Pub Date : 2025-08-26DOI: 10.1109/TEM.2025.3603183
Chin-Yi Lin;Tzu-Liang Tseng;Honglun Xu
In today’s volatile geopolitical environment and heightened emphasis on sustainability, effective supplier selection must simultaneously handle cost, delivery risks, and environmental + social + governance (ESG) considerations. This article proposes a GPT-augmented Bayesian reinforcement learning (i-SUP) framework, which integrates 1) GPT to extract real-time risk signals from unstructured text (news, social media), 2) Bayesian- best–worst method to capture expert uncertainty and produce robust multicriteria weights, 3) Bayesian belief networks (BBNs) for continuously updated disruption probabilities, 4) reinforcement learning (RL) for dynamic monthly or weekly order allocation, and 5) NSGA-II for long-horizon multiobjective contract planning. By combining semantic risk detection with Bayesian updates and RL-based adaptive decision-making, i-SUP (intelligent supplier selection system) dynamically adjusts to emergent risks (e.g., tariffs, labor unrest), while concurrently balancing ESG imperatives and cost efficiency. Empirical validation in the semiconductor industry—characterized by tight geopolitical sensitivity and high ESG demands—shows that i-SUP significantly reduces disruptions and ESG incidents relative to static or cost-only methods. Moreover, ablation analyses confirm that removing any single module (GPT, BBN, RL, or NSGA-II) undermines performance, demonstrating the necessity of a fully integrated pipeline. The findings underscore i-SUP’s ability to enhance supplier resilience and sustainability in a wide range of globalized supply networks that face evolving textual risk signals and multidimensional objectives.
{"title":"GPT-Augmented Bayesian Reinforcement Learning Framework for Multiobjective Supplier Selection","authors":"Chin-Yi Lin;Tzu-Liang Tseng;Honglun Xu","doi":"10.1109/TEM.2025.3603183","DOIUrl":"https://doi.org/10.1109/TEM.2025.3603183","url":null,"abstract":"In today’s volatile geopolitical environment and heightened emphasis on sustainability, effective supplier selection must simultaneously handle cost, delivery risks, and environmental + social + governance (ESG) considerations. This article proposes a GPT-augmented Bayesian reinforcement learning (i-SUP) framework, which integrates 1) GPT to extract real-time risk signals from unstructured text (news, social media), 2) Bayesian- best–worst method to capture expert uncertainty and produce robust multicriteria weights, 3) Bayesian belief networks (BBNs) for continuously updated disruption probabilities, 4) reinforcement learning (RL) for dynamic monthly or weekly order allocation, and 5) NSGA-II for long-horizon multiobjective contract planning. By combining semantic risk detection with Bayesian updates and RL-based adaptive decision-making, i-SUP (intelligent supplier selection system) dynamically adjusts to emergent risks (e.g., tariffs, labor unrest), while concurrently balancing ESG imperatives and cost efficiency. Empirical validation in the semiconductor industry—characterized by tight geopolitical sensitivity and high ESG demands—shows that i-SUP significantly reduces disruptions and ESG incidents relative to static or cost-only methods. Moreover, ablation analyses confirm that removing any single module (GPT, BBN, RL, or NSGA-II) undermines performance, demonstrating the necessity of a fully integrated pipeline. The findings underscore i-SUP’s ability to enhance supplier resilience and sustainability in a wide range of globalized supply networks that face evolving textual risk signals and multidimensional objectives.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3779-3804"},"PeriodicalIF":5.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073199","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 : 2025-08-25DOI: 10.1109/TEM.2025.3602008
Mengqi Li;Dengfeng Li;Lixiao Wei
With the maturity of digital technology, manufacturing platforms that achieve the connection between productions and sales are becoming increasingly common. For a two-level manufacturing platform supply chain comprising a manufacturing platform, a manufacturer of the check-in platform, and retailers, this article constructs a noncooperative–cooperative biform game model to study the green R&D with technology spillovers and pricing in co-opetition situation. The co-opetition is reflected in price competition and green competition between the manufacturing platform and the manufacturer, and the revenue sharing between online and offline channels. The coupling mechanism between competition and cooperation is revealed. Cooperation stability is proved by convexity of the cooperative game and individual rationality of allocation values. By a numerical example, our results reveal that increasing online channel acceptance will decrease the manufacturing platform’s profit, which is surprising and counterintuitive. A unidirectional technology spillover will reduce the price, green R&D level, and profit of the enterprise. But moderate technology spillover of the manufacturer can improve social welfare. In order to accomplish a win-win situation for enterprises and society, the manufacturing platform can get low-level technology spillover of the manufacturer by green R&D cooperation. Our article provides theoretical guidance for the co-opetition in platform supply chain.
{"title":"Manufacturing Platform’s Pricing and Green R&D With Technology Spillovers Under Supply Chain Co-opetition","authors":"Mengqi Li;Dengfeng Li;Lixiao Wei","doi":"10.1109/TEM.2025.3602008","DOIUrl":"https://doi.org/10.1109/TEM.2025.3602008","url":null,"abstract":"With the maturity of digital technology, manufacturing platforms that achieve the connection between productions and sales are becoming increasingly common. For a two-level manufacturing platform supply chain comprising a manufacturing platform, a manufacturer of the check-in platform, and retailers, this article constructs a noncooperative–cooperative biform game model to study the green R&D with technology spillovers and pricing in co-opetition situation. The co-opetition is reflected in price competition and green competition between the manufacturing platform and the manufacturer, and the revenue sharing between online and offline channels. The coupling mechanism between competition and cooperation is revealed. Cooperation stability is proved by convexity of the cooperative game and individual rationality of allocation values. By a numerical example, our results reveal that increasing online channel acceptance will decrease the manufacturing platform’s profit, which is surprising and counterintuitive. A unidirectional technology spillover will reduce the price, green R&D level, and profit of the enterprise. But moderate technology spillover of the manufacturer can improve social welfare. In order to accomplish a win-win situation for enterprises and society, the manufacturing platform can get low-level technology spillover of the manufacturer by green R&D cooperation. Our article provides theoretical guidance for the co-opetition in platform supply chain.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3849-3863"},"PeriodicalIF":5.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078656","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}
Drawing on institutional theory, this study disentangles the intricate relationships between environmental laws, corporate environmental ethics (CEE), green process innovation (GPI), and environmental performance. Using survey data from manufacturing firms in Pakistan, the study finds that environmental laws have a positive influence on GPI, and this association is mediated by CEE. Furthermore, the effects of environmental laws on environmental performance are sequentially mediated by CEE and GPI. In addition, institutional support plays a positive moderating role in enhancing the effects of environmental laws on CEE, as well as the effects of CEE on GPI. Theoretical, practical, and policy-related contributions are offered.
{"title":"Do Environmental Laws Matter for Corporate Ethics and Green Process Innovation in Environment Performance? The Moderating Role of Institutional Support","authors":"Huda Khan;Joseph Amankwah-Amoah;Benjamin Laker;Richard Lee;Deepak Sardana","doi":"10.1109/TEM.2025.3597927","DOIUrl":"https://doi.org/10.1109/TEM.2025.3597927","url":null,"abstract":"Drawing on institutional theory, this study disentangles the intricate relationships between environmental laws, corporate environmental ethics (CEE), green process innovation (GPI), and environmental performance. Using survey data from manufacturing firms in Pakistan, the study finds that environmental laws have a positive influence on GPI, and this association is mediated by CEE. Furthermore, the effects of environmental laws on environmental performance are sequentially mediated by CEE and GPI. In addition, institutional support plays a positive moderating role in enhancing the effects of environmental laws on CEE, as well as the effects of CEE on GPI. Theoretical, practical, and policy-related contributions are offered.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3678-3687"},"PeriodicalIF":5.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990183","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 : 2025-08-19DOI: 10.1109/TEM.2025.3600381
Milad Rahmati
The rapid expansion of electric vehicle (EV) charging infrastructure brings with it an increasing reliance on software systems for managing control logic, communication protocols, and real-time decision-making. As these systems grow more complex and interconnected, ensuring their operational reliability becomes essential—not only for individual charging stations but for maintaining broader energy grid stability and safety. This study introduces a new framework that models software reliability within EV charging systems, combining probabilistic techniques and explainable artificial intelligence (XAI) to improve failure prediction and monitoring transparency. By employing Bayesian reliability analysis and dynamic runtime observation, the proposed method identifies latent software vulnerabilities and offers interpretable diagnostic feedback, even under uncertain operating conditions. Unlike prior work focused primarily on hardware resilience or energy optimization, our research emphasizes control software robustness and the visibility of system behavior during operation. To validate the framework, we simulate an EV charging network featuring real-time data flows and multiple failure scenarios. Results show that our model enhances system stability, extends the average time between software failures, and facilitates faster issue diagnosis—all without compromising explainability. This contribution supports ongoing national efforts in clean energy transition, infrastructure modernization, and cyber-physical system safety by offering a scalable, modular, and intelligible approach to software reliability assurance in EV environments.
{"title":"Explainable Reliability Modeling and Runtime Monitoring of Software Systems in Electric Vehicle Charging Infrastructure","authors":"Milad Rahmati","doi":"10.1109/TEM.2025.3600381","DOIUrl":"https://doi.org/10.1109/TEM.2025.3600381","url":null,"abstract":"The rapid expansion of electric vehicle (EV) charging infrastructure brings with it an increasing reliance on software systems for managing control logic, communication protocols, and real-time decision-making. As these systems grow more complex and interconnected, ensuring their operational reliability becomes essential—not only for individual charging stations but for maintaining broader energy grid stability and safety. This study introduces a new framework that models software reliability within EV charging systems, combining probabilistic techniques and explainable artificial intelligence (XAI) to improve failure prediction and monitoring transparency. By employing Bayesian reliability analysis and dynamic runtime observation, the proposed method identifies latent software vulnerabilities and offers interpretable diagnostic feedback, even under uncertain operating conditions. Unlike prior work focused primarily on hardware resilience or energy optimization, our research emphasizes control software robustness and the visibility of system behavior during operation. To validate the framework, we simulate an EV charging network featuring real-time data flows and multiple failure scenarios. Results show that our model enhances system stability, extends the average time between software failures, and facilitates faster issue diagnosis—all without compromising explainability. This contribution supports ongoing national efforts in clean energy transition, infrastructure modernization, and cyber-physical system safety by offering a scalable, modular, and intelligible approach to software reliability assurance in EV environments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3667-3677"},"PeriodicalIF":5.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934404","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 : 2025-08-19DOI: 10.1109/TEM.2025.3600490
Yunbing Li;Jie Wu;Yong Zha
Excessive traffic consumption creates anxiety about traffic costs and encourages the popularity of data sponsorship, a business model in which internet service providers (ISPs) encourage content providers (CPs) to subsidize consumers’ mobile traffic costs. In practice, content with data sponsorship may be output at higher or lower resolution. We propose a game-theoretic model in which three cooperation options exist between the ISP and CP: Case N (no data subsidization is allowed), Case L (allowing the CP to subsidize low-resolution content), and Case H (allowing the CP to subsidize high-resolution content). We find that the ISP chooses Case H when the ad-revenue rate and degree of increased viewing cost for low-resolution content compared with high-resolution content (DIC) and degree of increased traffic for high-resolution content compared with low-resolution content (DIT) are high. However, the ISP chooses Case L when DIC and DIT are low and Case N when the ad-revenue rate is low. The CP offers full subsidization to cover consumers’ traffic costs under Case L but only partially subsidizes data under Case H. In addition, the Pareto zone shows that a large ad-revenue rate and a low DIC allow Case L to benefit both the ISP and CP, but a large DIC can let Case H benefit both parties, which sheds light on the motivation behind ISP–CP cooperation from a new perspective. We further identify the conditions under which consumer surplus and social welfare can benefit from a data plan.
{"title":"Sponsored Data: A Game-Theoretic Model With Content Provider Content Quality Differentiation","authors":"Yunbing Li;Jie Wu;Yong Zha","doi":"10.1109/TEM.2025.3600490","DOIUrl":"https://doi.org/10.1109/TEM.2025.3600490","url":null,"abstract":"Excessive traffic consumption creates anxiety about traffic costs and encourages the popularity of data sponsorship, a business model in which internet service providers (ISPs) encourage content providers (CPs) to subsidize consumers’ mobile traffic costs. In practice, content with data sponsorship may be output at higher or lower resolution. We propose a game-theoretic model in which three cooperation options exist between the ISP and CP: Case N (no data subsidization is allowed), Case L (allowing the CP to subsidize low-resolution content), and Case H (allowing the CP to subsidize high-resolution content). We find that the ISP chooses Case H when the ad-revenue rate and degree of increased viewing cost for low-resolution content compared with high-resolution content (DIC) and degree of increased traffic for high-resolution content compared with low-resolution content (DIT) are high. However, the ISP chooses Case L when DIC and DIT are low and Case N when the ad-revenue rate is low. The CP offers full subsidization to cover consumers’ traffic costs under Case L but only partially subsidizes data under Case H. In addition, the Pareto zone shows that a large ad-revenue rate and a low DIC allow Case L to benefit both the ISP and CP, but a large DIC can let Case H benefit both parties, which sheds light on the motivation behind ISP–CP cooperation from a new perspective. We further identify the conditions under which consumer surplus and social welfare can benefit from a data plan.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3805-3816"},"PeriodicalIF":5.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073200","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 : 2025-08-19DOI: 10.1109/TEM.2025.3598853
Xiang Li;Yue Wang;Daniel Y. Mo
Mass customization has emerged as a viable smart manufacturing strategy to deliver tailor-made products with the efficiency of mass production. It significantly impacts a company’s research, development, and engineering functions by fostering innovation in product design, manufacturing processes, and supply chain management. A critical challenge in mass customization is developing a user-friendly choice navigation process that enables customers to identify customized designs with minimal burden and complexity. This article addresses this challenge by proposing a novel approach to choice navigation that maps customer needs expressed in natural language to suitable product attribute choices. We tackle data sparsity issues by leveraging the extensive amount of online product-review text to mine customer needs and preferences. External domain knowledge in the product domain is distilled using conceptual graphs. We then develop a convolutional neural network-based structure and a transfer learning procedure to integrate this domain knowledge with contextual semantic information from the review and needs text. Our extensive experiments show that the approach’s effectiveness and robustness in the needs-attributes mapping, and demonstrate its potential to improve user-friendliness and customer satisfaction in mass customization systems.
{"title":"A Domain Knowledge Integrated Convolutional Neural Network for Translating Customer Needs Into Configuration Choices in Mass Customization","authors":"Xiang Li;Yue Wang;Daniel Y. Mo","doi":"10.1109/TEM.2025.3598853","DOIUrl":"https://doi.org/10.1109/TEM.2025.3598853","url":null,"abstract":"Mass customization has emerged as a viable smart manufacturing strategy to deliver tailor-made products with the efficiency of mass production. It significantly impacts a company’s research, development, and engineering functions by fostering innovation in product design, manufacturing processes, and supply chain management. A critical challenge in mass customization is developing a user-friendly choice navigation process that enables customers to identify customized designs with minimal burden and complexity. This article addresses this challenge by proposing a novel approach to choice navigation that maps customer needs expressed in natural language to suitable product attribute choices. We tackle data sparsity issues by leveraging the extensive amount of online product-review text to mine customer needs and preferences. External domain knowledge in the product domain is distilled using conceptual graphs. We then develop a convolutional neural network-based structure and a transfer learning procedure to integrate this domain knowledge with contextual semantic information from the review and needs text. Our extensive experiments show that the approach’s effectiveness and robustness in the needs-attributes mapping, and demonstrate its potential to improve user-friendliness and customer satisfaction in mass customization systems.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3567-3583"},"PeriodicalIF":5.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914105","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 : 2025-08-19DOI: 10.1109/TEM.2025.3587594
Abdul Hameed Pitafi;Nazrul Islam;Sarah Basahel;Rekha Attri;Abhishek Bhushan Singhal
Organizations increasingly use social media platforms to improve internal communication, content creation, and knowledge sharing among employees. This study seeks to explore whether task characteristics (complexity and interdependence) influence the relationship between the usage of enterprise social media (ESM) and employee agility. The regulatory focus theory is used to explain the influence of employees’ promotion and prevention focus on the relationship between the usage of ESM platforms and task characteristics, as well as employee agility. All assumptions were tested using 318 cases from Chinese companies using the PROCESS Macro tool. Both task complexity and task interdependence mediate the relationship of ESM platforms and employee agility. Promotion focus moderates the relationship of ESM platforms and task characteristics and the indirect connection of ESM platforms and employee agility through task characteristics, but prevention focus weakens these relationships.
{"title":"Enterprise Social Media and Employee Agility: The Role of Task Context and Personal Motivation","authors":"Abdul Hameed Pitafi;Nazrul Islam;Sarah Basahel;Rekha Attri;Abhishek Bhushan Singhal","doi":"10.1109/TEM.2025.3587594","DOIUrl":"https://doi.org/10.1109/TEM.2025.3587594","url":null,"abstract":"Organizations increasingly use social media platforms to improve internal communication, content creation, and knowledge sharing among employees. This study seeks to explore whether task characteristics (complexity and interdependence) influence the relationship between the usage of enterprise social media (ESM) and employee agility. The regulatory focus theory is used to explain the influence of employees’ promotion and prevention focus on the relationship between the usage of ESM platforms and task characteristics, as well as employee agility. All assumptions were tested using 318 cases from Chinese companies using the PROCESS Macro tool. Both task complexity and task interdependence mediate the relationship of ESM platforms and employee agility. Promotion focus moderates the relationship of ESM platforms and task characteristics and the indirect connection of ESM platforms and employee agility through task characteristics, but prevention focus weakens these relationships.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3305-3317"},"PeriodicalIF":5.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887764","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 : 2025-08-18DOI: 10.1109/TEM.2025.3599711
Dmitry Ivanov
This article is induced by novel decision-making settings entailed in supply chains in the wake of the global tariff crisis in spring 2025. Their context and scope differ from traditional risks and resilience analysis. In situations such as a global tariff crisis, economic shocks can propagate through supply networks, leading to ripple and bullwhip effects. In this article, we discuss methods and models for supply chain stress testing for tariff shocks and trade conflicts. We begin with an analysis of the short-term and long-term impacts of tariff shocks on supply chains, supply chain responses to tariff crises, and their consequences for economics. We show that tariff conflicts is a unique type of systemic shock characterized by a combination of immediate and delayed effects, cross-industry ripple effects, and mutual interrelations of supply chain and economics decisions. Most importantly, this setting incurs a reciprocal influence of operations and economics, which has never been examined in supply chain stress tests before. This type of shock has been underexplored and can motivate new and substantial contributions to supply chain resilience, the ripple effect, and viability.
{"title":"Supply Chain Stress Testing for Tariff Shocks and Trade Conflicts: Methods, Models, and Reciprocal Influence of Operations and Economics","authors":"Dmitry Ivanov","doi":"10.1109/TEM.2025.3599711","DOIUrl":"https://doi.org/10.1109/TEM.2025.3599711","url":null,"abstract":"This article is induced by novel decision-making settings entailed in supply chains in the wake of the global tariff crisis in spring 2025. Their context and scope differ from traditional risks and resilience analysis. In situations such as a global tariff crisis, economic shocks can propagate through supply networks, leading to ripple and bullwhip effects. In this article, we discuss methods and models for supply chain stress testing for tariff shocks and trade conflicts. We begin with an analysis of the short-term and long-term impacts of tariff shocks on supply chains, supply chain responses to tariff crises, and their consequences for economics. We show that tariff conflicts is a unique type of systemic shock characterized by a combination of immediate and delayed effects, cross-industry ripple effects, and mutual interrelations of supply chain and economics decisions. Most importantly, this setting incurs a reciprocal influence of operations and economics, which has never been examined in supply chain stress tests before. This type of shock has been underexplored and can motivate new and substantial contributions to supply chain resilience, the ripple effect, and viability.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3559-3566"},"PeriodicalIF":5.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914106","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 : 2025-08-15DOI: 10.1109/TEM.2025.3599638
Gengchen Wang;Min Huang;Sandun C. Perera;Songchen Jiang;Shu-Cherng Fang
Effective inventory management in manufacturing systems is vital for enhancing production efficiency and reducing costs, as multisourcing uncertainties pose a significant challenge. This study addresses the often-overlooked issue of production uncertainty by extending the classic newsvendor model to integrate uncertainties in production, supply, and demand within a multiperiod framework. A novel multiperiod newsvendor model is developed to determine the optimal order quantity, minimizing total costs, including ordering, production, holding, and shortage costs. Given the lack of distributional knowledge for uncertain parameters, we adopt a robust optimization approach, constructing two distinct uncertainty sets: 1) box and ellipsoidal and 2) budget-based, to model the uncertainties in supply, production, and demand. The model is reformulated into a tractable second-order cone programming problem. Computational experiments demonstrate the effectiveness and robustness of the model, showing strong resilience to parameter variations and price fluctuations. Managerial insights drawn from numerical experiments highlight the strategic advantage of leveraging early-stage supply to build inventory buffers in multiperiod, multisource uncertainty scenarios. The findings emphasize prioritized raw material acquisition in initial periods to counter cumulative risks, coupled with responsive order adjustments guided by real-time demand–production fluctuations and critical evaluations of supplier reliability. These findings underscore the practical applicability of the model in addressing real-world challenges within complex and uncertain manufacturing environments.
{"title":"Integrating Supply, Production, and Demand Uncertainties in Manufacturing Inventory Systems","authors":"Gengchen Wang;Min Huang;Sandun C. Perera;Songchen Jiang;Shu-Cherng Fang","doi":"10.1109/TEM.2025.3599638","DOIUrl":"https://doi.org/10.1109/TEM.2025.3599638","url":null,"abstract":"Effective inventory management in manufacturing systems is vital for enhancing production efficiency and reducing costs, as multisourcing uncertainties pose a significant challenge. This study addresses the often-overlooked issue of production uncertainty by extending the classic newsvendor model to integrate uncertainties in production, supply, and demand within a multiperiod framework. A novel multiperiod newsvendor model is developed to determine the optimal order quantity, minimizing total costs, including ordering, production, holding, and shortage costs. Given the lack of distributional knowledge for uncertain parameters, we adopt a robust optimization approach, constructing two distinct uncertainty sets: 1) box and ellipsoidal and 2) budget-based, to model the uncertainties in supply, production, and demand. The model is reformulated into a tractable second-order cone programming problem. Computational experiments demonstrate the effectiveness and robustness of the model, showing strong resilience to parameter variations and price fluctuations. Managerial insights drawn from numerical experiments highlight the strategic advantage of leveraging early-stage supply to build inventory buffers in multiperiod, multisource uncertainty scenarios. The findings emphasize prioritized raw material acquisition in initial periods to counter cumulative risks, coupled with responsive order adjustments guided by real-time demand–production fluctuations and critical evaluations of supplier reliability. These findings underscore the practical applicability of the model in addressing real-world challenges within complex and uncertain manufacturing environments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3749-3764"},"PeriodicalIF":5.2,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036882","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 : 2025-08-14DOI: 10.1109/TEM.2025.3599233
Samuel Shuai Liu;Benedict Jun Ma;Edwin Cheng;Ilya Jackson
Blockchain technology has been increasingly adopted in supply chains, offering new avenues for enhancing information transparency. This article investigates the role of blockchain in reducing consumer uncertainty, as well as its impact on competition between online and offline retailers under different market structures, using a game-theoretical approach. It highlights how blockchain enhances transparency and consumer trust, thereby influencing market dynamics and pricing strategies. The model incorporates consumer heterogeneity in terms of blockchain awareness and perceived product value, examining how these factors affect blockchain adoption decisions. A key finding reveals that blockchain can provide competitive advantages to online retailers, especially when consumer acceptance of the online channel is relatively low. Interestingly, if the online retailer adopts blockchain, higher mismatch costs could unexpectedly hurt profits. Moreover, our analysis shows that blockchain adoption does not necessarily benefit all retailers. Its effectiveness depends on consumer trust levels, awareness, and the magnitude of mismatch risks. Overall, this article offers managerial insights for retailers to tailor blockchain strategies based on market conditions, emphasizing the importance of consumer uncertainty and aligning blockchain’s benefits with specific market roles.
{"title":"Blockchain Adoption in Competing Retail Channels","authors":"Samuel Shuai Liu;Benedict Jun Ma;Edwin Cheng;Ilya Jackson","doi":"10.1109/TEM.2025.3599233","DOIUrl":"https://doi.org/10.1109/TEM.2025.3599233","url":null,"abstract":"Blockchain technology has been increasingly adopted in supply chains, offering new avenues for enhancing information transparency. This article investigates the role of blockchain in reducing consumer uncertainty, as well as its impact on competition between online and offline retailers under different market structures, using a game-theoretical approach. It highlights how blockchain enhances transparency and consumer trust, thereby influencing market dynamics and pricing strategies. The model incorporates consumer heterogeneity in terms of blockchain awareness and perceived product value, examining how these factors affect blockchain adoption decisions. A key finding reveals that blockchain can provide competitive advantages to online retailers, especially when consumer acceptance of the online channel is relatively low. Interestingly, if the online retailer adopts blockchain, higher mismatch costs could unexpectedly hurt profits. Moreover, our analysis shows that blockchain adoption does not necessarily benefit all retailers. Its effectiveness depends on consumer trust levels, awareness, and the magnitude of mismatch risks. Overall, this article offers managerial insights for retailers to tailor blockchain strategies based on market conditions, emphasizing the importance of consumer uncertainty and aligning blockchain’s benefits with specific market roles.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3735-3748"},"PeriodicalIF":5.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036881","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}
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