Pub Date : 2025-11-11DOI: 10.1109/TEM.2025.3631746
Xinxin Zhang;Xiuyi Zhang;Lipan Feng
Counterfeits have spread across nearly every product category, posing significant financial and reputational threats to brand companies. In response, companies are adopting various strategies to address this issue, including blockchain adoption and traditional anticounterfeiting. Specifically, anticounterfeiting aims to reduce or eliminate counterfeits from the market, increasing the likelihood that consumers will purchase the authentic product. In contrast, blockchain adoption allows consumers who engage with it to fully recognize the authentic product. In this article, we build analytical models to explore a brand company’s optimal strategies by considering a market where consumers are categorized into two groups based on their awareness of counterfeits: informed consumers and uninformed consumers. Our findings suggest that the brand company should implement anticounterfeiting when there are more uninformed consumers and the initial proportion of the authentic product is high. Otherwise, blockchain adoption is the better strategy. Furthermore, while both strategies effectively combat counterfeits, they should not be viewed solely as substitutes. When the initial proportion of the authentic product is low and the reputation loss is not significant, the company tends to earn more from anticounterfeiting when adopting blockchain compared to when blockchain is not used. In other words, the two strategies are complementary.
{"title":"Fight Against Counterfeits: Traditional Anticounterfeiting Versus Blockchain Adoption","authors":"Xinxin Zhang;Xiuyi Zhang;Lipan Feng","doi":"10.1109/TEM.2025.3631746","DOIUrl":"https://doi.org/10.1109/TEM.2025.3631746","url":null,"abstract":"Counterfeits have spread across nearly every product category, posing significant financial and reputational threats to brand companies. In response, companies are adopting various strategies to address this issue, including blockchain adoption and traditional anticounterfeiting. Specifically, anticounterfeiting aims to reduce or eliminate counterfeits from the market, increasing the likelihood that consumers will purchase the authentic product. In contrast, blockchain adoption allows consumers who engage with it to fully recognize the authentic product. In this article, we build analytical models to explore a brand company’s optimal strategies by considering a market where consumers are categorized into two groups based on their awareness of counterfeits: informed consumers and uninformed consumers. Our findings suggest that the brand company should implement anticounterfeiting when there are more uninformed consumers and the initial proportion of the authentic product is high. Otherwise, blockchain adoption is the better strategy. Furthermore, while both strategies effectively combat counterfeits, they should not be viewed solely as substitutes. When the initial proportion of the authentic product is low and the reputation loss is not significant, the company tends to earn more from anticounterfeiting when adopting blockchain compared to when blockchain is not used. In other words, the two strategies are complementary.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4320-4332"},"PeriodicalIF":5.2,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729335","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 : 2025-11-05DOI: 10.1109/TEM.2025.3629551
Yajiao Chen;Qinghua He;Xiaoyan Chen;Likai Zheng;Yifei Luo
Understanding collaboration strategies and dynamic patterns among organizations with unequal status in megaproject technological innovation networks represents a critical research issue, as it profoundly influences the innovation performance and ultimate success of megaprojects. However, existing research inadequately addresses the significant impact of network characteristics and organizational status disparities on megaproject innovations. To address this gap, this study develops a complex network evolutionary game-theoretic model and leverages Tien-yow Jeme Civil Engineering Prize data to simulate stability conditions and dynamic patterns of interorganizational collaboration. This article verifies the model’s equilibrium and stability conditions on random and scale-free networks and conducts sensitivity analyses of several key factors in an empirical network, further confirming their robustness. Findings indicate that rationalizing the benefit distribution system, reputation and punishment mechanisms can promote active collaboration within core organizations, while controlling spillover benefits helps to curb free-rider behavior in general organizations. Moreover, the appropriate configuration of these elements shall balance the fluctuations and inconsistencies between network-level indicators, not merely the organizational strategic transitions. This study offers project managers and scholars a novel theoretical lens for strategy evolution in collaborative networks and provides actionable insights for designing mechanisms that consider organizational heterogeneity to enhance innovation outcomes in megaprojects.
{"title":"Unveiling Megaproject Innovative Collaboration Through Game Theory: Evidence From China","authors":"Yajiao Chen;Qinghua He;Xiaoyan Chen;Likai Zheng;Yifei Luo","doi":"10.1109/TEM.2025.3629551","DOIUrl":"https://doi.org/10.1109/TEM.2025.3629551","url":null,"abstract":"Understanding collaboration strategies and dynamic patterns among organizations with unequal status in megaproject technological innovation networks represents a critical research issue, as it profoundly influences the innovation performance and ultimate success of megaprojects. However, existing research inadequately addresses the significant impact of network characteristics and organizational status disparities on megaproject innovations. To address this gap, this study develops a complex network evolutionary game-theoretic model and leverages Tien-yow Jeme Civil Engineering Prize data to simulate stability conditions and dynamic patterns of interorganizational collaboration. This article verifies the model’s equilibrium and stability conditions on random and scale-free networks and conducts sensitivity analyses of several key factors in an empirical network, further confirming their robustness. Findings indicate that rationalizing the benefit distribution system, reputation and punishment mechanisms can promote active collaboration within core organizations, while controlling spillover benefits helps to curb free-rider behavior in general organizations. Moreover, the appropriate configuration of these elements shall balance the fluctuations and inconsistencies between network-level indicators, not merely the organizational strategic transitions. This study offers project managers and scholars a novel theoretical lens for strategy evolution in collaborative networks and provides actionable insights for designing mechanisms that consider organizational heterogeneity to enhance innovation outcomes in megaprojects.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4294-4307"},"PeriodicalIF":5.2,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560626","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}
The integration of blockchain technology (BT) in supply chains (SCs) is advancing rapidly due to its benefits in transparency, traceability, security, and decentralization. However, SC managers face challenges in evaluating diverse strategies for effective BT adoption. This study proposes a comprehensive framework combining criteria weighting and evaluation models that prioritize simplicity, idealism, structure, and efficiency. It integrates five decision-making techniques; i.e., simple additive weighted (SAW), entropy method (EM), technique for order of preference by similarity to ideal solution (TOPSIS), super-efficiency data envelopment analysis (SEDEA), and network data envelopment analysis (NDEA), to provide a dynamic, adaptable evaluation process. In addition, a novel risk- and scenario-based optimization model and a predictive comparison approach are introduced to enhance decision-making. The framework is applied to the Norwegian Oil and Gas (O&G) sector, analyzing four BT adoption strategies. It incorporates machine learning (ML) for similarity analysis and dual-perspective clustering to better understand inter-strategy and method relationships. Findings show that a focused, single-purpose blockchain use, especially under high-risk conditions and transformational strategies, is most effective. Key adoption drivers include market and customer pressures, collaboration needs, and limited IT infrastructure. Simplicity and idealism/nonidealism-based models aligned most closely with existing literature, while network structure-based models varied more. The results demonstrate the importance of consensus-based evaluation when selecting blockchain strategies in SCs.
{"title":"An Efficiency-Uncertainty-Based Consensus Optimization Framework With Dual-Perspective Clustering Analysis for Blockchain Adoption in Supply Chains","authors":"Ardavan Babaei;Erfan Babaee Tirkolaee;Sadia Samar Ali;Sankar Kumar Roy;Gerhard-Wilhelm Weber","doi":"10.1109/TEM.2025.3626659","DOIUrl":"https://doi.org/10.1109/TEM.2025.3626659","url":null,"abstract":"The integration of blockchain technology (BT) in supply chains (SCs) is advancing rapidly due to its benefits in transparency, traceability, security, and decentralization. However, SC managers face challenges in evaluating diverse strategies for effective BT adoption. This study proposes a comprehensive framework combining criteria weighting and evaluation models that prioritize simplicity, idealism, structure, and efficiency. It integrates five decision-making techniques; i.e., simple additive weighted (SAW), entropy method (EM), technique for order of preference by similarity to ideal solution (TOPSIS), super-efficiency data envelopment analysis (SEDEA), and network data envelopment analysis (NDEA), to provide a dynamic, adaptable evaluation process. In addition, a novel risk- and scenario-based optimization model and a predictive comparison approach are introduced to enhance decision-making. The framework is applied to the Norwegian Oil and Gas (O&G) sector, analyzing four BT adoption strategies. It incorporates machine learning (ML) for similarity analysis and dual-perspective clustering to better understand inter-strategy and method relationships. Findings show that a focused, single-purpose blockchain use, especially under high-risk conditions and transformational strategies, is most effective. Key adoption drivers include market and customer pressures, collaboration needs, and limited IT infrastructure. Simplicity and idealism/nonidealism-based models aligned most closely with existing literature, while network structure-based models varied more. The results demonstrate the importance of consensus-based evaluation when selecting blockchain strategies in SCs.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4308-4319"},"PeriodicalIF":5.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612104","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 : 2025-10-27DOI: 10.1109/TEM.2025.3625725
Y. P. Tsang;C. K. H. Lee;C. H. Wu;Yanlin Li
Volatile demand and information delays in multitier supply chains give rise to the well-known bullwhip effect, yet the cognitive mechanisms that drive the inventory decisions behind this phenomenon remain poorly explored. To address this gap, a beer-game experiment couples game performance with neurophysiological evidence. Electroencephalogram (EEG) signals were collected while participants, who observed downstream orders, placed replenishment orders for 50 periods. A three-stage analytics process, incorporating topographical visualization, dynamic time warping, and hierarchical clustering, was applied to the EEG time-series data to uncover latent neural patterns. Comparing inconsistency values across linkage methods, Ward’s linkage performs best, with the lowest inconsistency (0.5785) and a cophenetic correlation coefficient of 0.7827. Furthermore, Silhouette analysis suggests an optimal solution of two clusters, with an average silhouette score of 0.65. Two cognitive profiles emerged: 1) hypoactive decision makers exhibiting lower cortical activation and 2) hyperactive decision makers with sustained high activation. Linking these profiles to operational outcomes shows that the hypoactive group generated 48.33% lower average cumulative cost and 66.59% lower standard deviation, indicating superior mitigation of the bullwhip effect. The results resonate with the Yerkes-Dodson law such that excessive activation may trigger overthinking and stress, degrading performance in uncertain environments, whereas moderate activation supports calmer and more consistent choices. By revealing how neuro-cognitive states shape operational effectiveness, this study contributes a novel measurement framework and offers actionable insights for designing decision-support tools and training programs in disruptive supply-chain contexts.
{"title":"Much Ado About Nothing? An EEG Study of the Beer Game for Neurophysiological Insights on Supply Chain Decision-Making","authors":"Y. P. Tsang;C. K. H. Lee;C. H. Wu;Yanlin Li","doi":"10.1109/TEM.2025.3625725","DOIUrl":"https://doi.org/10.1109/TEM.2025.3625725","url":null,"abstract":"Volatile demand and information delays in multitier supply chains give rise to the well-known bullwhip effect, yet the cognitive mechanisms that drive the inventory decisions behind this phenomenon remain poorly explored. To address this gap, a beer-game experiment couples game performance with neurophysiological evidence. Electroencephalogram (EEG) signals were collected while participants, who observed downstream orders, placed replenishment orders for 50 periods. A three-stage analytics process, incorporating topographical visualization, dynamic time warping, and hierarchical clustering, was applied to the EEG time-series data to uncover latent neural patterns. Comparing inconsistency values across linkage methods, Ward’s linkage performs best, with the lowest inconsistency (0.5785) and a cophenetic correlation coefficient of 0.7827. Furthermore, Silhouette analysis suggests an optimal solution of two clusters, with an average silhouette score of 0.65. Two cognitive profiles emerged: 1) hypoactive decision makers exhibiting lower cortical activation and 2) hyperactive decision makers with sustained high activation. Linking these profiles to operational outcomes shows that the hypoactive group generated 48.33% lower average cumulative cost and 66.59% lower standard deviation, indicating superior mitigation of the bullwhip effect. The results resonate with the Yerkes-Dodson law such that excessive activation may trigger overthinking and stress, degrading performance in uncertain environments, whereas moderate activation supports calmer and more consistent choices. By revealing how neuro-cognitive states shape operational effectiveness, this study contributes a novel measurement framework and offers actionable insights for designing decision-support tools and training programs in disruptive supply-chain contexts.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4250-4263"},"PeriodicalIF":5.2,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510078","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 : 2025-10-24DOI: 10.1109/TEM.2025.3625427
Jialan Shan;Zhaohua Deng;Zihao Deng;Dan Song
The rapid diffusion of artificial intelligence (AI) is accelerating the adoption of mental health chatbots (MHCs) as scalable tools for psychological support. Grounded in media naturalness theory (MNT) and computers are social actors paradigm, this study empirically investigates how MHCs’ humanlike competencies—cognitive, relational, and emotional—influence users’ self-esteem (termed AI-enabled self-esteem), through the mediating mechanism of emotional resonance. We conducted an online survey in Credamo and obtained 451 valid responses from experienced MHC users. Using a hybrid approach that combines structural equation modeling (SEM) and necessary condition analysis, we found that all three humanlike competencies positively predict emotional resonance and are identified as its necessary conditions. Emotional resonance, in turn, is positively associated with AI-enabled self-esteem. Notably, relational competency demonstrates the strongest effect on emotional resonance, challenging the common emphasis on emotional competency in human–chatbot interaction (HCI) design. Furthermore, the ordinary least squares regression corroborates the SEM results, and mediation analyses indicate a partial mediating role of emotional resonance, validating the robustness of our results. This study advocates for the design and adoption of MHCs with humanlike competencies. It also contributes to theoretical discourse on HCI literature and offers actionable guidance for MHC design and development.
{"title":"Emotional Resonance and Self-Esteem: The Role of Humanlike Competencies From Mental Health Chatbots","authors":"Jialan Shan;Zhaohua Deng;Zihao Deng;Dan Song","doi":"10.1109/TEM.2025.3625427","DOIUrl":"https://doi.org/10.1109/TEM.2025.3625427","url":null,"abstract":"The rapid diffusion of artificial intelligence (AI) is accelerating the adoption of mental health chatbots (MHCs) as scalable tools for psychological support. Grounded in media naturalness theory (MNT) and computers are social actors paradigm, this study empirically investigates how MHCs’ humanlike competencies—cognitive, relational, and emotional—influence users’ self-esteem (termed AI-enabled self-esteem), through the mediating mechanism of emotional resonance. We conducted an online survey in Credamo and obtained 451 valid responses from experienced MHC users. Using a hybrid approach that combines structural equation modeling (SEM) and necessary condition analysis, we found that all three humanlike competencies positively predict emotional resonance and are identified as its necessary conditions. Emotional resonance, in turn, is positively associated with AI-enabled self-esteem. Notably, relational competency demonstrates the strongest effect on emotional resonance, challenging the common emphasis on emotional competency in human–chatbot interaction (HCI) design. Furthermore, the ordinary least squares regression corroborates the SEM results, and mediation analyses indicate a partial mediating role of emotional resonance, validating the robustness of our results. This study advocates for the design and adoption of MHCs with humanlike competencies. It also contributes to theoretical discourse on HCI literature and offers actionable guidance for MHC design and development.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4281-4293"},"PeriodicalIF":5.2,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510080","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 : 2025-10-07DOI: 10.1109/TEM.2025.3618762
Han Guo;Lei Song;Feng Yang;Xiaolong Guo
Purchasing and supply chain management (PSCM) is a subfield of supply chain management and focuses on the purchasing process. Relevant studies emphasize the importance of logistics, commodity flow, and information flow in the above process and make in-depth research. However, there are few studies on PSCM capital flow management, especially in terms of systematic review of the literature. This article adopts a systematic literature review (SLR) approach that focuses on the role of a common complementary capital flow instrument, supply chain finance (SCF), in driving PSCM. First, based on the focal point of credit risk during SCF operations, this article systematically categorizes the development of PSCM driven by SCF into three patterns: seller-driven, buyer-driven, and third-party-driven. Second, in the above categorization patterns, this study focuses on the influence mechanisms of SCF on the four main decisions (pricing, quantity, quality, and timing). By clustering the main decisions under the three patterns, the results show that financing risks and costs under SCF operations change lenders’ decisions (e.g., lending rates, credit cycles, etc.), which in turn change borrowers’ decisions (e.g., price, quantity, etc.) in the purchasing process, and ultimately drive the development of PSCM. This review establishes SCF as an active driver of PSCM, revealing its dual role as a cost optimizer and strategic resource enabler, while reinterpreting capital flow as a proactive “fourth driver” in supply chains to dynamically coordinate procurement decisions. Finally, we summarize these studies and make recommendations for future research.
{"title":"Supply Chain Finance: A Strategic Enabler for Purchasing and Supply Chain Management","authors":"Han Guo;Lei Song;Feng Yang;Xiaolong Guo","doi":"10.1109/TEM.2025.3618762","DOIUrl":"https://doi.org/10.1109/TEM.2025.3618762","url":null,"abstract":"Purchasing and supply chain management (PSCM) is a subfield of supply chain management and focuses on the purchasing process. Relevant studies emphasize the importance of logistics, commodity flow, and information flow in the above process and make in-depth research. However, there are few studies on PSCM capital flow management, especially in terms of systematic review of the literature. This article adopts a systematic literature review (SLR) approach that focuses on the role of a common complementary capital flow instrument, supply chain finance (SCF), in driving PSCM. First, based on the focal point of credit risk during SCF operations, this article systematically categorizes the development of PSCM driven by SCF into three patterns: seller-driven, buyer-driven, and third-party-driven. Second, in the above categorization patterns, this study focuses on the influence mechanisms of SCF on the four main decisions (pricing, quantity, quality, and timing). By clustering the main decisions under the three patterns, the results show that financing risks and costs under SCF operations change lenders’ decisions (e.g., lending rates, credit cycles, etc.), which in turn change borrowers’ decisions (e.g., price, quantity, etc.) in the purchasing process, and ultimately drive the development of PSCM. This review establishes SCF as an active driver of PSCM, revealing its dual role as a cost optimizer and strategic resource enabler, while reinterpreting capital flow as a proactive “fourth driver” in supply chains to dynamically coordinate procurement decisions. Finally, we summarize these studies and make recommendations for future research.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4208-4224"},"PeriodicalIF":5.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405304","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 : 2025-10-02DOI: 10.1109/TEM.2025.3616029
Tian Liu;Bipan Zou;Linzi Zhang;Guohu Xu
Warehouses recently are increasingly using autonomous robots and compact structures to handle fluctuating demands in a cost-efficient way. While organizing items in a compact cube can save floor space cost, it sacrifices the operational efficiency due to reshuffling and robot detouring to get target items. This study investigates this trade-off in three typical robotic storage and retrieval systems (RS/RSs), including a compact structure that organizes stacks in a grid (RCS/RS), a disperse structure that establishes vertical aisles for robot traveling (VRS/RS), and a structure with an aisle between any two adjacent racks for robot traveling (AVS/RS). We first build an expected travel time model of retrieval transactions, and validate the accuracy of the model by simulation. Then, with the objective of minimizing the expected retrieval travel time of robots, we derive the optimal configuration for three systems. Finally, we construct a cost minimization model of the system with a required throughput capacity obtained by a closed queuing network. We derive the minimum system cost by determining optimal system configuration and the numbers of robots and workstations. The comparison results show that the AVS/RS is the most efficient in robot travel time. The VRS/RS needs the least investment to reach a required throughput capacity, and the AVS/RS is the most expensive. Moreover, when the budget for system configuration, robots and workstations is limited, the VRS/RS can provide the largest throughput capacity.
{"title":"Expected Retrieval Time Comparison Among Robotic Storage and Retrieval Systems With Various Structures","authors":"Tian Liu;Bipan Zou;Linzi Zhang;Guohu Xu","doi":"10.1109/TEM.2025.3616029","DOIUrl":"https://doi.org/10.1109/TEM.2025.3616029","url":null,"abstract":"Warehouses recently are increasingly using autonomous robots and compact structures to handle fluctuating demands in a cost-efficient way. While organizing items in a compact cube can save floor space cost, it sacrifices the operational efficiency due to reshuffling and robot detouring to get target items. This study investigates this trade-off in three typical robotic storage and retrieval systems (RS/RSs), including a compact structure that organizes stacks in a grid (RCS/RS), a disperse structure that establishes vertical aisles for robot traveling (VRS/RS), and a structure with an aisle between any two adjacent racks for robot traveling (AVS/RS). We first build an expected travel time model of retrieval transactions, and validate the accuracy of the model by simulation. Then, with the objective of minimizing the expected retrieval travel time of robots, we derive the optimal configuration for three systems. Finally, we construct a cost minimization model of the system with a required throughput capacity obtained by a closed queuing network. We derive the minimum system cost by determining optimal system configuration and the numbers of robots and workstations. The comparison results show that the AVS/RS is the most efficient in robot travel time. The VRS/RS needs the least investment to reach a required throughput capacity, and the AVS/RS is the most expensive. Moreover, when the budget for system configuration, robots and workstations is limited, the VRS/RS can provide the largest throughput capacity.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4184-4207"},"PeriodicalIF":5.2,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405269","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 : 2025-10-01DOI: 10.1109/TEM.2025.3613494
Xu Ouyang;Sai-Ho Chung
In the past five years, the logistics and service sectors struggled for disruptions in multiple dimensions, particularly since the COVID-19 pandemic. Lockdown, travel restriction, geopolitical tension, extreme weather, and the trade war imposed critical challenges to operations. The resulting disruptions impacted both the upstream (e.g., production and supply) and the downstream (e.g., final-mile delivery and customer service) throughout the industries negatively. It is thus meaningful to identify critical operational problems arising from these disruptions and to derive insights on how to hedge against their adverse impacts. This article provides a focused review of the latest literature on logistics and service operations under disruptions. We identify the primary sources of recent disruptions and classify their effects on operational uncertainties. Then, we demonstrate how classic operational problems (e.g., vehicle routing and inventory management) are developed to adapt to disruptions, as well as some emerging issues (e.g., network design under tariff uncertainty). According to application contexts, we review several representative methodologies (e.g., stochastic programming, robust optimization, game theory, and machine learning), which are widely applied in modeling and solving these problems. Last, we conclude this review by summarizing our findings and proposing a three-fold research agenda to inspire future studies on this vital topic.
{"title":"Logistics and Service Operations Under Disruptions: Recent Development Under the DT Taxonomy","authors":"Xu Ouyang;Sai-Ho Chung","doi":"10.1109/TEM.2025.3613494","DOIUrl":"https://doi.org/10.1109/TEM.2025.3613494","url":null,"abstract":"In the past five years, the logistics and service sectors struggled for disruptions in multiple dimensions, particularly since the COVID-19 pandemic. Lockdown, travel restriction, geopolitical tension, extreme weather, and the trade war imposed critical challenges to operations. The resulting disruptions impacted both the upstream (e.g., production and supply) and the downstream (e.g., final-mile delivery and customer service) throughout the industries negatively. It is thus meaningful to identify critical operational problems arising from these disruptions and to derive insights on how to hedge against their adverse impacts. This article provides a focused review of the latest literature on logistics and service operations under disruptions. We identify the primary sources of recent disruptions and classify their effects on operational uncertainties. Then, we demonstrate how classic operational problems (e.g., vehicle routing and inventory management) are developed to adapt to disruptions, as well as some emerging issues (e.g., network design under tariff uncertainty). According to application contexts, we review several representative methodologies (e.g., stochastic programming, robust optimization, game theory, and machine learning), which are widely applied in modeling and solving these problems. Last, we conclude this review by summarizing our findings and proposing a three-fold research agenda to inspire future studies on this vital topic.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4225-4236"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455834","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 : 2025-09-30DOI: 10.1109/TEM.2025.3616130
Cong Cheng;Jian Dai;Lulu Yan
Selecting the right merger and acquisition (M&A) target is a critical yet challenging endeavor, as the success of these strategic initiatives depends mainly on identifying compatible firms. This study draws upon the theoretical perspectives of strategic fit and organizational search to propose and validate a novel, two-stage M&A target recommendation approach designed as a managerial decision support system. Initially, the method facilitates a focused search in which acquirers define explicit criteria for identifying a highly relevant initial target within a knowledge graph (KG). It then employs a similarity-based search expansion using advanced KG embedding models to recommend additional targets that exhibit latent structural similarities. The efficacy of this approach is validated on a large-scale U.S. M&A dataset (2010–2022). Our key findings are threefold. First, our model demonstrates statistically significant superiority over benchmarks, confirmed through robustness checks, including tenfold cross-validation and temporal validation. Second, in an experiment on deals by experienced acquirers, our model is more effective at identifying these targets, quantitatively demonstrating its superior recommendation quality. Third, our analysis uncovers counterintuitive patterns, revealing that machine-identified structural similarities can be more potent predictors of fit than traditional human-centric filters, such as geography. It further explores the tool’s boundary conditions, showing that it is more effective in complex, high-tech sectors. This KG-based methodology offers a more informed, strategically refined, and empirically validated tool to enhance the quality of M&A decisions.
{"title":"A Knowledge Graph-Based Target Recommendation Approach for Mergers and Acquisitions","authors":"Cong Cheng;Jian Dai;Lulu Yan","doi":"10.1109/TEM.2025.3616130","DOIUrl":"https://doi.org/10.1109/TEM.2025.3616130","url":null,"abstract":"Selecting the right merger and acquisition (M&A) target is a critical yet challenging endeavor, as the success of these strategic initiatives depends mainly on identifying compatible firms. This study draws upon the theoretical perspectives of strategic fit and organizational search to propose and validate a novel, two-stage M&A target recommendation approach designed as a managerial decision support system. Initially, the method facilitates a focused search in which acquirers define explicit criteria for identifying a highly relevant initial target within a knowledge graph (KG). It then employs a similarity-based search expansion using advanced KG embedding models to recommend additional targets that exhibit latent structural similarities. The efficacy of this approach is validated on a large-scale U.S. M&A dataset (2010–2022). Our key findings are threefold. First, our model demonstrates statistically significant superiority over benchmarks, confirmed through robustness checks, including tenfold cross-validation and temporal validation. Second, in an experiment on deals by experienced acquirers, our model is more effective at identifying these targets, quantitatively demonstrating its superior recommendation quality. Third, our analysis uncovers counterintuitive patterns, revealing that machine-identified structural similarities can be more potent predictors of fit than traditional human-centric filters, such as geography. It further explores the tool’s boundary conditions, showing that it is more effective in complex, high-tech sectors. This KG-based methodology offers a more informed, strategically refined, and empirically validated tool to enhance the quality of M&A decisions.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4113-4126"},"PeriodicalIF":5.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255935","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 : 2025-09-29DOI: 10.1109/TEM.2025.3615542
Kerem Kayabay;Mert Onuralp Gökalp;Atilla Kılınç;Ebru Gökalp;Tugrul Daim
Many organizations struggle to keep their artificial intelligence (AI) systems aligned with operational data and computing resources in today’s volatile landscape. Data science roadmapping (DSR) embeds data layers into planning scenarios and enables a human-centric process. While DSR is effective for creating data science roadmaps, it lacks a clear implementation framework. This research advances DSR as a continuous AI alignment platform through three phases: 1) a multivocal literature review of academic and grey sources identifies gaps and tools; 2) synthesis of these findings adapts DSR for ongoing AI alignment; 3) a retrospective case study evaluates the adapted process. Initial results show the effectiveness of agile modifications to the DSR framework and the integration of a real-time platform for roadmap implementation and monitoring. Case study participants strongly supported a dedicated roadmapping operations team, especially to manage communication, detect AI deviations, and ensure compliance. This underscores how the operationalization of roadmapping can strengthen data and AI governance.
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