Pub Date : 2024-09-25DOI: 10.1109/TEM.2024.3463179
Philipp Zellmer;Lennart Holsten;Jacob Krüger;Thomas Leich
The automotive industry is undergoing a significant transformation, with vehicles evolving into complex, interconnected cyber-physical systems. This transformation is caused by new customer demands, legal standards, and technological innovations, which lead to an increasing amount of electronic control units, software, and features. To address the consequent software-related challenges, automotive manufacturers are adopting methodologies like software product-line engineering, electrics/electronics platforms, and product generation engineering. However, each of these methodologies relies on an own vocabulary, necessitating a unification of the divergent understandings and interpretations of key terms and definitions. In this article, we investigate and discuss a terminological framework that provides a common ground for specifying a unified product-structuring concept. For this purpose, we conducted a systematic mapping study to develop a framework of existing terms and definitions used to describe product-structuring concepts in software, electrics/electronics, as well as mechanical engineering. We discuss the differences and commonalities of the terminologies to help practitioners in integrating and applying product-structuring concepts as well as to guide future research.
{"title":"The Terminology of Automotive Product-Structuring Concepts: A Systematic Mapping Study","authors":"Philipp Zellmer;Lennart Holsten;Jacob Krüger;Thomas Leich","doi":"10.1109/TEM.2024.3463179","DOIUrl":"https://doi.org/10.1109/TEM.2024.3463179","url":null,"abstract":"The automotive industry is undergoing a significant transformation, with vehicles evolving into complex, interconnected cyber-physical systems. This transformation is caused by new customer demands, legal standards, and technological innovations, which lead to an increasing amount of electronic control units, software, and features. To address the consequent software-related challenges, automotive manufacturers are adopting methodologies like software product-line engineering, electrics/electronics platforms, and product generation engineering. However, each of these methodologies relies on an own vocabulary, necessitating a unification of the divergent understandings and interpretations of key terms and definitions. In this article, we investigate and discuss a terminological framework that provides a common ground for specifying a unified product-structuring concept. For this purpose, we conducted a systematic mapping study to develop a framework of existing terms and definitions used to describe product-structuring concepts in software, electrics/electronics, as well as mechanical engineering. We discuss the differences and commonalities of the terminologies to help practitioners in integrating and applying product-structuring concepts as well as to guide future research.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14974-14990"},"PeriodicalIF":4.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1109/TEM.2024.3457233
Xincheng Wang;Mijia Gong;Tianyu Gong
This research builds upon problemistic search theory to explain why and when external shocks may lead to firm first-time digital innovation, a novel and risky solution. Problemistic search theory posits that the serious problems faced by firms may trigger their problemistic search scope for solutions through two processes: discovery process and evaluation process. Our theorization explains why external shocks such as the COVID-19 create novel or radical problems for firms, and thus trigger firms’ problemistic search scope for solutions in the discovery process, increasing firms’ willingness to delve into first-time digital innovation. Furthermore, problemistic search theory also suggests that in the evaluation process, the evaluation of solutions depends on both the firm's own experience (i.e., firm's prior R&D) and the experiences of others (i.e., firms’ social linkages). To be specific, cognitive entrenchment and inertia embedded in the firm's prior research and development (R&D) may, however, pose barriers to the evaluation of first-time digital innovation. Moreover, a firm's linkage with digital firms can potentially alleviate such cognitive barriers. Using a difference-in-differences design based on data from Chinese published firms, we find empirical support for our theoretical predictions. This research deepens our understanding of the literature on firms’ digital innovation, problemistic search theory, and R&D.
{"title":"External Shocks, R&D Investment, and Firms’ First-Time Digital Innovation","authors":"Xincheng Wang;Mijia Gong;Tianyu Gong","doi":"10.1109/TEM.2024.3457233","DOIUrl":"https://doi.org/10.1109/TEM.2024.3457233","url":null,"abstract":"This research builds upon problemistic search theory to explain why and when external shocks may lead to firm first-time digital innovation, a novel and risky solution. Problemistic search theory posits that the serious problems faced by firms may trigger their problemistic search scope for solutions through two processes: discovery process and evaluation process. Our theorization explains why external shocks such as the COVID-19 create novel or radical problems for firms, and thus trigger firms’ problemistic search scope for solutions in the discovery process, increasing firms’ willingness to delve into first-time digital innovation. Furthermore, problemistic search theory also suggests that in the evaluation process, the evaluation of solutions depends on both the firm's own experience (i.e., firm's prior R&D) and the experiences of others (i.e., firms’ social linkages). To be specific, cognitive entrenchment and inertia embedded in the firm's prior research and development (R&D) may, however, pose barriers to the evaluation of first-time digital innovation. Moreover, a firm's linkage with digital firms can potentially alleviate such cognitive barriers. Using a difference-in-differences design based on data from Chinese published firms, we find empirical support for our theoretical predictions. This research deepens our understanding of the literature on firms’ digital innovation, problemistic search theory, and R&D.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14824-14835"},"PeriodicalIF":4.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1109/TEM.2024.3466949
Keshav Singh Rawat;Tarun Sharma
Quantum computing (QC) has emerged as a promising research area transitioning from theory to practical realization, driven by interdisciplinary integration. To gain a comprehensive understanding of the evolving research landscape in QC, this study conducts a scientometric analysis of scientific literature retrieved from the Scopus database between 2010 and 2023. Employing the CiteSpace visualization tool, this study examines publication trends, citation patterns, and identifies the prestigious journals in literature. Furthermore, collaboration analysis reveals the top collaborating countries highlighting the active global network of research in QC. Moreover, author cocitation analysis identifies prominent contributors, underscoring their impact and influence in the field. In addition, keyword cooccurrence analysis and thematic analysis pinpoints the areas of significant interest and emerging research frontiers. The content analysis further explores these findings to provide in-depth insights in to themes and trends shaping the field. By providing insights into the evolution of QC, this study contributes to a deeper understanding of technological advancements and determines the future pathway of the field.
{"title":"A Leap From Theory to Reality: Knowledge Visualization of Quantum Computing","authors":"Keshav Singh Rawat;Tarun Sharma","doi":"10.1109/TEM.2024.3466949","DOIUrl":"https://doi.org/10.1109/TEM.2024.3466949","url":null,"abstract":"Quantum computing (QC) has emerged as a promising research area transitioning from theory to practical realization, driven by interdisciplinary integration. To gain a comprehensive understanding of the evolving research landscape in QC, this study conducts a scientometric analysis of scientific literature retrieved from the Scopus database between 2010 and 2023. Employing the CiteSpace visualization tool, this study examines publication trends, citation patterns, and identifies the prestigious journals in literature. Furthermore, collaboration analysis reveals the top collaborating countries highlighting the active global network of research in QC. Moreover, author cocitation analysis identifies prominent contributors, underscoring their impact and influence in the field. In addition, keyword cooccurrence analysis and thematic analysis pinpoints the areas of significant interest and emerging research frontiers. The content analysis further explores these findings to provide in-depth insights in to themes and trends shaping the field. By providing insights into the evolution of QC, this study contributes to a deeper understanding of technological advancements and determines the future pathway of the field.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14861-14877"},"PeriodicalIF":4.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/TEM.2024.3461508
Tássia Farssura Lima da Silva;Darli Rodrigues Vieira;Marly Monteiro de Carvalho
Building information modeling (BIM) is transforming the construction life cycle. Nonetheless, there is a notable gap in research regarding the key challenges associated with BIM. This study aims to investigate the primary challenges in the design phase and their implications for project success. To address these objectives, cross-country case studies were conducted in four large engineering companies from the USA, Canada, Brazil, and United Arab Emirates. Data were collected through 23 semi-structured interviews with managers, engineers and directors, and content analysis was performed using NVIVO software. The resulting coding structure revealed the following categories: organizational and cultural issues, professional and knowledge issues, technological and operational issues, cost issues, BIM specific issues, design issues, data issues, and information and communication issues. The findings highlighted the most significant challenge as the lack of BIM knowledge or expertise. Additionally, an important enabler in the design phase is the accuracy of data provided by BIM, which enhances project management analysis. Finally, the BIM challenges and enablers influence various benefits dimensions, particularly on the efficiency.
{"title":"Exploring the Challenges in Building Information Modeling (BIM) During the Design Phase: Evidence From Cross-Country Studies","authors":"Tássia Farssura Lima da Silva;Darli Rodrigues Vieira;Marly Monteiro de Carvalho","doi":"10.1109/TEM.2024.3461508","DOIUrl":"https://doi.org/10.1109/TEM.2024.3461508","url":null,"abstract":"Building information modeling (BIM) is transforming the construction life cycle. Nonetheless, there is a notable gap in research regarding the key challenges associated with BIM. This study aims to investigate the primary challenges in the design phase and their implications for project success. To address these objectives, cross-country case studies were conducted in four large engineering companies from the USA, Canada, Brazil, and United Arab Emirates. Data were collected through 23 semi-structured interviews with managers, engineers and directors, and content analysis was performed using NVIVO software. The resulting coding structure revealed the following categories: organizational and cultural issues, professional and knowledge issues, technological and operational issues, cost issues, BIM specific issues, design issues, data issues, and information and communication issues. The findings highlighted the most significant challenge as the lack of BIM knowledge or expertise. Additionally, an important enabler in the design phase is the accuracy of data provided by BIM, which enhances project management analysis. Finally, the BIM challenges and enablers influence various benefits dimensions, particularly on the efficiency.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14846-14860"},"PeriodicalIF":4.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1109/TEM.2024.3459032
Zhijian Cui;Vladimir Baraboshkin;Dilney Gonçalves
The conventional wisdom in idea selection literature typically assumes that selecting-the-best ideas and eliminating-the-worst ideas represent the two sides of the same coin. In other words, selecting-the-best ideas from a pool of ideas should be equivalent to eliminating-the-worst ones until only the best remain. However, our explorative experimental investigation regarding the accuracy of these two idea evaluation processes indicates major differences. Specifically, our results suggest that the elimination process outperforms the selection process in terms of the probability of selecting the highest quality innovation ideas. Our text analysis further reveals that when participants are asked to do the selection or elimination tasks, their cognitive perception of each idea tends to focus on different aspects of the ideas, namely, the positive (pros) vs. negative (cons) sides of the same idea. We use a 2 × 2 experimental design by priming the participants with pros and cons information in selecting-the-best and eliminating-the-worst scenarios. Surprisingly, we find that with pros, the selection process outperforms the elimination process, whereas with cons, the efficacies of the two idea evaluation processes are equivalent. Additionally, we find that the efficacy of the selection process does not change whether the participant has pros or cons, yet the efficacy of the elimination process is significantly improved with cons compared to with pros. Based on analysis of the experimental data, we present and test an explanatory model in which the evaluation accuracy, measured in terms of the percentage of matches, is influenced by factors, such as the evaluation process, response duration, and the moderating effect of cognitive biases.
{"title":"Selecting-the-Best vs. Eliminating-the-Worst: An Experimental Investigation of Idea Evaluation Processes Under Cognitive Bias Conditions","authors":"Zhijian Cui;Vladimir Baraboshkin;Dilney Gonçalves","doi":"10.1109/TEM.2024.3459032","DOIUrl":"https://doi.org/10.1109/TEM.2024.3459032","url":null,"abstract":"The conventional wisdom in idea selection literature typically assumes that selecting-the-best ideas and eliminating-the-worst ideas represent the two sides of the same coin. In other words, selecting-the-best ideas from a pool of ideas should be equivalent to eliminating-the-worst ones until only the best remain. However, our explorative experimental investigation regarding the accuracy of these two idea evaluation processes indicates major differences. Specifically, our results suggest that the elimination process outperforms the selection process in terms of the probability of selecting the highest quality innovation ideas. Our text analysis further reveals that when participants are asked to do the selection or elimination tasks, their cognitive perception of each idea tends to focus on different aspects of the ideas, namely, the positive (pros) vs. negative (cons) sides of the same idea. We use a 2 × 2 experimental design by priming the participants with pros and cons information in selecting-the-best and eliminating-the-worst scenarios. Surprisingly, we find that with pros, the selection process outperforms the elimination process, whereas with cons, the efficacies of the two idea evaluation processes are equivalent. Additionally, we find that the efficacy of the selection process does not change whether the participant has pros or cons, yet the efficacy of the elimination process is significantly improved with cons compared to with pros. Based on analysis of the experimental data, we present and test an explanatory model in which the evaluation accuracy, measured in terms of the percentage of matches, is influenced by factors, such as the evaluation process, response duration, and the moderating effect of cognitive biases.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14775-14788"},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1109/TEM.2024.3458901
Diaz Rafael;Acero Beatriz;Behr Joshua;Juita-Elena Yusuf
This article examines the logistics of evacuation and sheltering of medically fragile populations, who tend to have less capacity to safely manage rapidly shifting storm-induced conditions under a pandemic environment. Health awareness and the health and financial impacts of the pandemic have altered households’ evacuation and sheltering calculus. The timing and volume of evacuees have significant implications for configuring available transportation infrastructures and means and opening shelters and refuge of last resort as the storm materializes and degrades the built environment. This article asks five questions about the effect of medical fragility, health risk awareness, health and financial impacts of the pandemic, and the availability of noncongregate shelters on evacuation and sheltering behavior. The empirical analysis uses data from a survey of 2200 households conducted during the COVID-19 pandemic to gauge risk perceptions under the compound threat of a hurricane and pandemic. Takeaways from our findings have significant implications for managers and policymakers and indicate, first, that medically fragile households are more likely to evacuate than nonmedically fragile households. Second, households with health concerns about the pandemic are more likely to evacuate regardless of medical fragility. Third, the expected sheltering of these segments varies depending on the facilities provided by the authorities. Anticipating the behavior of population groups allows managers to deploy technology that supports effective resource configuration and coordination and provides effective emergency service during evacuation planning and execution.
{"title":"The Logistics of Evacuating and Sheltering Medically Fragile Populations Under Pandemics","authors":"Diaz Rafael;Acero Beatriz;Behr Joshua;Juita-Elena Yusuf","doi":"10.1109/TEM.2024.3458901","DOIUrl":"https://doi.org/10.1109/TEM.2024.3458901","url":null,"abstract":"This article examines the logistics of evacuation and sheltering of medically fragile populations, who tend to have less capacity to safely manage rapidly shifting storm-induced conditions under a pandemic environment. Health awareness and the health and financial impacts of the pandemic have altered households’ evacuation and sheltering calculus. The timing and volume of evacuees have significant implications for configuring available transportation infrastructures and means and opening shelters and refuge of last resort as the storm materializes and degrades the built environment. This article asks five questions about the effect of medical fragility, health risk awareness, health and financial impacts of the pandemic, and the availability of noncongregate shelters on evacuation and sheltering behavior. The empirical analysis uses data from a survey of 2200 households conducted during the COVID-19 pandemic to gauge risk perceptions under the compound threat of a hurricane and pandemic. Takeaways from our findings have significant implications for managers and policymakers and indicate, first, that medically fragile households are more likely to evacuate than nonmedically fragile households. Second, households with health concerns about the pandemic are more likely to evacuate regardless of medical fragility. Third, the expected sheltering of these segments varies depending on the facilities provided by the authorities. Anticipating the behavior of population groups allows managers to deploy technology that supports effective resource configuration and coordination and provides effective emergency service during evacuation planning and execution.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14882-14896"},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A robust knowledge-sharing network is designed for horizontal integration under disruption risks and epistemic uncertainties by introducing a novel optimization model using a fuzzy robust possibilistic programming approach to optimize knowledge sharing among supply chain members with varying knowledge levels. In this article, we aim to identify an efficient knowledge-sharing network, thereby reducing costs and enhancing suppliers' knowledge levels. By challenging the common assumption that companies with higher knowledge levels are always the primary contributors and have more added value for cooperation, this study highlights their potential inefficiencies and higher sharing costs. The proposed model promotes the integration of diverse knowledge sources within the supply chain, emphasizing the importance of horizontal integration. It advocates for comprehensive knowledge sharing among suppliers and organizations to enhance supply chain efficiency, collaboration, and performance while reducing costs. Quantitative analysis demonstrates that knowledge sharing significantly increases supply chain integration, and the study endorses the use of multiobjective mathematical programming for optimal decision making in scheduling. The results emphasize the value of collaborating with closely aligned companies to minimize knowledge-sharing costs and enhance broader organizational collaboration. Furthermore, the introduced model proposes practical execution scheduling and knowledge-sharing processes, as evidenced by a case study, leading to effective execution scheduling, reduced costs, improved communication, strengthened collaboration, and increased supply chain efficiency. Overall, this article contributes to research in supply chain management and knowledge-sharing models, enabling them to navigate constraints and market dynamics to improve supply chain performance through effective knowledge sharing and collaboration.
{"title":"Horizontal Integration Through Knowledge Sharing in the Supply Chain Under Uncertainty","authors":"Mostafa Jafari;Shayan Naghdi Khanachah;Peyman Akhavan","doi":"10.1109/TEM.2024.3459609","DOIUrl":"https://doi.org/10.1109/TEM.2024.3459609","url":null,"abstract":"A robust knowledge-sharing network is designed for horizontal integration under disruption risks and epistemic uncertainties by introducing a novel optimization model using a fuzzy robust possibilistic programming approach to optimize knowledge sharing among supply chain members with varying knowledge levels. In this article, we aim to identify an efficient knowledge-sharing network, thereby reducing costs and enhancing suppliers' knowledge levels. By challenging the common assumption that companies with higher knowledge levels are always the primary contributors and have more added value for cooperation, this study highlights their potential inefficiencies and higher sharing costs. The proposed model promotes the integration of diverse knowledge sources within the supply chain, emphasizing the importance of horizontal integration. It advocates for comprehensive knowledge sharing among suppliers and organizations to enhance supply chain efficiency, collaboration, and performance while reducing costs. Quantitative analysis demonstrates that knowledge sharing significantly increases supply chain integration, and the study endorses the use of multiobjective mathematical programming for optimal decision making in scheduling. The results emphasize the value of collaborating with closely aligned companies to minimize knowledge-sharing costs and enhance broader organizational collaboration. Furthermore, the introduced model proposes practical execution scheduling and knowledge-sharing processes, as evidenced by a case study, leading to effective execution scheduling, reduced costs, improved communication, strengthened collaboration, and increased supply chain efficiency. Overall, this article contributes to research in supply chain management and knowledge-sharing models, enabling them to navigate constraints and market dynamics to improve supply chain performance through effective knowledge sharing and collaboration.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14669-14687"},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1109/TEM.2024.3459922
Hamza Muhammad Dawood;Chunguang Bai;Syed Imran Zaman;Matthew Quayson;Cristian Garcia
The value of Industry 4.0 technology in promoting sustainable development cannot be fully realized without considering the mutual influence between sustainable supply chain management (SSCM) and Industry 4.0. To our knowledge, the investment of Industry 4.0 technology and SSCM has not yet been studied from an integration perspective. This study aims to determine the enablers for integrating Industry 4.0 and SSCM and provide a theoretical framework and approach for evaluating those enablers. First, a human, technology, organization, and environment fit (HTOE-fit) theoretical framework is developed to identify and categorize 16 enablers. Second, Fuzzy-DEMATEL and Fuzzy-TOPSIS techniques are used to analyze the influence relationships between the enablers and then rank those enablers. The case of the textile industry in a developing economy has been investigated. Results showed that technology is the most essential aspect, and automation is the most important enabler in the textile industry. The theoretical implications are based on the HTOE-fit framework, which offers a novel approach for identifying critical enablers that are necessary for the successful integration of Industry 4.0 and SSCM, based on the above-mentioned four aspects. This study also identifies the mutual influence relationship among the enablers, which helps the textile companies in formulating investment and implementation paths for integrating Industry 4.0 and SSCM.
{"title":"Enabling the Integration of Industry 4.0 and Sustainable Supply Chain Management in the Textile Industry: A Framework and Evaluation Approach","authors":"Hamza Muhammad Dawood;Chunguang Bai;Syed Imran Zaman;Matthew Quayson;Cristian Garcia","doi":"10.1109/TEM.2024.3459922","DOIUrl":"https://doi.org/10.1109/TEM.2024.3459922","url":null,"abstract":"The value of Industry 4.0 technology in promoting sustainable development cannot be fully realized without considering the mutual influence between sustainable supply chain management (SSCM) and Industry 4.0. To our knowledge, the investment of Industry 4.0 technology and SSCM has not yet been studied from an integration perspective. This study aims to determine the enablers for integrating Industry 4.0 and SSCM and provide a theoretical framework and approach for evaluating those enablers. First, a human, technology, organization, and environment fit (HTOE-fit) theoretical framework is developed to identify and categorize 16 enablers. Second, Fuzzy-DEMATEL and Fuzzy-TOPSIS techniques are used to analyze the influence relationships between the enablers and then rank those enablers. The case of the textile industry in a developing economy has been investigated. Results showed that technology is the most essential aspect, and automation is the most important enabler in the textile industry. The theoretical implications are based on the HTOE-fit framework, which offers a novel approach for identifying critical enablers that are necessary for the successful integration of Industry 4.0 and SSCM, based on the above-mentioned four aspects. This study also identifies the mutual influence relationship among the enablers, which helps the textile companies in formulating investment and implementation paths for integrating Industry 4.0 and SSCM.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14704-14717"},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1109/TEM.2024.3459645
Ema Vasileska;Aleksandar Argilovski;Mite Tomov;Bojan Jovanoski;Valentina Gecevska
Metal additive manufacturing (AM), particularly laser powder bed fusion (LPBF), has emerged as a promising technology for rapidly producing intricate parts while minimizing material waste. However, the widespread adoption of AM has been hindered by the lack of adequate quality control measures. To address this challenge, a large number of machine learning (ML) applications have been proposed to improve the quality and productivity of AM processes. This study proposes the Lean concept as a guiding framework for classifying ML applications according to the Lean principles they support. Through a comprehensive review of literature studies, the research demonstrates the efficacy of this holistic approach, emphasizing ML's contributions to the Lean principles and the derived benefits to refine metal AM practices, improve efficiency, foster continuous improvement in LPBF, and finally bring value to the customer. The obtained results are particularly important for manufacturing engineers, quality control specialists, and decision-makers in the AM industry, as they provide actionable insights for enhancing process reliability, reducing waste, and achieving higher productivity.
金属增材制造（AM），尤其是激光粉末床熔融技术（LPBF），已成为一种既能快速生产复杂零件，又能最大限度减少材料浪费的前景广阔的技术。然而，由于缺乏适当的质量控制措施，AM 的广泛应用受到了阻碍。为了应对这一挑战，人们提出了大量机器学习（ML）应用，以提高 AM 流程的质量和生产率。本研究提出了精益概念，作为根据精益原则对 ML 应用进行分类的指导框架。通过对文献研究的全面回顾，研究证明了这一整体方法的有效性，强调了 ML 对精益原则的贡献，以及在完善金属 AM 实践、提高效率、促进 LPBF 的持续改进并最终为客户带来价值方面的益处。所获得的结果对制造工程师、质量控制专家和 AM 行业的决策者尤为重要，因为它们为提高工艺可靠性、减少浪费和实现更高的生产率提供了可行的见解。
{"title":"Implementation of Machine Learning for Enhancing Lean Manufacturing Practices for Metal Additive Manufacturing","authors":"Ema Vasileska;Aleksandar Argilovski;Mite Tomov;Bojan Jovanoski;Valentina Gecevska","doi":"10.1109/TEM.2024.3459645","DOIUrl":"https://doi.org/10.1109/TEM.2024.3459645","url":null,"abstract":"Metal additive manufacturing (AM), particularly laser powder bed fusion (LPBF), has emerged as a promising technology for rapidly producing intricate parts while minimizing material waste. However, the widespread adoption of AM has been hindered by the lack of adequate quality control measures. To address this challenge, a large number of machine learning (ML) applications have been proposed to improve the quality and productivity of AM processes. This study proposes the Lean concept as a guiding framework for classifying ML applications according to the Lean principles they support. Through a comprehensive review of literature studies, the research demonstrates the efficacy of this holistic approach, emphasizing ML's contributions to the Lean principles and the derived benefits to refine metal AM practices, improve efficiency, foster continuous improvement in LPBF, and finally bring value to the customer. The obtained results are particularly important for manufacturing engineers, quality control specialists, and decision-makers in the AM industry, as they provide actionable insights for enhancing process reliability, reducing waste, and achieving higher productivity.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14836-14845"},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1109/TEM.2024.3457874
Suqin Liao;Zaiyang Xie
Research investigating the underlying mechanisms and boundary conditions under which Big Data analytic capabilities (BDACs) influence business model innovation (BMI) in incumbents remains largely underdeveloped. Drawing on the dynamic capabilities view (DCV), we developed a moderated multimediation model in which unlearning success beliefs and unlearning failure beliefs were theorized as the different mechanisms underlining why incumbents are more likely to engage in BMI under the influence of BDACs. We further proposed that deep uncertainty is an important boundary condition that affects such a relationship. Multisource data from a multiwave survey was analyzed using structural equation modeling to test the theoretical framework. The results indicated that BDACs positively affect incumbents’ BMI through not only unlearning success beliefs but also unlearning failure beliefs. Furthermore, the results provided evidence for that deep uncertainty positively moderates the mediation of unlearning success beliefs. Notably, although the moderating effect of deep uncertainty on the mediation of unlearned failure beliefs is negative, it is insignificant. Our study contributes theoretically to the research on BDACs, organizational unlearning, BMI, and DCV, while practical implications are also discussed.
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