual identity. Using the consistency dimension framework, we observe the implementation of the five Indonesian state universities' identities on buildings, social media, promotional media, and websites. We examine how consistently these universities implement the visual identity standard guideline. Universities that manage visual identity more consistently have a better reputation, evidenced by their rankings according to THE, QS, and Webometrics.

Older adults are a growing segment in society. One of the main challenges that older adults experience relates to mobility. Recognizing this noteworthy challenge, a state-of-the-art mobility app-based solution called the TakeMe app was developed to bring transportation options and volunteers to older adults’ fingertips and thus enabling them to remain mobile outside the home environment to meet their needs. The present study aims to explore older adults’ evaluations of the mobility app-based solution. To do so, the study adopts a qualitative approach to interview older adults’ and a quantitative approach to perform and report on a content analysis of older adults’ evaluations of the mobility app-based solution, thereby leveraging on the benefit of soliciting participant voices in the former and the benefit of objectivity in analysis and reporting in the latter. Findings of the study reveal three key aspects that older adults consider when evaluating mobile apps: learning experience, value perception, and usability. The study concludes with key takeaways and their implications for theory and practice, as well as limitations and future research directions.

Warehouses see an increased need to become more and more digitalized and automated. This paper will look at a 3PL company operating warehouses worldwide with all types of customers. With the increased need for warehouse space, it becomes essential to standardize and automate warehouse operations. This paper will present several initiatives to standardize and digitalize warehouses to prepare them for automation. The four standardization initiatives that are investigated and presented for the case company are: Streamlined process flows based on offered services, automated put-away, automated setup of new clients based on predefined options with a configurator, and an automation framework to standardize the requirements for implementation of automation. Each initiative was developed and presented to different stakeholders at the warehouses. The stakeholders showed interest in the initiatives and especially the streamlined process flows, and the configurator will be implemented. It became clear that the biggest challenge will be implementing the suggested solutions on a big scale in multiple warehouses. However, this is also where the most significant benefits are as the benefits of running multiple warehouses can be archived.

Testing, Inspection and Certification (TIC) industry supports product production activities affecting nearly all aspects of our daily life. The industry ensures product compliance to a certain acceptance level and quality. Yet, the Industry is facing some problems with the advancement of technology and certification frameworks. To solve the problem, Industry 4.0 (I4.0), which has been positively supporting manufacturing industry can be solutions to problems in TIC industry. This paper studies the TIC industry and reviews on the concept of I4.0 from available marketing and academic research papers for further adoption of I4.0 to the industry. The review showed that key I4.0 enabling technologies can be greatly supporting the TIC industry with specially designed framework, which is worth for further design and investigation.

Artificial intelligence (AI) is permeating one human endeavor after another. However, there is increasing concern regarding the use of AI: potential biases it represents, as well as mis-judged AI use. This study continues the recent investigations into the biases and issues that are potentially introduced into human decision-making with AI. We experimentally set-up a decision-making classification task and observe human classifiers when they are guided in their decision-making either by AI or other humans. We find that over-reliance or authoritative stigmatization is present when AI is concerned and that with human guidance discursive explanatory decision-making is present. We conclude that while AI is seen as authoritative even in a low stake decision-making setting, it does not suppress choice, but combined with a lack of transparency, AI suppresses visibility into rationality creation by the decision maker. Based on the emergent explorative relationships between types of rationality, AI transparency and authoritativeness, we provide future research avenues based on our findings.

Electric vehicles have been considered as one of the measures to reduce emissions in Indonesia. The paper aims at understanding public acceptance toward electric vehicles in Indonesia. An empirical survey involving eighty respondents was conducted to collect data on the technical performance, the usage, the perception of electric vehicle as well as the respondents’ characteristics. The data was analysis using multinomial logistic regression (MLR) to identity the significant factors explaining the acceptance toward electric vehicles. It is found that the technical performance of electric vehicles, the use of electric vehicles, perceptions of electric vehicles, and consumer characteristics are all to be significant in public acceptance of electric vehicles. Among the factors, the technical performance appears to be the most significant, whereas the usage factor (operating and maintenance cost, supporting facilities) is least significant. The application of MLR and potential interventions to improve public acceptance of electric vehicles in Indonesia are presented. Avenues for potential future research are also discussed.

Japanese ICT companies have not been able to gain competitive advantages in the global market, although they have paid lots of effort into promoting innovation. From the point of view of ambidextrous organization, it is reported that Japanese ICT companies have problems with low exploration degree. This paper provides knowledge that can be used to improve the level of exploration by investigating the degree of exploratory technology development efforts of Japanese ICT companies in recent years and discovering common elements from organizations with high exploration degree. As a result, the relationship between some organizational structure and exploration degree, such as the density of R&D network is derived.

The emergence of Fourth Industrial Revolution (4IR) presented numerous solutions in the institutions of higher learning without connectivity challenges. The outbreak of COVID-19 in 2019 exerted more pressure on institutions of learning still reluctant to utilize technology in teaching and learning. The current study purposes to evaluate the technology integrated in the first-year level within the Mechanical Engineering department in the School of Engineering at the University of South Africa to ascertain performance encountered. The specific technology is a three-dimensional viewing tool to improve students understanding of isometric and orthographic engineering drawings. The research study targeted a previous assessment without the integration of the technology and compared with a current assessment after the intervention, for three first year drawing modules. Online technology adoption survey was utilised to measure the performance during the intervention. The findings of the research indicated that student’s performance with respect to average grade drastically improved from approximately 55% to 70%. This result demonstrates a significant benefit from the intervention for students to develop their mental visualization skills required for technical drawing.

The cooperative innovation is receiving an increasing amount of attention in the innovation development strategy, and cooperative innovation behavior is becoming the predominant form of innovation in the technology sector and an essential component of the national innovation system. Through an examination and comparison of the literature on cooperative innovation that has been published in recent years, this investigation investigates the game behaviors of collaborative players in various forms of innovation alliances. In conclusion, paper discuss the shortcomings of the previously conducted study as well as the opportunities for new research endeavors.

This study analyzes the effect of different speeds of a firm’s adopting new scientific knowledge on the firm’s performance. The previous research indicates whether being a pioneer to adopt leading-edge scientific knowledge among competitors is a double-edged sword. Using a new measure of knowledge speed – “pioneering”, based on the order-of-citing new scientific articles from patent citations, the study can directly test firms’ new knowledge sourcing behaviors. Results show that pioneering and knowledge stock has a positive interaction effect on firm performance, whereas either knowledge diversity or knowledge maturity interacting with pioneering has a negative effect on firm performance. Knowledge stock is necessary for the pioneering knowledge applied to the current R&D process. The benefit of pioneering is enlarged when a firm has specialized knowledge that enables the firm to dominate a technology domain. Since the pioneering scientific knowledge is likely to be a new generation knowledge, a firm’s profit from this knowledge depends on familiarity with leveraging emerging knowledge.

As competition between companies using patents intensifies, the need for strategic use and visualization of patents is expanding, and limitations of expert-based methodologies are being raised. Therefore, this study aims to develop an analysis methodology that identifies the competitive environment of technologies and companies in consideration of technical function information and establishes an final patent strategy. First, we extract a subject-action-object (SAO) structure from patent data and visualize a Generative Topographic Mapping (GTM) based patent map. Second, we explore the competitive environment of technologies and companies through the nodes of the GTM map to extract patent strategy patterns, and then establish a final patent strategy. In order to verify the proposed methodology, a case study was conducted for the autonomous vehicle industry. This study has the contribution of proposing a patent map including more technical information using the technical function information of patents, quantifying and visualizing patent strategies.

Metal Matrix Composites (MMCs) are demanding in various industries due to its remarkable mechanical properties. But, machining of these materials is challenging due to inherent properties of matrix material and reinforcing material. Electric Discharge Machining (EDM) is a most versatile and popular machining process for generation of intricate geometrical shapes with high surface quality on such MMCs. But low material removal rate and poor geometrical characteristics restrict its further application. Therefore, in this paper, a variant of EDM i.e. Magnetic Force-assisted Powder-mixed EDM (MFPEDM) has been proposed for machining of such difficult-to-machining MMCs. This research is devoted to machining of Aluminium Titanium Di-boride MMC on MFPEDM and experimentally investigation of the impact of controlling factors for machining characteristics such as material removal rate, surface roughness, overcut and concluded that the Current, pulse-on time, and powder concentration was most common significant controlling factors.

Due to the COVID-19 pandemic, employees are required to respond to changes and conform to stringent safety laws and regulations. This study aimed to analyze the impact of the COVID-19 pandemic on the organizational commitment among employees in the semiconductor industry. A total of 272 employees working in the semiconductor industry answered a self-administered questionnaire that considered 51 questions, distributed online. Utilizing Structural equation modeling (SEM), the results showed that the COVID-19 was found to have a significant direct effect on employees' perceived job outcomes (PO) and a negative direct effect on job demands (JD). Moreover, PO had a positive effect on job motivation (JM) and job satisfaction (JS). Subsequently, JM presented a significant positive direct effect on JS, while JD showed a negative effect on JS. Lastly, JS showed a significant positive direct effect on organizational commitment (OC). Intriguingly, an indirect effect of PO on JS was seen. This study is one of the first studies that analyzed the organization commitment among semiconductor workers during the COVID-19 pandemic. This paper could be utilized as a foundation to enhance organizational commitment, particularly in the semiconductor industry worldwide.

Six sigma is a technique of quality control that is used to improve existing products and service of companies, through detecting and removing defects, with the aim of streamlining business processes quality control in order to reduce variation across the board. Company A’s employees fail to complete six sigma green belt projects. During six sigma green belt deployment, some factors affect its success. This study is important because it identifies the factors and challenges that affect six sigma deployments and contribute towards the knowledge of six sigma deployment. A quantitative method was used. 56 participants from Company A (quality managers, operation managers, team managers, operational technical managers, metrologist, plant managers, supply quality inspectors, plant technical service managers, R and D scientist, workshop specialists, procedural and quality Generals) were selected. As a result of this study, the most important factors influencing the intentions of green belts to complete the six sigma projects are support of Company A’s management and failure of selected team.

This study investigates the influence of lean manufacturing as a strategy to eliminate waste if not minimizing it by considering the different types of waste to eliminate, the 5S Lean method, Kaizen, Just in Time, and Value-added management. The study provides a theoretical application of lean manufacturing on how it can be introduced and implemented in the business environment. A quantitative approach with the use of questionnaires has been used to gather information from 80 organizations that voluntarily took part in the study. Results have shown that many of the respondents are aware of lean and use it, nevertheless, different internal barriers such as reluctance to change, management support, and staff training make lean’s application difficult even though they all agreed that it had a positive impact on their production process. The sample size is a major limitation to the study, using a much bigger one would provide more reliable result to confirm these findings and enrich the existing literature.

Product Lifecycle Management (PLM) faces challenges for adaption to the global economy. These challenges range from strategic restructuring of product involved departments, to leveraging novel technologies like smart devices and Machine Learning. Social media on the other hand is recognized as a strong source for exploring knowledge from customers and product users. However, this domain is not explored for its potentials in relation with PLM framework. Today’s state of Artificial Intelligence, Machine Learning techniques, in addition to ever falling computational costs, provide inevitable opportunities for mining customers’ product related data. This paper aims to understand and analyze the patterns of PLM’s evolution with social media integration. It is intended to identify a possible extension for PLM architectures enrichment by leveraging the social media domain. The paper will introduce some of the most relevant PLM definitions, followed by a literature review of domain experts. Consequently, it will analyze the findings and evaluate the possibility of social networks domain integration in the current PLM. Finally, it will highlight the evaluation perspectives for the benefits of proposed solutions against current literature solutions.

The intensifying complexity in the modeling and construction of production facilities and the accumulative degree of automation has increased the importance of refining the technical process of system availability and reliability. This study investigates the impact of preventative maintenance strategy on production system reliability indicators. The study reviewed production data of 744 hours, consisting of different production stoppages. Using reliability indicators obtained through literature, the performance and availability of the system were measured. The findings revealed that an excessive number of stoppages hamper system availability and productivity.

Due to the increased complexity of supply chains and the various challenges that these supply chains are facing, it is important for supply chains to automate and optimize their supply chain management processes to respond to these challenges and maintain their competitive advantages. Order management plays an integral role in supply chain management, and one of the ways where the order management process can be streamlined is to adopt a no-touch approach. In this paper, we describe a natural language processing (NLP)-based engine prototype to extract and interpret order information from customer natural language orders, which will facilitate no-touch order processing. This engine prototype can then be integrated into an overall no-touch order management engine that can be used to demonstrate a reliable Advanced Available-to-Promise (AATP) process at the critical sites in a supply chain testbed.

In mechanical engineering, individual functional units of a machine are often assembled by one operator at single workstations or at one-piece flow lines. Based on the order information, the required parts are taken from flow racks and assembled step by step to build a functional unit. The existing assembly concepts have two decisive disad­vantages in operational practice. First, a large number of components to be provided leads to long walking distances at the work station or line. Second, as the complexity of the assembly task increases, the informational portion of the work increases, so that paper-based information provision can lead to unnecessary assembly errors and additional times. For these reasons, a compacted assembly system has been developed in which, firstly, material is supplied via driven carousels and, secondly, the necessary information is provided to the operator via a cognitive assistance system. The article shows that this concept can reduce walking distances while avoiding assembly errors and additional times.

With energy prices always rising, it is necessary to have better insulated buildings. However, since the construction rates of new buildings are very low, it is essential to improve the thermal performance of existing buildings and therefore to renovate building facades. Many works have focused on exterior insulation improvements using configurable modular panels. Some software tools for designing layout of insulation solutions have also been proposed. In order to feed these software tools, the goal of this communication is to describe a data model able to represent a facade with all the characteristics necessary for its insulation with modular panels. The model is UML based and will consider three kinds of geometric objects: (i) all openness as window or doors that should be integrated in modular panels, (ii) all façade resisting areas where panel can be attached, (iii) finally all façade singularities that will be transformed either in holes in a panel (light, solar panel…) or in areas to be bypassed by panels (garage door, balcony…). The proposed model and its processing will be illustrated with an example.

The industrial Internet is an industrial ecosystem formed by the deep integration of the Internet and industrial systems. The identity resolution system, which serves as the "identity card of the digital world", has become the basis for supporting the interconnection of information and data inside the industrial Internet. However, the goal of timely information sharing and efficient cooperation among different identity resolution systems has not been met at the current stage. Taking cold chain traceability as a practical example, shortcomings of cold chain information interaction are analyzed and a corresponding identity resolution system is proposed in this paper. The related identity functions for cold chain traceability are explained through the operating technologies and mechanism of each layer including identity registration and resolution. The detailed application is carried out in a specific case study of cold chain traceability, which proved that the adoption of the identity resolution system makes the convey and sharing of information about virus inspection and transportation records more efficient.

An Artificial neural network (ANN) is composed of artificial neurons. Due to the ANN has limited nonlinear mapping ability when processing high complexity information, it can be evolved into high-order neural networks by improving the nonlinearity of S-box. In this paper, we propose a new adaptive genetic algorithm based S-box design (AGA S-box) and apply it to neural networks for improving the ability of information processing. The results of our benchmark test reveal that the optimized Boolean function in S-box leads to better nonlinearity and differential uniformity when compared with other SOTA methods. The AGA S-box can effectively resist linear attack, differential attack and differential power analysis attacks, which significantly increase the security and nonlinearity of the neural network.

Nowadays, additive manufacturing is becoming increasingly important in various industries, e.g., automotive, aerospace, and dental. In particular, powder bed-based additive manufacturing processes for polymers have gained acceptance, as the technology itself offers high productivity and thus a high potential for industrialization. However, the technology is utilized in so-called additive production systems, in which a lack of standardization in the digital and physical processes is common these days. One example is the part recognition within the powder-bed-based process chain. Today, this task is done completely by manual labor, resulting in increased costs. Different approaches for simplifying this step have been evaluated in research, but there has not been discussed an app-based approach for this recognition task. Accordingly, in this paper, an approach for an app-based recognition system is presented, optimized, and benchmarked to existing solutions.

COVID-19 has changed the Indonesian people’s shopping habits for consumer goods. The online retail application came as a response to social distancing and stay-at-home advice. KlikIndomaret is an online retail application that uses the omnichannel concept. As the number of downloads increased, the number of various comments and sentiments on that application also increased. In this study, the researcher did a sentiment analysis aimed to improve the quality of application experiences and retail services. The result of the analysis reflected the services given to customers thus far. The data included reviews and star ratings derived from 4,066 reviews which went under the process of data pre-processing. The methods used in this study were VADER and NLTK, improved by Transformer, without pre-training data. These methods could filter the users’ reviews with sarcasm tone. The results were sentiment labels that were appropriate based on the score comparison of positive and negative sentiments in one user’s review. This approach made the review sentiment process of thousands of data faster and more accurate.

Business success is increasingly dependent on portfolio management and assessment through increased individualization of products. Evaluation of product portfolios from the perspective of production logistics offers new valuable insights in addition to existing market-oriented approaches. Regarding product portfolio management, deleting problematic products is often seen as an unfavorable decision, inducing the potential of unnecessarily overloaded and overcomplex product portfolios. This paper presents a product portfolio assessment and management approach based on production-related distance measures to identify and remove logistically problematic products. In addition, proposed distance measures are identified considering their general meaningfulness and usefulness for cluster analysis. Finally, the presented approach is used on an existing production dataset of a global manufacturer, showing the potential of a production logistics-oriented perspective for product portfolio assessment and management.

For industrial companies, rising product variety intensifies the dilemma between economies of scale and economies of scope, which needs to be overcome to secure a competitive advantage. The strategy of Mass Customization can support companies in achieving this objective but requires the identification and exploitation of complexity reduction potentials in production, e.g. through standardization. This paper presents a data-based approach to create transparency and derive recommendations for complexity reduction measures by applying cluster analysis methods to production data. It is validated using the case of a German parts manufacturer, creating insights about its production complexity by means of a Process Similarity Index and deriving complexity reduction potentials through the classification of the processes into the categories of Outliers, Representative Processes, and Cluster Processes.

Onomatopoeia is a language that sensorially expresses the state of objects and human feelings. Recently, onomatopoeia has found several applications. For example, onomatopoeia is used in product packaging because it familiarizes the products to the customer and easily leaves an impression. Studies have thus analyzed the effect of onomatopoeias and their features. However, the difference between onomatopoeias with similar nuances has not been quantitatively clarified because there are numerous ways of expressing onomatopoeias. This study focusses on understanding the meaning of onomatopoeias. We extracted characteristic words in each part of speech, which influences onomatopoeias and proposed the use of semantic analysis with these characteristic words to understand the relationship between their onomatopoeias. The semantic analysis extracts the onomatopoeia that is obtained by adding affective elements to the existing onomatopoeias ;this helps understand the relationship between onomatopoeias through these affective elements. We used global vectors for word representation (GloVe) to perform the semantic analyses. Thus, , we can use the onomatopoeia when designing a new product package by understanding the consumer’s perception of the onomatopoeia using the proposed method.

Current research has shown that a data-driven approach to identify customer usage patterns provides sufficient indication to cluster, predict and derive decisions. However, specific problems and questions arise due to the peculiarities of complex mechatronic systems. This research aims at the development of a process model to support a systematic reduction of machine variants by analyzing field-gathered data concerning machine function usage. To analyze factors influencing the data-driven identification of machine usage data, 21 semi-structured expert interviews are conducted. Based on the core statement given by the participants, six factors have derived that influence a data-driven identification of machine function usage. Further, based on the derived requirements, the CRISP-DM process model and the SPALTEN problem-solving methodology, an initial process model is developed. This model is evaluated by the conduction of multiple data analyses. To evaluate the designed process-model the effect of the derived analysis results on the potential reduction of variants is examined. The exclusion of two identified unused machine functions indicated a theoretical bisection of the variety of machine variants.

This paper focuses on developing an alternative method for measuring the dimension of a moving vehicle in a side-view on the road. The main tools used in this proposed solution are the Light Detection and Ranging (LiDAR) camera, which can measure the distance between the vehicle and it, and photos of a vehicle, and hence obtain the length and height by calculation and several features. This method aims to have easy and high installation mobility, thereby lowering the manufacturing and maintenance costs. Word identification, object detection and wheel detection are further analysis with an aid of machine learning based classification.

The capacitated vehicle routing problem (CVRP), which is referred as NP-hard problem is a variant of Traveling Salesman Problem (TSP). CVRP constructs the route with the lowest cost without violating vehicle capacity constraints to meet demands of customer nodes. Following the advent of artificial intelligence and deep learning, the use of deep reinforcement learning (DRL) to solve CVRP is giving promising results. In this paper we proposed DRL model to solve CVRP. The transformer-based encoder of our proposed model fuses node and edge information to construct a rich graph embedding. The proposed architecture is trained using proximal policy optimization (PPO). Experiments using randomly generated test instances show that the proposed model gives rise to better results in comparison with the existing DRL methods. In addition, we also tested our model on locally generated real-world data to verify its performance. Accordingly, the results show that our model has a good generalization performance for both of random instance testing to real-world instance testing.

In the last two decades the volatility and complexity of industrial dynamics, and subsequently uncertainties of asset-centric organizations, have increased challenging the risk and value profiles of asset owners and operators. Changing socio-economic, political and commercial circumstances increase the uncertainties of new and operating assets. In such industrial contexts, new perspectives are in demand in order to improve upon the strategic capabilities of asset-centric organizations. Amidst subsequently growing focus on modern collaborative solutions and new business models in some sectors, this paper explores, defines, and elaborates on the notion of industrial agility as a core strategic capability for continuous competitive performance when engineering assets are embedded within a dedicated industrial eco-system to deliver complex objectives.

Planning and operating retail chain distribution processes is becoming more challenging, due to the increasing demand and urban congestion. This research applied a Capacitated Heterogeneous Fleet Vehicle Routing Problem with Multiple Depots (CHFVRPMD) model to optimize a retail chain distribution network with a real-world road network. The model attempts to obtain a distribution plan that minimizes the total distribution cost. A hybrid solution algorithm was proposed to solve the CHFVRPMD model. The solution algorithm was developed by combining the heuristic and metaheuristic methods. A case study was carried out to collect actual data to test the proposed optimization model and the solution algorithm. All the input parameters were estimated using a real-world industry application, to minimize the gap between the theoretical model and the real-world applicability. Computation experiments demonstrate the proposed hybrid solution algorithm’s effectiveness in optimizing the CHFVRPMD model in a feasible computational time. The overall findings reveal that the CHFVRPMD model has achieved about 10.7% savings in daily distribution cost, in optimizing the selected retail distribution network. The proposed model and solution algorithm assist industry practitioners to mitigate transport inefficiencies in retail distribution networks.

High competition in the global market has forced companies to improve sales by different methods. One such method is the partial permissible delay in payment policy where the supplier allows the retailer to settle part of the outstanding amount within a certain period of time. It is common that the trade period is the time between ordering the current and next batches. We consider a retailer who places an order when the inventory level reaches reorder point. A fraction of the price of the batch is paid when placing the order. However, the rest of the balance is settled after a certain delay period. We assume that the lead time is uniformly distributed and the demand observed by the retailer is deterministic and constant. We develop a mathematical formula for the expected total cost which comprised of the holding, ordering and shortage costs. Acknowledgement: The author would like to thank Kuwait University for its support.

A one-year case study has been conducted in an offshore and onshore facilities building as well as ship building and repair plant. The main objectives of the study are to identify and analyze ineffective and unsystematic motions that occurred during the material transportation, develop and select an improved layout to minimize the material transportation distance and validate the proposed layouts using WITNESS simulation software. The approaches used are including MULTI-Floor Plant Layout Evaluation (MULTIPLE), Graph-based Method (GBM), Group technology and WITNESS simulation software. The results of the study show that layouts generated using MULTIPLE and GBM can reduce the total material transportation distance by 54.03% and 52.07% respectively. The reduction in average work in progress (AWIP) for MULTIPLE and GBM layouts are 8.81% and 8.70% respectively. In conclusion, the layout generated using MULTIPLE approach is selected as the best alternative since this alternative has eliminated ineffective and unsystematic motions in the current layout and provided greater reduction in total material transportation distance and AWIP as compared to the current layout and the layout generated using GBM.

An electronic device designed for use by an individual to actively or passively collect data relevant to that individual's health condition and behaviour. This research aims to develop a smart and healthy watch product design that can answer today's human health needs. The methodology of this research is by collecting data from the previous study and continuing with a quantitative study with Questionnaire data processing from 198 respondents intended as the beginning of this research. Finally, the result of watch features will be developed and designed to calculate the calorie number from the hologram as food intake control, water consumption control, vitamins intake control, food calories needed, social media, sleep monitor, exercise needed and reminder. The healthy smart-watch component needed: Lithium battery 400 mAH, GPS, Heart Rate Sensor, Anti-Scratch, Waterproof, Bluetooth, and Smart Application. This smartwatch helps people to develop a life of healthy habits otherwise comorbid people can maintain their health in avoiding the worst condition.

Selecting suitable locations for healthcare facilities hinges on various selection criteria that requires multidimensional or multi-criteria decision-making (MCDM) methods. This study aims to present a scoping review and bibliometric analysis by exploring methods employed for the healthcare facility location selection problem. The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) is used as a reference framework. Articles of interest were retrieved by searching electronic databases, including Scopus, PubMed, Web of Science, and Science Direct, up to early 2022. A final database was compiled based on inclusion and exclusion criteria, after which a bibliometric analysis was executed to answer specific research questions. The full text of the papers was studied to identify which mathematical, machine learning (ML), or MCDM methods were employed and which MCDM environments were used. The results concluded that Analytical Hierarchy Process (AHP) combined with Geographical Information System (GIS) are the most prolific method when selecting a location for healthcare facilities, fuzzy triangular numbers are the most employed, and fuzzy and grey MCDM environments are preferred as they take uncertainty into account.

The COVID-19 pandemic has led to a dramatic loss of human life and the global economy, and presents an unprecedented challenge to public health management for all countries around the world. Access to an accurate epidemic prediction model plays a crucial role in epidemic prevention, infection scale control, and medical resource allocation. In this paper, we first propose a multipeak SEIYAQURD model by using the multipeak learning algorithm to predict the COVID-19 epidemic. The model separates the total population according to characteristics of COVID-19 and can capture trend changes in the epidemic. Then, the fitting period technique and the rolling prediction strategy are proposed to improve the prediction accuracy. Numerical experiments based on the data of COVID-19 in the United States are performed to demonstrate the effectiveness of our proposed method by comparing with two benchmark methods from the literature in two cases, one has a smooth trend and the other has a significant changing trend.

This research aims to develop the criteria determination of Lean and Green practices as well as their relationship towards sustainability among three main dimensions of environment, economy, and society for secondary hospitals in Thailand. Experts who have experience in outpatient service participated in the design of a questionnaire and developing hypothetical models with empirical data from respondents. All observed variables including latent variables go to construct validity using a Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) analysis. After checking and modifying to obtain the most acceptable adjusted model under the consideration of basic indices including the model’s goodness of fit indices, the proposed model demonstrates the criteria of Lean practices integrated with Green practices to sustainable dimensions. Finally, the results of rankings in each area of Lean and Green encourage hospitals to focus on appropriate practices leading to leverage of the performance of the healthcare supply chain system in sustainable ways.

This article serves to analyse and review the literature surrounding a Monitoring and Evaluation methodology utilising a systems-thinking approach to realist evaluation. Such an approach could gain more realistic evaluation results by considering the context of the project as a dynamic system. The article further examines the literature to reflect on what has been done thus far surrounding a systems-thinking approach to realist evaluation in healthcare. The results indicated that a systems-thinking approach to realist evaluation is an emerging topic and has predominantly been applied to projects involving social interventions. Furthermore, the literature revealed that the topic has been applied to the healthcare sector and can be considered suitable for applications in the realm of healthcare.

Advances in information technology cause research to focus on involving smart technology to find optimal healthcare. The aim of this study is to provide a brief survey of the latest literature. This study can be used by researchers as basic for development related to healthcare and swarm intelligence (SI). SI optimization algorithm is proven to be able to solve problems from disease diagnosis, scheduling, routing, and service satisfaction. However, the results obtained have not been integrated, accordingly, effective and efficient healthcare services. This study also presents the potential and trends of future healthcare problems that can be solved by the SI.

Comprehensive digitization is one of the biggest megatrends of our time and affects all industries, including the large sector of transport logistics. To gain transparency about the economic added values in digitization investments, there are various evaluation methods in business administration. However, the transport logistics industry is characterized by individual very heterogeneous market participants and application-specific processes. A deep and diverse understanding of already used and additionally suitable business evaluation methods in transport logistics is therefore not given. To obtain previous scientific knowledge, on the use of business evaluation methods in transport logistics, it is necessary to conduct a systematic literature review (SLR). This ensures the development of the current state of research in compliance with scientific principles. This paper presents a research protocol that describes the methodological procedure for conducting a suitable systematic literature review to meet the necessary scientific requirements of comparability, repeatability, and rigor. This research protocol forms the basis for the procedure of the planned systematic literature review and thus ensures the reproducible and rigorous future extraction of results.

Food loss occurs at every stage in the food supply chain, especially in the early stages of the food supply chain. To increase the added value of food loss, it can be done by changing food loss into food ingredients such as snack products or food supplement powder. This product change will increase the shelf life of the product and provide high nutritional value. A processing facility is needed to convert food loss into value-added food products. Therefore, this study aims to develop a model for determining the optimal number and location of processing facilities. The model developed is in the form of Mixed Integer Linear Programming (MILP) which minimizes production cost, transportation cost, food loss transportation cost, processing facility set up cost, and processed product transportation cost. Numerical example is also given in this study for the application of the resulting MILP model.

Digitization has become an integral part of today's business landscape, and the field of transportation logistics is no exception. Nevertheless, the selection of a suitable digitization technology presents users with major challenges, such as the heterogeneous system landscape, the process-specific requirements, and the decision based on the technology specifications. This paper presents a systematic procedure for selecting suitable digitization technologies using the example of a transport logistics forwarding process. For this purpose, the transport logistics process is first systematically surveyed and modeled using the established modeling notation BPMN 2.0. Based on the process model, the requirements regarding the necessary process information are elicited. A proposed system architecture provides the consideration horizon of possible digitization technologies that can be applied. The selection is based on process-related selection criteria and the specifications of the different technology alternatives. Here, the Analytic Hierarchy Process (AHP) is used as an approach for multi-criteria decision analysis to concretize the digitization technologies available for selection into a final decision set. The paper is concluded by a sensitivity analysis and critical appraisal of the author's own work. The main contribution of this work is the application of the well-established AHP with a strong focus on sensitivity analysis in a new field, such as transport logistics, in order to select a suitable digitization technology in a specific use case.

Despite the fact that the supply chain's sequential optimization of manufacturing, storage, and distribution processes has been thoroughly explored to yield significant profits. The integrated optimization of various processes in a logical manner is crucial to making a difference in the current economic climate, when most industrial sectors are experiencing strong competition. An Algerian company that operates a manufacturing center with two production units inspired and motivated our research in this paper. Each production unit specializes in one type of product, and a fleet of homogeneous trucks used to supply the warehouse (a distribution center) and many clients in direct shipment. Certain aspects must be considered, such as production unit capacity, multiple products, inventory levels, and delivery needs, such as vehicle capacity, number of vehicles available, and lateness penalty. Each vehicle is capable of making many journeys while staying within the deadlines. The goal is to keep the overall cost of manufacturing, inventory, and transportation as low as possible. For multi-product lot-sizing problems with multi-trip direct shipment, we provide a full linear mixed program and we develop simulated annealing algorithm.

Global production network developed to complex systems, which are strongly influenced by their fast pace surrounding. Besides a robust and flexible network structure, the network management gets more important, as it has a direct impact on the network performance. Especially the production management offers great potential to steer the production in the network in the most efficient way, regarding economic as well as ecological aspects. However, the allocation of responsibilities is still often subjective and historically grown. In order to objectify this process, the presented paper analysis how the degree of centralization influences the decision quality regarding decision in strategical- and operational production management. Thus, the paper provides evidences, how further potentials in the network management can be realized by a targeted degree of centralization.

Municipal Solid Waste (MSW) Management is widely being practiced in the current times due to the rapid waste generation of humans of the increasing human population. However, the practice of MSW management through its facilities and activities can potentially harm the health of individuals and the environment. With this, proper disposal, methods, and tools are continuously being developed to optimize resources, routes, and scheduling of waste collection and reduce the negative impacts on the environment and humans. A common tool is the Vehicle Routing Problem (VRP) model, where this study developed a VRP model that considers multi-vehicles and multi-objectives through the Goal Programming approach and covers the research gap of considering objectives in economic, environmental, health, and time. The model is run using General Algebraic Modelling Software (GAMS) to validate it and perform sensitivity analysis to further study the model behavior. The study was able to produce a VRP model that is able to consider all these objectives and propose a method to safely collect and dispose of waste while reducing other negative impacts to the environment and community.

There has been much interest in the unicorn companies emerging around the world, the vast amount of capital raised for these digital start-ups, the disruption these ventures have brought to several industries, as well as their global impact. Despite this interest digital start-ups, digital opportunities, and their venture creation process do not have a unified definition, or a unified venture creation model. This paper reports an explorative PhD study of the venture creation process of digital, innovative start-ups. The full thesis is available online [1]. We look at what is missing from entrepreneurial process models, when the context is digital technology, and how early stage digital start-ups carry out the venture creation process. The process starts in the pre-phase of antecedents and ends at the launching and scaling of the venture.The research analysed existing models to propose a novel process for the digital, innovative start-up venture creation, which describes the nature and patterns of the process. The resulting conceptual model was developed based on literature of entrepreneurship, information systems, and innovation management, and has been empirically assessed with a multi-method qualitative research design [1] (not reported here).The contribution to the entrepreneurship theory is a new, illustrative model, of venture creation process of digital, innovative start-up, including the emergent outcomes (e.g. mobile apps, web-based solutions, digital platforms, software solution as SaaS) of the process having a digital artefact(s) in the core.

The COVID-19 pandemic has changed work arrangements and increased worldwide unemployment. Increasingly, many people have turned to gig work for income. However, low-skilled gig workers, such as food delivery personnel and ride-hailing drivers, are vulnerable to a plethora of disadvantageous working conditions, such as unstable income, lack of medical insurance, and heavy workload. Working remotely outside the workplace has also led to severe loneliness and isolation. Survey results from 100 gig-workers indicated that receiving social support and positive emotion improved job performance and satisfaction. Hence, we propose a peer-mentor supporting system. This paper primarily contributes to improving public awareness about the disadvantageous situations of gig workers. By examining the gig workers' perceptions, emotions, and motivations, this paper contributes to integrating job satisfaction and their inner work-life system. We suggest organizations broadcast gig workers' contributions during the COVID-19 pandemic, which improved their sense of responsibility and intrinsic motivation.

Launching branded mobile payment becomes an emerging trend, so how to attract consumers’ attention and retain customers has become a tough task. Therefore, we investigate how to keep customers using the branded mobile payment. The research model is based on the customer engagement model and considers rational and affective routes inducing continuous usage intention. The customer engagement model describes the process from customers’ experience of products/services to loyalty. Usability and brand experience are considered at the initial stage. Perceived benefit and inertia are outcomes of the evaluation and then induce customer engagement and continuous usage. This study conducted an online survey. Findings are drawn from analyzing 399 usable collected data. At the stage of experiential product/service, usability is more important than brand experience. In comparison with the influence of rational and affective evaluation, the effect of brand benefit is greater than inertia. Looking into usability, consumers emphasize the navigation of the user interface and user interface graphics design. Consumers pay more attention to functional and symbolic benefits. Consumers’ affective-based inertia contributes more to inertia. Theoretical and managerial implications are also listed.

Thailand has one of Asia's fastest-growing economies, but many people are still unfamiliar with E-Commerce. because of the covid-19 pandemic, which forces many customers to use E-Commerce platforms. The purpose of this study is to look into the factors that influence a company's decision to adopt online shopping in Thailand. The goal of this study is to look into the factors that influence customers' concerns when using an E-Commerce platform in Thailand, as well as the types of customers who use online platforms during the COVID-19 pandemic. EFA used data collection and process interviews as qualitative and data analysis techniques in this study. The sample for this study was derived from a survey of customers who use E-Commerce platforms. The result showed 5 groups of component factors, consisting of 1.  Customer trust and Transportation, 2. Product review and quality, 3. Promotion, 4. Commercial images and Financial, and 5 Advertising media.

A minimum viable product (MVP) is a product with minimal but enough number of features to be used by end users to assess the product in terms of its functionality and ease of use. Startups, specifically digital startups use MVP to gain better insight in to design issues and features of newly made products to make them better in future. A better product in terms of desired features and innovation ensures its applicability in market and enables startups to scale with the passage of time. As there is very little research on usage and impact of MVPs on startup performance we have tried to gain insight in to different usages of MVPs in digital startups. Our findings show that different kinds of MVPs such as digital prototypes, 3D MVPs etc. serve different purposes such as feature validation and idea extraction at different phases of startups. We have also identified different challenges faced by technical team while designing MVP. These include challenges regarding time, budget, communicating ideas with stakeholders and identifying the most valuable features for the MVP.

The shift to online communication and e-commerce in the digital landscape aggravates challenges faced by older adults who are not technologically savvy. These challenges entail predicaments among vulnerable groups in society, especially older adults in rural areas. Adopting an easily accessible technology such as smartphones is necessary for their survival in the digital world, but not sufficient without effective use of it. Smartphones provide ubiquitous access to education and information for the marginalized older adults to learn and address their needs by using smartphones cost-effectively. Through in-depth interviews with 13 Bottom 40% (B40) Malaysian older women in rural areas, this paper reveals that these rural older women were facing different challenges when using smartphones. There were four aspects of physical, cognitive, psychological, and usability challenges. To address these challenges, this paper proposes that family support, in-person guidance, and a user-friendly application interface are essential needs for rural older women to learn how to use smartphones. A conceptual framework was proposed to assist older women in rural areas to overcome challenges and address their needs in learning through smartphones.

With the dissemination of IoT-enabled devices, innovative services are being provided that have not been possible before. One possible example is the subscription-based sales of IoT devices. Collected data can be used to improve consumer service as well as device manufacturer’s SCM. However, it will not be widely deployed unless the benefit is clear to both consumer and manufacturer. This study assumes a subscription-based sales of IoT device and quantifies its benefits by using an agent-based simulation. This study also evaluates factors that affect benefit of the service to find the preferable situations of the sales. The result of this study is valuable when companies decide whether they should start a subscription service or not.

This paper is aimed at investigating the benefits that SMEs in the food industry gain by having innovative products. For this research, a questionnaire to collect data from SME representatives was distributed through email after drawing on the database in Thailand. The 83 SMEs from the food industry that responded were divided into two groups: 1) 38 non-innovative ones that had delivered no innovative products and 2) 45 innovative that had created new products for this type of industry. The author adopted t-testing to find the difference of benefits in a range of aspects, including: cost saving, fewer team members, less office space needed, time savings, decrease in routine work, fast in response time, increase in collaboration, greater market penetration and work flexibility. The results revealed benefits for innovative SMEs in all aspects considered when compared with non-innovative ones. In particular, innovative SMEs exhibited quicker response times and increased collaboration for the business than their counterparts. The findings support the focal SMEs taking advantage of the benefits of innovation for their business by creating and releasing onto the innovative products.

This paper demonstrates a multi-disciplinary co-creation approach to explore local governance of future regional development in remote areas. This study aims to increase the understanding of factors that can accelerate or hamper regional development. In particular, the inquiry focuses on local decision-makers’ perceptions about a future society, regional cooperation, and reasons for the influx of people, businesses and industries. The results reveal discrepancies and similarities between local perceptions and regional necessities. The study thus contributes valuable insights about crucial issues for future development of remote areas, such as strengthening local identities so that local communities fit together in a region appearing as a complete entity to potentially interested parties.

Outcome-based models (OBMs) guarantee quality outcomes through pay-for-performance mechanisms. This article considers OBMs to reduce healthcare spending and improve healthcare outputs by conducting a scoping review (SR) of literature surrounding this topic. The set of articles considered was visualised and analysed through Bibliometrix and full-text reading. Key findings from the review include the growth of the topic in the last five years and the need for further research on contract agreements, cost/risk-sharing, measurement of outcomes, types of OBMs, and guiding conceptual frameworks.

The circular economy is a cornerstone in a sustainable model for production and consumption. From an industrial perspective, it can be achieved by deploying new business models that shift focus from producing and selling products to providing access to products. Several innovative ways to support access to physical products have been suggested and the emergence of smart products enables new bundles of hardware, software, and services. These ways are, however, not sufficient since the products and services also need to be prepared for leasing, repairing, refurbishing, reselling, upgrading, and recycling already in the development phase. Furthermore, to be able to sell their products on the European market, all companies must conform to the upcoming legislation presented by the European Commission and similar requirements in the US and other large markets. This research explores the challenges faced by 4 product development and manufacturing companies, regarding the future demands on sustainability, both legally and from consumers. It presents a tentative framework for Business model driven development and manufacturing of smart products to handle the new legal and business landscape.

Building Information Modeling (BIM) tries to enable the production and management of a digital display of physical and functional characteristics of sites. Especially, integration of BIM in the processes related to the design, construction, and operation of buildings according to the aspects of environmental sustainability is one of the motivational topics. Using the Internet of Things (IoT) paradigm in Industry 4.0, this paper has tried to enhance the BIM more effectively and efficiently. The paper has focused on using the capabilities of IoT and its applications for BIM. Moreover, the Axiomatic Design (AD) framework has been applied to ensure the feasibility proposed framework. The AD ensures that the different functionalities and features of IoT in the proposed framework will not create a complex environment. The resulted framework has included the real-time interactions among the stakeholders and residents/stakeholders in the building while considering the energy consumption concerns. Moreover, capabilities like realization of users' preferences, using data mining techniques for learning from past experiences for accurate and timely adjustment, managing building performance have been considered.

BRILink is a branchless banking service that provides quintessential banking transactions to the unbanked population in Indonesia through human agents. Due to the important role of agents, the quantity and quality of agents become critical in BRILink operations to achieve the expected business performance. Thus, new agent acquisition as well as agent upgrade and activation are essential as endeavors to maintain and improve agent quantity and quality. However, the existing acquisition, upgrade, and activation method is troublesome due its high reliance on subjective judgment and non-data-driven approach. Therefore, human-centered machine learning solutions are implemented to determine highly qualified and potential customers to be acquired, and agents to be upgraded and activated. Based on evaluation, the implemented machine learning solutions significantly can achieve thousands of agent acquisition and up to 18% increases of agent upgrade and activation, then increase above 13% fee-based income on average.

Electronic commerce (e-commerce) opens up various growth possibilities, with product returns being one of the major challenges. Product returns prediction can be beneficial in taking business decisions in e-commerce. This research is based on an e-commerce company in India with nearly 25% product returns as a case study. Logistic-regression-based statistical analysis was used to predict the dichotomous dependent variable (product returns).[1]Product size, payment mode, price, quantity, ZIP codes, and states were considered as independent variables. A decision support system was proposed based on the variables and a machine learning approach. A total of 3,187 past orders were used to train the model, which was applied in a one-month pilot study. The model could predict 60% of correct product returns with an overall efficiency of 87%. The case company used these predictions to take preliminary decisions on predicted return orders, saving 17% of the costs associated with product returns.

An increased push towards innovation and testing of autonomous shipping has primarily begun due to the need for cutting operational costs, for increasing safety at sea, for increasing productivity and for reducing carbon-footprint to make shipping more sustainable to meet the greenhouse gas emission targets of IMO (International Maritime Organization); ably supported by the enabling environment by governments and institutions worldwide. The objective of this paper is to establish a body of knowledge for real-time risk analysis and its application to autonomous (unmanned) ships. A three-pronged systematic literature review is conducted focusing on the research topics of real-time risk analysis, autonomous ships and finally real-time risk analysis of autonomous ships. It yields a considerable number of results for the former two with 80 and 55 research studies respectively, whereas a sparse 15 research studies for the latter indicating a recent interest in this topic. The study acts as a guide for future researchers working within real-time risk analysis of autonomous shipping, and for developing real-time risk models for autonomous ships. It is equally applicable to other autonomous marine systems.

The COVID-19 pandemic has affected hundreds of millions of people in countries around the world. The number of new cases has reached 100,000 per day since the last wave of COVID-19 in Vietnam. It has become very apparent that the front-line employees are overworked. There are not enough PCR tests to keep up with the rate of the virus spreading in our community. In addition, the PCR test is expensive for the government, highly invasive, and time-consuming for patients, which discourages individuals from visiting the clinic for testing. Therefore, it is very necessary to have a quicker and simpler way of prescreening patients. This is the reason why the paper will introduce a new artificial intelligence application, named COVCOUGH, to early detect COVID-19 patients using cough sounds recorded by smartphones. During the recent peak of the epidemic in Vietnam, the COVCOUGH has been deployed and has more than 10,000 users.

Intelligent surveillance systems are inherently computationally intensive. And with their ever-expanding utilization in both small-scale home security applications and on the national scale, the necessity for efficient computer vision processing is critical. To this end, we propose a framework that utilizes modern hardware by incorporating multi-threading and concurrency to facilitate the complex processes associated with object detection, tracking, and identification, enabling lower-powered systems to support such intelligent surveillance systems effectively. The proposed architecture provides an adaptable and robust processing pipeline, leveraging the thread pool design pattern. The developed method can achieve respectable throughput rates on low-powered or constrained compute platforms.

This paper proposes a condition monitoring method for the early defect detection in a chain sprocket drive (CSD) system and classification of fault types before a catastrophic failure occurs. In the operation of a CSD system, early defect detection is very useful in preventing system failure. In this work, eight fault types associated with the CSD system components, such as the gear tooth, bearings, and drive motor shaft, were arbitrarily damaged and incorporated into the CSD system. To detect the fault signals during the CSD system operation, the vibration was measured using an Internet of Things (IoT) device, which features a wireless MEMS accelerometer, Bluetooth function, Wi-Fi function, and battery. The IoT device was mounted on the gearbox housing. The measured one-dimensional vibration time-series was transformed into time-scale images using continuous wavelet transform (CWT). A convolution neural network (CNN) was employed to extract deep features embedded in the images, which are closely related to fault types.

With both production planning and scheduling being isolated activities that are conducted by different departments at manufacturing firms, understanding the interactions between various components within a production system is imperative for better coordination between the production planning and scheduling stages in a dynamic manufacturing environment. The main challenge arises in improving production performance due to decision makers being unsure of key parameters affecting the production performance. Digital twin presents the potential in improving the production performance through combining various key production parameters. In this research, by making use of production data retrieved within smart manufacturing setting, a surrogate based adaptive annealing genetic algorithm (AAGA) is implemented to generate hybrid production strategies for updating the production plan and improving the production performance at the revision stage of production planning and scheduling. By constructing a surrogate model using multivariate adaptive regression spline (MARS), it is then used as the fitness function in AAGA for the intelligent search of hybrid production strategies. The effectiveness of the proposed surrogate based AAGA is demonstrated using an industrial case study of a wafer fabrication production.

This research is on an antenna gain enhancement of a flexible antenna for electromagnetic wave based power transfer system. Especially, it is about the change of the center frequency and the compensation method when the flexible antenna is bent. For this purpose, a patch antenna was designed using Polydimethylsiloxane(PDMS), which is a representative flexible substrate, and the variation of center frequency according to the degree of bending was measured. As a result, it was confirmed that when the antenna is bent outward, the inductance increases and the center frequency increases. To improve this, a capacitor was added to compensate the frequency. In conclusion, we could compensate the shifted frequency with the initial center frequency and improve the antenna gain up to 1.7dBi.

With diverse and rapidly changing customer demands, companies need a flexible and fast response in the name of mass customization. Correspondingly, product design based on modularization and a common platform has been emphasized, and digital transformation became the starting point for realizing and facilitating mass customization. In terms of production, it is required for companies to switch to an efficient and flexible manufacturing system with optimal production line configuration to quickly respond to a variety of market changes as well as reduce cost and time. In this research, we aim to present decision criteria and a methodology to determine optimal line configuration through a digital twin-based production line modelling and simulation process. To develop the decision-making process for the production line configuration, significant variables such as utilization rate, various costs, and time from each case of the production line models are evaluated and compared. An industrial case of an assembly line is applied to develop a digital twin-based simulation model and to validate the applicability of the proposed methodology.

Applications of AI to solve business problems are diverse, and multiple solutions exist for specific problems. In the business scene, it is no longer possible to keep up with all the AI technologies that are evolving daily. We need an appropriate matching system to match a problem and a solution. GPT3 has been applied to many cases as a text generation model, and in recent years, it also has the function of a question-answering system. On the other hand, there has been little discussion on the limitations and possibilities of the close domain, which uses academic knowledge as its domain. In this study, we tested whether GPT-3 can return an appropriate AI solution by using arXiv data to train GPT-3 and inputting a problem expression. Comparing the results with those of several machine learning methods showed that our implementation of the question-and-answer system is effective in accuracy and practicality. Selection of ‘max len’ was essential for outputting suitable solutions. The results of this experiment are significant not only for academics but also for the business of text generation.

With the development and upgrading of industries, how to achieve sustainable development under the conflict of profit-making and environmental damage has become a very important issue. The Ellen MacArthur Foundation has proposed that circular economy is a promising way to achieve sustainable development. Through the closed cycle between resources, waste and environmental pollution can be reduced. Therefore, in our research, a nonlinear programming model is established to provide a new industrial structure for the government as a reference for policy-making. This structure can ensure the balance of supply and demand in the food, energy, and water industries (FEW Nexus) and overall economic development. The research uses growth rates and growth amount as control indicators to measure the overall value-added of all sectors and individual industries to establish an industrial structure that can strengthen the overall economy. The results show that the new industrial structure can reduce the harm to the environment, stabilize food, energy, and water resources, and improve the overall economy while the domestic industries are developing and upgrading.

With the recent popularity of shared electric bikes, the operators have been widely concerned about the high cost of battery swapping. At present, most platforms use trucks to visit the parking stations to swap the batteries of electric bikes with low power, which is inefficient and environmentally unfriendly. This paper proposes a user-based battery swapping strategy to rationalize users’ riding behavior through incentives. Specifically, e-bikes with low power are recommended to selected users according to their destination to realize the concentration of low-power e-bikes at certain stations, which will relieve the task of battery-swapping trucks. This strategy is modeled as an integer programming problem to decide station selection, user relocation, and truck routing. A variable neighborhood search algorithm is designed to solve the problem. Numerical results on different instances and a real-world case verify the effectiveness of our model and method.