Network nestedness, which refers to the hierarchical structure of interconnections within a network, plays an important role in supply chain resilience but remains understudied. We use data from listed firms in China between 2002 and 2022 to construct buyer-supplier networks and measure nestedness using the SNODF metric. We validate SNODF's robustness across varying levels of network completeness. Listed firms are centrally positioned in our networks, making them crucial focal points for analysis. Our empirical results show that network nestedness has a dual effect on supply chain resilience: it weakens short-term resistance to disruptions but enhances long-term recovery. This trade-off arises because hierarchical structures concentrate vulnerability at hub nodes while enabling coordinated resource reallocation after a disruption. We examine two managerial levers that moderate these effects: (1) supplier concentration, an external strategy that attenuates the negative effect on resistance but dampens recovery gains; and (2) corporate digitalization, an internal strategy that mitigates initial losses and enhances recovery. These findings imply that firms should balance two approaches: (1) mitigating risk through supplier diversification to reduce dependence on dominant hubs, and (2) leveraging digital technologies to improve recovery capabilities, thus strengthening long-term resilience.
{"title":"Impact of network nestedness on resistance and recovery of supply chain resilience","authors":"Sihang Chen , Junqin Lin , Xiaopo Zhuo , Libo Yin , Jiaxin Shen","doi":"10.1016/j.ijpe.2025.109854","DOIUrl":"10.1016/j.ijpe.2025.109854","url":null,"abstract":"<div><div>Network nestedness, which refers to the hierarchical structure of interconnections within a network, plays an important role in supply chain resilience but remains understudied. We use data from listed firms in China between 2002 and 2022 to construct buyer-supplier networks and measure nestedness using the SNODF metric. We validate SNODF's robustness across varying levels of network completeness. Listed firms are centrally positioned in our networks, making them crucial focal points for analysis. Our empirical results show that network nestedness has a dual effect on supply chain resilience: it weakens short-term resistance to disruptions but enhances long-term recovery. This trade-off arises because hierarchical structures concentrate vulnerability at hub nodes while enabling coordinated resource reallocation after a disruption. We examine two managerial levers that moderate these effects: (1) supplier concentration, an external strategy that attenuates the negative effect on resistance but dampens recovery gains; and (2) corporate digitalization, an internal strategy that mitigates initial losses and enhances recovery. These findings imply that firms should balance two approaches: (1) mitigating risk through supplier diversification to reduce dependence on dominant hubs, and (2) leveraging digital technologies to improve recovery capabilities, thus strengthening long-term resilience.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"291 ","pages":"Article 109854"},"PeriodicalIF":10.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-11-19DOI: 10.1016/j.ijpe.2025.109862
Mohammed Merghem , Mohammed Haoues , Ahmed Senoussi , Mohammed Dahane , Nadia Kinza Mouss
This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.
{"title":"Integrated production and maintenance planning in imperfect hybrid manufacturing–remanufacturing systems with outsourcing and carbon emissions","authors":"Mohammed Merghem , Mohammed Haoues , Ahmed Senoussi , Mohammed Dahane , Nadia Kinza Mouss","doi":"10.1016/j.ijpe.2025.109862","DOIUrl":"10.1016/j.ijpe.2025.109862","url":null,"abstract":"<div><div>This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"291 ","pages":"Article 109862"},"PeriodicalIF":10.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Last-mile delivery, the final leg of the supply chain to consumers, significantly impacts the environment, particularly in the grocery sector, due to the perishable nature of food items often necessitating expedited delivery methods. The role of consumer behavior in this process has been overlooked, and their preferences in a trade-off between the benefits of grocery home delivery and the environment have yet to be clarified. This paper bridges this gap by integrating behavioral logistics with key drivers of green consumer behavior to optimize grocery delivery, leveraging primary consumer data to enhance efficiency and minimize environmental impact. Using a two-part methodology, the research combines a controlled experiment and an optimization model. By partnering with an Italian supermarket, this research examines the impact of financial incentives, green nudges, and peer collaboration on consumer grocery delivery choices and routing optimization. The findings reveal that moral green nudges outweigh financial incentives and amplify the effect, and peer influence drives the adoption of shared and more sustainable delivery systems. The optimization model quantifies these behavioral insights, demonstrating cost reductions of up to 42.5% through flexible delivery scheduling and 76.8% via a shared cart mechanism. The findings provide valuable insights for practitioners and policymakers willing to intervene in the crossing of consumer choices and grocery delivery efficiency by presenting an innovative and ready-to-implement solution, which we denote as “the shared cart”. In showing the importance of social pressure for collaborative pro-environmental behavior, the shared cart practically embodies the potential for safeguarding the environment, while enabling cost-saving for last-mile delivery in the grocery sector.
{"title":"Nudging the last mile: Combining behavioral insights and monetary incentives for sustainable delivery choices","authors":"Eleonora Rizzitello , Giovanna Lo Nigro , Simona Mancini , Margaretha Gansterer","doi":"10.1016/j.ijpe.2025.109855","DOIUrl":"10.1016/j.ijpe.2025.109855","url":null,"abstract":"<div><div>Last-mile delivery, the final leg of the supply chain to consumers, significantly impacts the environment, particularly in the grocery sector, due to the perishable nature of food items often necessitating expedited delivery methods. The role of consumer behavior in this process has been overlooked, and their preferences in a trade-off between the benefits of grocery home delivery and the environment have yet to be clarified. This paper bridges this gap by integrating behavioral logistics with key drivers of green consumer behavior to optimize grocery delivery, leveraging primary consumer data to enhance efficiency and minimize environmental impact. Using a two-part methodology, the research combines a controlled experiment and an optimization model. By partnering with an Italian supermarket, this research examines the impact of financial incentives, green nudges, and peer collaboration on consumer grocery delivery choices and routing optimization. The findings reveal that moral green nudges outweigh financial incentives and amplify the effect, and peer influence drives the adoption of shared and more sustainable delivery systems. The optimization model quantifies these behavioral insights, demonstrating cost reductions of up to 42.5% through flexible delivery scheduling and 76.8% via a shared cart mechanism. The findings provide valuable insights for practitioners and policymakers willing to intervene in the crossing of consumer choices and grocery delivery efficiency by presenting an innovative and ready-to-implement solution, which we denote as “the shared cart”. In showing the importance of social pressure for collaborative pro-environmental behavior, the shared cart practically embodies the potential for safeguarding the environment, while enabling cost-saving for last-mile delivery in the grocery sector.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"291 ","pages":"Article 109855"},"PeriodicalIF":10.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-11-04DOI: 10.1016/j.ijpe.2025.109841
Stefano Genetti, Giorgio Scarton, Marco Formentini, Giovanni Iacca
In the context of Industry 4.0, several technologies converge to orchestrate improvements in business performance. Among these, Artificial Intelligence and Digital Twins stand out as some of the most promising. These two technologies are connected through the concept of intelligent Digital Twins (iDTs), which enhance standard Digital Twins with intelligent capabilities while keeping humans at the core of the process. One of the main obstacles to the broad adoption of iDTs in operations and supply chain management is the reliance on opaque AI models, which often limit trust and acceptability among operations experts and managers. To address this, it is critical to design iDTs that not only leverage the advanced capabilities of AI but also provide interpretable and actionable insights to stakeholders. In this paper, we present an action research in Adige Spa to develop an iDT framework for production scheduling. Our framework integrates interpretable machine learning techniques, employing evolutionary learning to produce decision trees that are transparent by design. Additionally, we incorporate Large Language Models to explain decision tree policies in natural language, enhancing user understanding. The framework also facilitates human interaction, allowing users to express preferences and guide the tree learning process. Results in a hybrid flow shop setting demonstrate that the proposed iDT framework delivers interpretable and effective decision-support policies while empowering users to influence and refine its outcomes, hence bridging the gap between AI-driven insights and real-world applicability.
{"title":"An intelligent Digital Twin based on machine learning for interpretable decision-making in manufacturing","authors":"Stefano Genetti, Giorgio Scarton, Marco Formentini, Giovanni Iacca","doi":"10.1016/j.ijpe.2025.109841","DOIUrl":"10.1016/j.ijpe.2025.109841","url":null,"abstract":"<div><div>In the context of Industry 4.0, several technologies converge to orchestrate improvements in business performance. Among these, Artificial Intelligence and Digital Twins stand out as some of the most promising. These two technologies are connected through the concept of intelligent Digital Twins (iDTs), which enhance standard Digital Twins with intelligent capabilities while keeping humans at the core of the process. One of the main obstacles to the broad adoption of iDTs in operations and supply chain management is the reliance on opaque AI models, which often limit trust and acceptability among operations experts and managers. To address this, it is critical to design iDTs that not only leverage the advanced capabilities of AI but also provide interpretable and actionable insights to stakeholders. In this paper, we present an action research in Adige Spa to develop an iDT framework for production scheduling. Our framework integrates interpretable machine learning techniques, employing evolutionary learning to produce decision trees that are transparent by design. Additionally, we incorporate Large Language Models to explain decision tree policies in natural language, enhancing user understanding. The framework also facilitates human interaction, allowing users to express preferences and guide the tree learning process. Results in a hybrid flow shop setting demonstrate that the proposed iDT framework delivers interpretable and effective decision-support policies while empowering users to influence and refine its outcomes, hence bridging the gap between AI-driven insights and real-world applicability.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"291 ","pages":"Article 109841"},"PeriodicalIF":10.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-06-30DOI: 10.1016/j.ijpe.2025.109723
Lang Zhao , Jiawei Xu , Baofeng Zhang, Jianjun Lu
How to leverage advanced technologies to enhance resource efficiency under conditions of intensified resource scarcity is becoming a pressing issue that urgently needs to be addressed. Drawing on ecological modernization theory and resource-based view, this study explores the relationship between AI and firms’ resource efficiency under different environmental pressures. Using a comprehensive dataset of Chinese listed firms, the findings reveal that artificial intelligence (AI) adoption significantly improves resource efficiency, primarily reflected in more efficient management of energy, materials, and waste. Green continuous innovation capabilities fully mediates the relationship between AI and resource efficiency. Moreover, the positive effect of AI on resource efficiency is amplified under stringent environmental pressures, indicating that firms facing higher pollution governance pressure and carbon emission pressure derive greater benefits from AI technologies. Moreover, the study further reveals four combination patterns of pollution governance pressure and carbon emission pressure at different levels, which result in differentiated outcomes in the relationship between AI and resource efficiency. Among these, high carbon emission pressure is a necessary condition for driving firms to use AI technology to enhance resource efficiency. Our study not only contributes to the theoretical understanding of the relationship between AI, external environmental pressures, and resource efficiency, but also provides some valuable insights for managers and policymakers on how to adopt AI and formulate effective environmental regulations to enhance resource efficiency.
{"title":"Leveraging AI to enhance firms’ resource efficiency: ecological modernization theory and resource-based view perspectives","authors":"Lang Zhao , Jiawei Xu , Baofeng Zhang, Jianjun Lu","doi":"10.1016/j.ijpe.2025.109723","DOIUrl":"10.1016/j.ijpe.2025.109723","url":null,"abstract":"<div><div><span>How to leverage advanced technologies to enhance resource efficiency under conditions of intensified resource scarcity is becoming a pressing issue that urgently needs to be addressed. Drawing on ecological modernization theory and resource-based view, this study explores the relationship between AI and firms’ resource efficiency under different environmental pressures. Using a comprehensive dataset of Chinese listed firms, the findings reveal that artificial intelligence (AI) adoption significantly improves resource efficiency, primarily reflected in more efficient management of energy, materials, and waste. Green continuous innovation capabilities fully mediates the relationship between AI and resource efficiency. Moreover, the positive effect of AI on resource efficiency is amplified under stringent environmental pressures, indicating that firms facing higher pollution governance pressure and </span>carbon emission<span> pressure derive greater benefits from AI technologies. Moreover, the study further reveals four combination patterns of pollution governance pressure and carbon emission pressure at different levels, which result in differentiated outcomes in the relationship between AI and resource efficiency. Among these, high carbon emission pressure is a necessary condition for driving firms to use AI technology to enhance resource efficiency. Our study not only contributes to the theoretical understanding of the relationship between AI, external environmental pressures, and resource efficiency, but also provides some valuable insights for managers and policymakers on how to adopt AI and formulate effective environmental regulations to enhance resource efficiency.</span></div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"291 ","pages":"Article 109723"},"PeriodicalIF":10.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-11-27DOI: 10.1016/j.ijpe.2025.109866
Minjian Liu , Qi Dong , Yunbing Li , Shaofu Du
The rise of the online food delivery (OFD) industry has greatly facilitated consumers, but frequent delivery delays have damaged the shopping experience. In order to mitigate the negative effects of delivery delays, many OFD platforms have launched buy-online-and-pick-up-in-store (BOPS) and delay insurance services. In this context, the merchant needs to decide whether to implement BOPS and offer free delay insurance (FI) to consumers. BOPS allows consumers to place orders online and pick up products offline; FI provides platform compensation for consumers who experience delivery delays. Considering delivery delays, we study the impact of BOPS and FI services on consumer purchasing, merchant pricing, and the platform’s FI premium decisions. We then examine the merchant’s joint optimization strategies of these two services. Four scenarios are analyzed regarding whether the merchant adopts these two services. Interestingly, we find that BOPS can always expand or at least maintain the merchant’s market coverage, while FI may cause market coverage to decline. Whether a merchant implements BOPS depends on the fixed costs of implementation and the compensation of FI, and whether to offer FI depends on consumers’ hassle costs and the merchant’s unit operation cost in the online channel. Counter-intuitively, when the merchant’s unit operation cost is relatively high or relatively low, FI will increase the merchant’s motivation to implement BOPS. Finally, we find that none of the four service strategies is dominant for the merchant; the platform’s profits cannot be maximized if no services are provided; consumers can only maximize total surplus when BOPS is implemented.
{"title":"Navigating trade-offs in online food delivery: The interplay of buy-online-and-pick-up-in-store and delay insurance","authors":"Minjian Liu , Qi Dong , Yunbing Li , Shaofu Du","doi":"10.1016/j.ijpe.2025.109866","DOIUrl":"10.1016/j.ijpe.2025.109866","url":null,"abstract":"<div><div>The rise of the online food delivery (OFD) industry has greatly facilitated consumers, but frequent delivery delays have damaged the shopping experience. In order to mitigate the negative effects of delivery delays, many OFD platforms have launched buy-online-and-pick-up-in-store (BOPS) and delay insurance services. In this context, the merchant needs to decide whether to implement BOPS and offer free delay insurance (FI) to consumers. BOPS allows consumers to place orders online and pick up products offline; FI provides platform compensation for consumers who experience delivery delays. Considering delivery delays, we study the impact of BOPS and FI services on consumer purchasing, merchant pricing, and the platform’s FI premium decisions. We then examine the merchant’s joint optimization strategies of these two services. Four scenarios are analyzed regarding whether the merchant adopts these two services. Interestingly, we find that BOPS can always expand or at least maintain the merchant’s market coverage, while FI may cause market coverage to decline. Whether a merchant implements BOPS depends on the fixed costs of implementation and the compensation of FI, and whether to offer FI depends on consumers’ hassle costs and the merchant’s unit operation cost in the online channel. Counter-intuitively, when the merchant’s unit operation cost is relatively high or relatively low, FI will increase the merchant’s motivation to implement BOPS. Finally, we find that none of the four service strategies is dominant for the merchant; the platform’s profits cannot be maximized if no services are provided; consumers can only maximize total surplus when BOPS is implemented.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"291 ","pages":"Article 109866"},"PeriodicalIF":10.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the effects of market and technology uncertainties on reciprocal investments and the subsequent moderating role of appropriability mechanisms (i.e., patents, contracts, lead time, knowledge complexity). The study forwards several hypotheses that draw upon transaction cost economics and contingency theory. These hypotheses are tested using survey data of 313 firms that engage in horizontal coopetition relationships. Results suggest that both market and technology uncertainties lead rival partners to commit to reciprocal investments. The results further indicate that partners’ use of formal appropriability negatively moderates the relationship between market uncertainty and reciprocal investments, while informal appropriability positively moderates this relationship. Alternatively, formal appropriability positively moderates the relationship between technology uncertainty and investments, whereas informal appropriability negatively moderates this relationship. Contrary to the prevailing practice of viewing appropriability as a standalone mechanism, our findings point towards the significance of viewing appropriability mechanisms dichotomously, especially in the case of uncertain environments. As the findings suggest, employing more relevant appropriability mechanisms based on the type of uncertainties in which partners operate could lead to better outcomes for the partners pursuing coopetition relationships.
{"title":"Environmental uncertainties and reciprocal investments within coopetition: The contingent role of appropriability mechanisms","authors":"Chandrasekararao Seepana , Antony Paulraj , Aneesh Datar","doi":"10.1016/j.ijpe.2025.109811","DOIUrl":"10.1016/j.ijpe.2025.109811","url":null,"abstract":"<div><div>This study investigates the effects of market and technology uncertainties on reciprocal investments and the subsequent moderating role of appropriability mechanisms (i.e., patents, contracts, lead time, knowledge complexity). The study forwards several hypotheses that draw upon transaction cost economics and contingency theory. These hypotheses are tested using survey data of 313 firms that engage in horizontal coopetition relationships. Results suggest that both market and technology uncertainties lead rival partners to commit to reciprocal investments. The results further indicate that partners’ use of formal appropriability negatively moderates the relationship between market uncertainty and reciprocal investments, while informal appropriability positively moderates this relationship. Alternatively, formal appropriability positively moderates the relationship between technology uncertainty and investments, whereas informal appropriability negatively moderates this relationship. Contrary to the prevailing practice of viewing appropriability as a standalone mechanism, our findings point towards the significance of viewing appropriability mechanisms dichotomously, especially in the case of uncertain environments. As the findings suggest, employing more relevant appropriability mechanisms based on the type of uncertainties in which partners operate could lead to better outcomes for the partners pursuing coopetition relationships.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"290 ","pages":"Article 109811"},"PeriodicalIF":10.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-29DOI: 10.1016/j.ijpe.2025.109807
Jianyu Zhao , Yingbo Xu , Bo Zou , Xi Xi , Wei Liu
Artificial intelligence (AI) reshapes the business landscape. The role of AI applications in product innovation has received much attention, yet the impact of AI technologies on the product itself remains insufficient. To address this question, we developed a theoretical framework around the resource-based view and the concept of generativity. We theorize and investigate the impact of AI application on generativity in a product architecture and whether generativity promotes product sales. Using a new energy vehicle (NEV) as a sample, we set a research context of digital manufacturing in which we argue that AI applications facilitate the generation of intelligent functions in NEVs, and intelligent functions promote vehicle sales. We further find that AI–large language model (LLM) adoption weakens the positive relationship between intelligent functions and vehicle sales. We aim to contribute to AI application and generativity studies, and we provide evidence for manufacturers to develop appropriate AI system strategies.
{"title":"From invisible to visible: How artificial intelligence facilitates generativity in product architecture","authors":"Jianyu Zhao , Yingbo Xu , Bo Zou , Xi Xi , Wei Liu","doi":"10.1016/j.ijpe.2025.109807","DOIUrl":"10.1016/j.ijpe.2025.109807","url":null,"abstract":"<div><div>Artificial intelligence (AI) reshapes the business landscape. The role of AI applications in product innovation has received much attention, yet the impact of AI technologies on the product itself remains insufficient. To address this question, we developed a theoretical framework around the resource-based view and the concept of generativity. We theorize and investigate the impact of AI application on generativity in a product architecture and whether generativity promotes product sales. Using a new energy vehicle (NEV) as a sample, we set a research context of digital manufacturing in which we argue that AI applications facilitate the generation of intelligent functions in NEVs, and intelligent functions promote vehicle sales. We further find that AI–large language model (LLM) adoption weakens the positive relationship between intelligent functions and vehicle sales. We aim to contribute to AI application and generativity studies, and we provide evidence for manufacturers to develop appropriate AI system strategies.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"290 ","pages":"Article 109807"},"PeriodicalIF":10.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-28DOI: 10.1016/j.ijpe.2025.109777
Lu Shen , Kevin Zheng Zhou , Yan Ye
Governance for reliability has gained prominence in the post-COVID era. However, it remains unclear whether supplier concentration enhances reliable efficiency amidst supply chain risks, especially during catastrophic risks like the COVID-19 pandemic. Our research leverages the COVID-19 pandemic to assesses the impacts of supplier concentration by analyzing data from 1870 Chinese listed firms from 2017 through 2022. The results reveal a significant decline in operational efficiency as the severity of the pandemic increases, with this decline being more pronounced among firms with high supplier concentration. However, the negative interactive impact of supplier concentration and pandemic severity decreases when firms possess high levels of digitalization or relational ties. These findings highlight the importance of distinguishing between different types of supply chain risks when assessing the impact of supplier concentration and reveals risk-mitigating strategies that firms can adopt to navigate the paradoxical effects of supplier concentration amid catastrophic risks.
{"title":"Supplier concentration and operational efficiency during catastrophic risks","authors":"Lu Shen , Kevin Zheng Zhou , Yan Ye","doi":"10.1016/j.ijpe.2025.109777","DOIUrl":"10.1016/j.ijpe.2025.109777","url":null,"abstract":"<div><div>Governance for reliability has gained prominence in the post-COVID era. However, it remains unclear whether supplier concentration enhances reliable efficiency amidst supply chain risks, especially during catastrophic risks like the COVID-19 pandemic. Our research leverages the COVID-19 pandemic to assesses the impacts of supplier concentration by analyzing data from 1870 Chinese listed firms from 2017 through 2022. The results reveal a significant decline in operational efficiency as the severity of the pandemic increases, with this decline being more pronounced among firms with high supplier concentration. However, the negative interactive impact of supplier concentration and pandemic severity decreases when firms possess high levels of digitalization or relational ties. These findings highlight the importance of distinguishing between different types of supply chain risks when assessing the impact of supplier concentration and reveals risk-mitigating strategies that firms can adopt to navigate the paradoxical effects of supplier concentration amid catastrophic risks.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"290 ","pages":"Article 109777"},"PeriodicalIF":10.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-25DOI: 10.1016/j.ijpe.2025.109772
Yu Liu , Shenle Pan , Eric Ballot
Urban logistics faces increasing pressure from rising population densities, escalating delivery demands, and constrained urban resources. Traditional logistics systems struggle to adapt to real-time urban dynamics, leading to inefficiencies, congestion, and environmental concerns. A key challenge lies in mobilizing underutilized assets, such as off-hour freight parking, and adopting multimodal solutions to navigate diverse and increasingly strict regulations, thereby enhancing both sustainability and operational efficiency. However, effective management and utilization of these assets require real-time visibility, cross-stakeholder collaboration, and intelligent decision-making. This study proposes a federated digital twin platform to enhance logistics operations efficiency by integrating asset management and knowledge-driven operations management, relying on real-time asset visibility and delivery knowledge, such as destination characteristics and preferred logistics modalities. Unlike traditional logistics planning, which relies on static assumptions, our approach adapts to urban constraints by continuously querying real-time asset information and integrating logistics-related knowledge into operations management. To assess the effectiveness of this approach, an optimization-based simulation framework with decision-making tools is developed. The study evaluates multi-echelon logistics networks, incorporating micro-hubs, dynamic transshipment points, and multimodal logistics options, including on-foot porters, E-cargo bikes, and Road Autonomous Delivery Robots (RADRs). Findings demonstrate that integrating federated digital twins with knowledge-driven approaches, such as destination-based clustering and modality selection, reduces costs by over 50 % and emissions by more than 30 %. This study underscores the transformative potential of digital twins in enabling real-time, knowledge-driven operations management, and fostering more sustainable and efficient urban logistics systems.
{"title":"Federated digital twins platform for smart city logistics: A knowledge-driven approach","authors":"Yu Liu , Shenle Pan , Eric Ballot","doi":"10.1016/j.ijpe.2025.109772","DOIUrl":"10.1016/j.ijpe.2025.109772","url":null,"abstract":"<div><div>Urban logistics faces increasing pressure from rising population densities, escalating delivery demands, and constrained urban resources. Traditional logistics systems struggle to adapt to real-time urban dynamics, leading to inefficiencies, congestion, and environmental concerns. A key challenge lies in mobilizing underutilized assets, such as off-hour freight parking, and adopting multimodal solutions to navigate diverse and increasingly strict regulations, thereby enhancing both sustainability and operational efficiency. However, effective management and utilization of these assets require real-time visibility, cross-stakeholder collaboration, and intelligent decision-making. This study proposes a federated digital twin platform to enhance logistics operations efficiency by integrating asset management and knowledge-driven operations management, relying on real-time asset visibility and delivery knowledge, such as destination characteristics and preferred logistics modalities. Unlike traditional logistics planning, which relies on static assumptions, our approach adapts to urban constraints by continuously querying real-time asset information and integrating logistics-related knowledge into operations management. To assess the effectiveness of this approach, an optimization-based simulation framework with decision-making tools is developed. The study evaluates multi-echelon logistics networks, incorporating micro-hubs, dynamic transshipment points, and multimodal logistics options, including on-foot porters, E-cargo bikes, and Road Autonomous Delivery Robots (RADRs). Findings demonstrate that integrating federated digital twins with knowledge-driven approaches, such as destination-based clustering and modality selection, reduces costs by over 50 % and emissions by more than 30 %. This study underscores the transformative potential of digital twins in enabling real-time, knowledge-driven operations management, and fostering more sustainable and efficient urban logistics systems.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"290 ","pages":"Article 109772"},"PeriodicalIF":10.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}