Pub Date : 2024-11-02DOI: 10.1016/j.ijpe.2024.109461
Pervaiz Akhtar , Muthu De Silva , Zaheer Khan , Shlomo Tarba , Joseph Amankwah-Amoah , Geoffrey Wood
Recent years have seen the extensive use of big data analytics, related technological infrastructure, and machine learning applications for digital transformation. The resource dependency related to data-driven applications elicits public-private collaborations (PPCs) between governments and private or non-government organizations (NGOs) for value creation. Such collaborations are effective for the success of humanitarian supply chain operations (HSCOs), particularly in the event of large-scale disasters. By building on resource dependence theory (RDT), our study explores the links between digital transformation, PPCs, and HSCO success. Using structural equation modeling on data collected from 224 key decision-makers and experts, we found that digital transformation mediates the relationship between private-NGO collaborations and HSCO success while host government support moderates it. Our study thus makes an original contribution to RDT and the emerging domains of contemporary digital and data-driven applications in HSCO. The implications and future research directions arising from this study are also discussed in this research paper.
{"title":"Digital transformation in public-private collaborations: The success of humanitarian supply chain operations","authors":"Pervaiz Akhtar , Muthu De Silva , Zaheer Khan , Shlomo Tarba , Joseph Amankwah-Amoah , Geoffrey Wood","doi":"10.1016/j.ijpe.2024.109461","DOIUrl":"10.1016/j.ijpe.2024.109461","url":null,"abstract":"<div><div>Recent years have seen the extensive use of big data analytics, related technological infrastructure, and machine learning applications for digital transformation. The resource dependency related to data-driven applications elicits public-private collaborations (PPCs) between governments and private or non-government organizations (NGOs) for value creation. Such collaborations are effective for the success of humanitarian supply chain operations (HSCOs), particularly in the event of large-scale disasters. By building on resource dependence theory (RDT), our study explores the links between digital transformation, PPCs, and HSCO success. Using structural equation modeling on data collected from 224 key decision-makers and experts, we found that digital transformation mediates the relationship between private-NGO collaborations and HSCO success while host government support moderates it. Our study thus makes an original contribution to RDT and the emerging domains of contemporary digital and data-driven applications in HSCO. The implications and future research directions arising from this study are also discussed in this research paper.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"279 ","pages":"Article 109461"},"PeriodicalIF":9.8,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-26DOI: 10.1016/j.ijpe.2024.109447
Jongsuk Lee , Ping Chong Chua , Bufan Liu , Seung Ki Moon , Manuel Lopez
In the Industry 4.0 era, advanced technologies are transforming manufacturing processes and systems. Additionally, the increasing prevalence of big data and AI technologies have made decision-making using manufacturing data increasingly important. However, Small and Medium-sized Enterprises (SMEs) have encountered significant obstacles in adopting these technologies due to resource limitations and constraints. For SMEs, selecting an appropriate production strategy is challenging due to the complexity of manufacturing systems. As a response, this paper proposes a hybrid Simulation-Optimization with Multi-Criteria Decision-Making (SOMCDM) framework for SMEs to identify effective and customized production layouts. In the proposed approach, we model various production scenarios using a cellular manufacturing system. Surrogate models for different production layouts are created to basis functions using Multivariate Adaptive Regression Splines (MARS). Subsequently, the basis functions are used as fitness functions to identify optimal production parameters in a genetic algorithm. Then, optimized parameters are applied to production criteria and ranked using a multi-criteria decision-making technique. In a case study, the proposed framework is applied to select the best production platform among three scenarios for a company assembling complex products. The selected production platform improves overall manufacturing performance by 11.95% compared to the existing one. This study demonstrates the effectiveness of the proposed framework in identifying the best production platform for labor-intensive SMEs manufacturing high-mix, low-volume products using SOMCDM for a digital twin environment. The proposed framework is further detailed through a case study of a 3D printer assembly factory.
在工业 4.0 时代，先进技术正在改变制造流程和系统。此外，大数据和人工智能技术的日益普及也使得利用制造数据进行决策变得越来越重要。然而，由于资源的限制和制约，中小型企业（SMEs）在采用这些技术时遇到了巨大障碍。对于中小企业来说，由于制造系统的复杂性，选择合适的生产战略具有挑战性。为此，本文提出了一个混合模拟优化与多标准决策（SOMCDM）框架，以帮助中小企业确定有效的定制生产布局。在所提出的方法中，我们使用蜂窝制造系统对各种生产场景进行建模。使用多变量自适应回归样条曲线（MARS）创建不同生产布局的替代模型，并将其转化为基函数。随后，在遗传算法中将基函数作为拟合函数来确定最佳生产参数。然后，将优化参数应用于生产标准，并使用多标准决策技术进行排序。在一个案例研究中，所提出的框架被应用于为一家组装复杂产品的公司从三种方案中选择最佳生产平台。与现有生产平台相比，所选生产平台的整体生产绩效提高了 11.95%。这项研究证明了所提出的框架在使用数字孪生环境下的 SOMCDM 为生产高混合、小批量产品的劳动密集型中小企业确定最佳生产平台方面的有效性。通过对一家 3D 打印机装配厂的案例研究，进一步详细介绍了所提出的框架。
{"title":"A hybrid data-driven optimization and decision-making approach for a digital twin environment: Towards customizing production platforms","authors":"Jongsuk Lee , Ping Chong Chua , Bufan Liu , Seung Ki Moon , Manuel Lopez","doi":"10.1016/j.ijpe.2024.109447","DOIUrl":"10.1016/j.ijpe.2024.109447","url":null,"abstract":"<div><div>In the Industry 4.0 era, advanced technologies are transforming manufacturing processes and systems. Additionally, the increasing prevalence of big data and AI technologies have made decision-making using manufacturing data increasingly important. However, Small and Medium-sized Enterprises (SMEs) have encountered significant obstacles in adopting these technologies due to resource limitations and constraints. For SMEs, selecting an appropriate production strategy is challenging due to the complexity of manufacturing systems. As a response, this paper proposes a hybrid Simulation-Optimization with Multi-Criteria Decision-Making (SOMCDM) framework for SMEs to identify effective and customized production layouts. In the proposed approach, we model various production scenarios using a cellular manufacturing system. Surrogate models for different production layouts are created to basis functions using Multivariate Adaptive Regression Splines (MARS). Subsequently, the basis functions are used as fitness functions to identify optimal production parameters in a genetic algorithm. Then, optimized parameters are applied to production criteria and ranked using a multi-criteria decision-making technique. In a case study, the proposed framework is applied to select the best production platform among three scenarios for a company assembling complex products. The selected production platform improves overall manufacturing performance by 11.95% compared to the existing one. This study demonstrates the effectiveness of the proposed framework in identifying the best production platform for labor-intensive SMEs manufacturing high-mix, low-volume products using SOMCDM for a digital twin environment. The proposed framework is further detailed through a case study of a 3D printer assembly factory.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"279 ","pages":"Article 109447"},"PeriodicalIF":9.8,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552805","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}
Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies—AI, cloud computing, and BDA—further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.
{"title":"Value of blockchain for scope 3 carbon disclosure: The moderating role of data processing technologies","authors":"Yuan Chen , Yunting Feng , Kee-Hung Lai , Qinghua Zhu","doi":"10.1016/j.ijpe.2024.109445","DOIUrl":"10.1016/j.ijpe.2024.109445","url":null,"abstract":"<div><div>Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies—AI, cloud computing, and BDA—further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"279 ","pages":"Article 109445"},"PeriodicalIF":9.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536100","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 : 2024-10-23DOI: 10.1016/j.ijpe.2024.109443
Yang Yang, Mingyang Zou
Non-compliance with operational procedures can significantly disrupt the functioning of supply chains. This study examines the impact of corporate misconduct by both supplier and customer firms on the corporate misconduct of the focal firm within the supply chain. Utilizing data from 723 publicly listed companies in China, we employ a difference-in-differences approach for our analysis. The results indicate that misbehavior exhibited by both supplier and customer firms contributes to an increase in corporate misconduct by the focal firm. Based on the social contagion theory, we argue that supplier misconduct leads to an increase in focus firm misconduct through a mechanism similar to “spillover effect.” Customer misconduct leads to an increase in focus firm misconduct through a mechanism similar to the “learning effect”. And this phenomenon is influenced by the cooperation intensity and the industry sensitivity. The conclusion to our research makes some theoretical contributions. First, our research focuses on silent organizational factors in supply chain contagion, providing evidence of such factors spreading unobserved in the supply chain. Secondly, we explains the different mechanisms of transmission between suppliers and customers in the dissemination of misconduct across the supply chain. Finally, our research findings provide support for managing supply chain misconduct as well as supplier and customer collaboration.
{"title":"Contagion of corporate misconduct in the supply chain: Evidence from customers and suppliers in China","authors":"Yang Yang, Mingyang Zou","doi":"10.1016/j.ijpe.2024.109443","DOIUrl":"10.1016/j.ijpe.2024.109443","url":null,"abstract":"<div><div>Non-compliance with operational procedures can significantly disrupt the functioning of supply chains. This study examines the impact of corporate misconduct by both supplier and customer firms on the corporate misconduct of the focal firm within the supply chain. Utilizing data from 723 publicly listed companies in China, we employ a difference-in-differences approach for our analysis. The results indicate that misbehavior exhibited by both supplier and customer firms contributes to an increase in corporate misconduct by the focal firm. Based on the social contagion theory, we argue that supplier misconduct leads to an increase in focus firm misconduct through a mechanism similar to “spillover effect.” Customer misconduct leads to an increase in focus firm misconduct through a mechanism similar to the “learning effect”. And this phenomenon is influenced by the cooperation intensity and the industry sensitivity. The conclusion to our research makes some theoretical contributions. First, our research focuses on silent organizational factors in supply chain contagion, providing evidence of such factors spreading unobserved in the supply chain. Secondly, we explains the different mechanisms of transmission between suppliers and customers in the dissemination of misconduct across the supply chain. Finally, our research findings provide support for managing supply chain misconduct as well as supplier and customer collaboration.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"279 ","pages":"Article 109443"},"PeriodicalIF":9.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552806","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 : 2024-10-22DOI: 10.1016/j.ijpe.2024.109437
Gaurav Kapoor , Yoon Sang Lee , Riyaz Sikora , Selwyn Piramuthu
Warehouse inventory management is a complex process. When inventory includes perishables, the complexity of these processes is compounded with additional requirements such as appropriate ambient storage conditions and placement of one type of perishables (e.g., bananas) far away from another type of perishables (e.g., strawberries). With perishables spending a significant amount of time post-harvest in warehouses, appropriate management of warehouse inventory is necessary to reduce wastage due to spoilage. Drone-based warehouse inventory management is gaining popularity as seen in the increasing number of firms in this space as well as the number of research publications. RFID tags have been widely used for inventory management for more than two decades. While drones have been successfully used in warehouses with non-perishables, RFID and drone use in warehouses with perishables has not witnessed its fair share as evidenced by the lack of publications in this general area. This paper is a step in the direction to address this void in published literature. We consider object-level RFID tags and drones to automate warehouse inventory management of perishables. Results from our analytical model and simulation analysis indicate that such warehouse automation is beneficial to both the warehouse operators and their customers.
仓库库存管理是一个复杂的过程。当库存包括易腐物品时，这些过程的复杂性就会因额外的要求而变得更加复杂，例如适当的环境储存条件和将一种易腐物品（如香蕉）放置在远离另一种易腐物品（如草莓）的地方。由于易腐物品收获后会在仓库中存放很长时间，因此有必要对仓库库存进行适当管理，以减少因变质而造成的浪费。基于无人机的仓库库存管理越来越受欢迎，这从该领域公司数量和研究出版物数量的不断增加中可见一斑。二十多年来，RFID 标签一直被广泛用于库存管理。虽然无人机已成功应用于非易腐物品仓库，但 RFID 和无人机在易腐物品仓库中的应用还没有得到应有的重视，这一点从该领域缺乏相关出版物可见一斑。本文正是朝着解决这一文献空白的方向迈出的一步。我们考虑使用对象级 RFID 标签和无人机来实现易腐物品仓库库存管理的自动化。我们的分析模型和模拟分析结果表明，这种仓库自动化对仓库经营者及其客户都有好处。
{"title":"Drone-based warehouse inventory management of perishables","authors":"Gaurav Kapoor , Yoon Sang Lee , Riyaz Sikora , Selwyn Piramuthu","doi":"10.1016/j.ijpe.2024.109437","DOIUrl":"10.1016/j.ijpe.2024.109437","url":null,"abstract":"<div><div>Warehouse inventory management is a complex process. When inventory includes perishables, the complexity of these processes is compounded with additional requirements such as appropriate ambient storage conditions and placement of one type of perishables (e.g., bananas) far away from another type of perishables (e.g., strawberries). With perishables spending a significant amount of time post-harvest in warehouses, appropriate management of warehouse inventory is necessary to reduce wastage due to spoilage. Drone-based warehouse inventory management is gaining popularity as seen in the increasing number of firms in this space as well as the number of research publications. RFID tags have been widely used for inventory management for more than two decades. While drones have been successfully used in warehouses with non-perishables, RFID and drone use in warehouses with perishables has not witnessed its fair share as evidenced by the lack of publications in this general area. This paper is a step in the direction to address this void in published literature. We consider object-level RFID tags and drones to automate warehouse inventory management of perishables. Results from our analytical model and simulation analysis indicate that such warehouse automation is beneficial to both the warehouse operators and their customers.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"278 ","pages":"Article 109437"},"PeriodicalIF":9.8,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531685","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 : 2024-10-21DOI: 10.1016/j.ijpe.2024.109440
Rekha Guchhait , Biswajit Sarkar
Competition in the production business is getting harder day by day for the manufacturer and the corresponding retailer of the supply chain. When a supply chain deals with deteriorating products, an efficient production system, and a logistic provider are two important pillars to keep the business profitable. Logistics facility for deteriorating products requires additional attention, especially when deteriorating products require a refrigerate-storage facility. This study investigates the economic evaluation of flexible production and logistics outsourcing for deteriorating products with the help of logistics outsourcing. The retailer outsources logistics facilities through a fourth-party logistics provider. Fourth-party logistics uses reefer shipping to transport deteriorating products for preventing deterioration during transportation. The refrigerant of the shipping container has a random leakage during shipping. In the meantime, the manufacturer uses preservation facility to reduce the random deterioration rate of products. A classical optimization finds the global minimum total cost for a unique solution of decision variables. Results prove that flexible production is very sensitive to machinery systems and fresh products are more cost-effective for transportation than frozen products. The proposed system saves 38.38% of the total cost in comparison with a constant production rate. Sensitivity analysis finds transportation costs of 4PL are the most sensitive parameters of the system.
{"title":"Economic evaluation of an outsourced fourth-party logistics (4PL) under a flexible production system","authors":"Rekha Guchhait , Biswajit Sarkar","doi":"10.1016/j.ijpe.2024.109440","DOIUrl":"10.1016/j.ijpe.2024.109440","url":null,"abstract":"<div><div>Competition in the production business is getting harder day by day for the manufacturer and the corresponding retailer of the supply chain. When a supply chain deals with deteriorating products, an efficient production system, and a logistic provider are two important pillars to keep the business profitable. Logistics facility for deteriorating products requires additional attention, especially when deteriorating products require a refrigerate-storage facility. This study investigates the economic evaluation of flexible production and logistics outsourcing for deteriorating products with the help of logistics outsourcing. The retailer outsources logistics facilities through a fourth-party logistics provider. Fourth-party logistics uses reefer shipping to transport deteriorating products for preventing deterioration during transportation. The refrigerant of the shipping container has a random leakage during shipping. In the meantime, the manufacturer uses preservation facility to reduce the random deterioration rate of products. A classical optimization finds the global minimum total cost for a unique solution of decision variables. Results prove that flexible production is very sensitive to machinery systems and fresh products are more cost-effective for transportation than frozen products. The proposed system saves 38.38% of the total cost in comparison with a constant production rate. Sensitivity analysis finds transportation costs of 4PL are the most sensitive parameters of the system.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"279 ","pages":"Article 109440"},"PeriodicalIF":9.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652122","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 : 2024-10-18DOI: 10.1016/j.ijpe.2024.109444
Huan He, Yuxiao Ye, Baofeng Huo
Production repurposing is an initiative for firms to alter their manufacturing capabilities and outputs to meet new demands, particularly during times of crisis. This initiative is crucial for businesses to remain resilient to unexpected changes in the market. Despite its importance, the specific factors that drive production repurposing during crises, especially from the perspective of human capital development, are not well understood. Drawing upon the ability, motivation, and opportunity (AMO) framework, this study aims to examine the impact of pre-pandemic employee training, an ability-enhancing practice, on production repurposing during the pandemic. Based on a dataset of 4679 firm-year observations from 32 countries sourced from the World Bank, our regression results indicate that firms that engaged in pre-pandemic employee training are more likely to initiate production repurposing during the pandemic. Furthermore, we delve into the moderating roles of government wage subsides, a motivation factor, and labor shortages, an opportunity constraint. The results reveal that government wage subsidies amplify the positive effect of pre-pandemic training on production repurposing, whereas labor shortages dampen this impact. Our heterogeneity analysis further suggests that national socioeconomic features can influence these relationships. This study underscores the role of employee training in preparing firms to actively adapt during crises. It also highlights the necessity of providing adequate motivations and creating conducive opportunities to facilitate human capital. These insights are valuable for managers and policymakers aiming to enhance firm adaptability and ensure resilient operations during crises.
{"title":"Can employee training facilitate production repurposing in crises? An ability-motivation-opportunity perspective","authors":"Huan He, Yuxiao Ye, Baofeng Huo","doi":"10.1016/j.ijpe.2024.109444","DOIUrl":"10.1016/j.ijpe.2024.109444","url":null,"abstract":"<div><div>Production repurposing is an initiative for firms to alter their manufacturing capabilities and outputs to meet new demands, particularly during times of crisis. This initiative is crucial for businesses to remain resilient to unexpected changes in the market. Despite its importance, the specific factors that drive production repurposing during crises, especially from the perspective of human capital development, are not well understood. Drawing upon the ability, motivation, and opportunity (AMO) framework, this study aims to examine the impact of pre-pandemic employee training, an ability-enhancing practice, on production repurposing during the pandemic. Based on a dataset of 4679 firm-year observations from 32 countries sourced from the World Bank, our regression results indicate that firms that engaged in pre-pandemic employee training are more likely to initiate production repurposing during the pandemic. Furthermore, we delve into the moderating roles of government wage subsides, a motivation factor, and labor shortages, an opportunity constraint. The results reveal that government wage subsidies amplify the positive effect of pre-pandemic training on production repurposing, whereas labor shortages dampen this impact. Our heterogeneity analysis further suggests that national socioeconomic features can influence these relationships. This study underscores the role of employee training in preparing firms to actively adapt during crises. It also highlights the necessity of providing adequate motivations and creating conducive opportunities to facilitate human capital. These insights are valuable for managers and policymakers aiming to enhance firm adaptability and ensure resilient operations during crises.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"278 ","pages":"Article 109444"},"PeriodicalIF":9.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537996","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 : 2024-10-18DOI: 10.1016/j.ijpe.2024.109442
Xun Wang , Vasco Sanchez Rodrigues , Emrah Demir , Joseph Sarkis
Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.
{"title":"Algorithm aversion during disruptions: The case of safety stock","authors":"Xun Wang , Vasco Sanchez Rodrigues , Emrah Demir , Joseph Sarkis","doi":"10.1016/j.ijpe.2024.109442","DOIUrl":"10.1016/j.ijpe.2024.109442","url":null,"abstract":"<div><div>Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"278 ","pages":"Article 109442"},"PeriodicalIF":9.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.ijpe.2024.109439
M.A. Hoque , Md Ashraful Babu
Production of a product at multiple sources and its deliveries to multiple destinations are a common practice in business. Minimization of the integrated total cost of performing operations and transportation considering the relevant factors such as the minimum order quantity contract, capacities of transport vehicles, and times of transportation in this supply chain is essential, in supplying products to customers at reasonable lesser prices. However, such a supply chain has received little attention in terms of minimizing the integrated total cost taking into account these related factors explicitly. So, there lies a research scope on this topic to fulfill the growing need of minimizing the cost of production-deliveries of products to meet customers’ demands fruitfully. Here we develop such a generalized mathematical model to minimize the integrated total cost of carrying out operations at manufacturers and retailers, and transporting batches (sub-lots) of lots from sources to destinations considering the mentioned realistic constraints. The integrated production-delivery flow is synchronized by delivering lots with batches of equal and/or unequal sizes. First without considering transportation costs, we obtain optimal batches to minimize the total cost of the model. Each of these optimal batches is proportionally distributed at manufacturers as supplies and at retailers as demands. Then the minimum transportation cost solution from manufacturers to retailers is incorporated to the earlier solution to obtain the final result. We illustrate this solution policy with numerical example problems. Sensitivity analyses are performed to see the effect of increasing values of parameters on the minimum total cost.
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This paper proposes to estimate the returns-to-scale of production sets by considering the individual return of each observation, considered as a decision-making unit through the notion of -returns to scale assumption. Along this line, the global technology is then constructed as the intersection of all the individual technologies. Hence, an axiomatic foundation is proposed to present the notion of -returns to scale. This new characterization of the returns-to-scale encompasses the definition of -returns to scale, as a special case as well as the standard non-increasing and non-decreasing returns-to-scale models. A non-parametric procedure based on the goodness of fit approach is proposed to assess these individual returns-to-scale. To illustrate this notion of -returns to scale assumption, an empirical illustration is provided based on a dataset involving 63 industries constituting the whole American economy over the period 1987-2018.
{"title":"Characterization of production sets through individual returns-to-scale: A non parametric specification and an illustration with the U.S industries","authors":"Jean-Philippe Boussemart , Walter Briec , Raluca Parvulescu , Paola Ravelojaona","doi":"10.1016/j.ijpe.2024.109433","DOIUrl":"10.1016/j.ijpe.2024.109433","url":null,"abstract":"<div><div>This paper proposes to estimate the returns-to-scale of production sets by considering the individual return of each observation, considered as a decision-making unit through the notion of <span><math><mi>Λ</mi></math></span>-returns to scale assumption. Along this line, the global technology is then constructed as the intersection of all the individual technologies. Hence, an axiomatic foundation is proposed to present the notion of <span><math><mi>Λ</mi></math></span>-returns to scale. This new characterization of the returns-to-scale encompasses the definition of <span><math><mi>α</mi></math></span>-returns to scale, as a special case as well as the standard non-increasing and non-decreasing returns-to-scale models. A non-parametric procedure based on the goodness of fit approach is proposed to assess these individual returns-to-scale. To illustrate this notion of <span><math><mi>Λ</mi></math></span>-returns to scale assumption, an empirical illustration is provided based on a dataset involving 63 industries constituting the whole American economy over the period 1987-2018.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"278 ","pages":"Article 109433"},"PeriodicalIF":9.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531799","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}
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