International Journal of Production Economics最新文献

Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The risks reflect the impact of uncertainty and volatility on the resilience of the supply chains. Therefore, there is a need for the enhancement of the production planning in Energy Supply Chains (ESCs), as it enables affordable energy supplies and supports the companies transition to a clean, secure and sustainable energy mix. This study aims to understand the interactive behavior among individuals and optimize their production planning under uncertainty scenarios. In particular, we propose a novel framework to couple an Agent-based Modelling (ABM) and a Co-evolutionary Algorithm (CEA), to realize its capacity to solve a Many-objective Optimization Problem (MaOP) where the profits of multiple agents are concurrently maximized in their interactive transaction processes under normal conditions and uncertain disruption events.

For demonstration, we illustrate the proposed approach by considering a five-layer oil and gas ESC model, where uncertainties from multiple sources and the structural dynamics challenge the balance between supply and demand. The results obtained by an integration of a Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) algorithm into ABM show the pricing and orders of the target agents are optimized while the loss of ESC resilience is minimized under uncertainty scenarios, proving its capacity of improving the diversity and the convergence, compared to the classic evolutionary algorithms.

Working only with suppliers that meet certain social and environmental standards has become a new norm in the selection of business partners for many firms, which encourages suppliers to become more transparent to gain a competitive advantage. This study investigates whether buyer legitimacy is associated with supply chain transparency from an environmental, social, and governance (ESG) perspective. Using a dataset of 605 dyadic relationships from Chinese listed firms for the period 2011–2019, we empirically examine our conceptual framework using hierarchical linear regression models. We demonstrate that buyer legitimacy is positively associated with supplier ESG transparency in dyadic supply chains. We further find that the supplier's competition intensity negatively moderates the positive relationship between buyer legitimacy and supplier ESG transparency. Overall, our findings contribute to a better understanding of organizational legitimacy and the buyer–supplier relationship and offer practical guidelines for enhancing supply chain transparency.

To address the UN’s zero hunger goal (SDG 2), scattered and isolated initiatives by nonprofit organizations towards operating foodbanks are generally ineffective in developing countries where the foodbank ecosystem is at a preliminary stage. Establishing an integrated system comprising entities such as donors, foodbanks, food recovery and redistribution agencies (FRRA), and beneficiaries can be quite complex due to an underlying hierarchy, scale of operation, types of donors, and the severity of food insecurity of the beneficiaries. In this work, we present a strategic mixed-integer programming model to design an integrated foodbank network towards achieving an efficient, effective, and equitable food distribution mechanism for food-insecure beneficiaries while accounting for their age profile and nutritional requirements. We ensure cost-efficiency by minimizing the total system cost, effectiveness by discouraging food waste and unmet demand via charging penalties, and equity by adopting five variants of an egalitarian approach. We conduct a case study with a mix of real and realistically estimated data to design a foodbank network in Delhi (India) and present detailed analyses with insights for the practitioners. Specifically, the effects of foodbanks’ initial capacities, budget and strategic-to-operational cost constraints on the solution are identified. Among important observations, our analyses highlight when initiatives for collecting more ready-to-eat foods might be taken to relieve the pressure on the integrated system, and also help in identifying the conditions when investment in capacity building serves the beneficiaries’ interests better than direct spending.

This paper investigates the adoption strategy of e-commerce consumer credit services (e-CCS) and its impact on pricing strategies of a dual-channel system consisting of a wholesale platform, an agency platform, and a common supplier. We analyze the interaction between e-CCS strategies and channel modes by constructing a game model with four subgame scenarios: neither channel implements e-CCS ($\text{NN}$), only the wholesale channel provides e-CCS ($\text{IN}$), only the agency channel offers e-CCS ($\text{NI}$), both channels launch e-CCS ($\text{II}$). Our findings reveal that channel forms and e-CCS features significantly influence the adoption of e-CCS. Interestingly, the key influencing factors vary among decision makers. Specifically, the wholesale platform is willing to launch e-CCS as long as the demand benefit is prominent, while the agency platform only offers the service when the bad debt risk rate is mild. Moreover, when the agency platform provides e-CCS, the supplier accepts to use it only when there is a substantial demand benefit. Counterintuitively, we also find that the adoption of e-CCS can generate a positive spillover effect on the rival platform, leading to a “win-win” situation. In addition, we examine the impact of e-CCS on pricing decisions. Our findings indicate that the supplier and the wholesale platform may adopt distinct pricing strategies to address higher bad debt risk. Finally, we demonstrate the robustness of our main findings to investigate the impact of competition intensity, the correlation between demand benefit and price, negative externalities on demand benefit, and consumer heterogeneity on e-CCS.

The existing literature has shown that the Internet of Things (IoT) has proven to be beneficial for managing operations in the food supply chain. However, the potential failures resulting from these new technologies have not been thoroughly examined. Therefore, the aim of this study is to create a new framework for analyzing the risks associated with implementing IoT in the food supply chain. To achieve this, we introduce an extended failure mode and effect analysis framework using an integrated generalised Toma de Decisão Iterativa Multicritério (TODIM) method within the interval-valued spherical fuzzy setting. Initially, we developed a risk structure consisting of four dimensions and sixteen sub-factors to facilitate the implementation of IoT technology in the food supply chain. We then introduced an interval-valued spherical fuzzy-weighted Bonferroni mean operator to gather expert opinions and create a group risk matrix considering the interaction between the input risk data. Subsequently, we proposed a newly developed interval-valued spherical fuzzy TODIM method based on the Logarithmic percentage change-driven objective weighting (LOPCOW) method to evaluate and rank the risks associated with IoT application in the food supply chain. This method can capture the bounded rational decision behavior of experts and the uncertain preference of risk parameters. Based on the identified risk factors, we implemented a proposed risk analysis framework in line with the preferences of the experts. The results indicate that "human resource risk" and "uncertainty in technology adoption" are the most and least significant risks, respectively. We also conducted sensitivity and comparison studies to test the reliability and rationality of the proposed framework. The main findings can assist policymakers and managers in mitigating and minimizing the risks connected with IoT applications in the food supply chain. Additionally, these outcomes can aid stakeholders in making well-informed decisions about resource allocation for risk management.

Generative AI has exerted a transformative impact on various industries. However, the effective integration of generative AI into supply chain management (SCM) remains unclear. To address this, we employ the relational view to examine the relationships among generative AI usage depth, supply chain coordination, and supply chain performance at different levels of supply chain dynamism. We analyze survey data from 236 Chinese firms that have implemented generative AI to varying extents. We identify a positive association between generative AI usage depth and supply chain performance. Two types of supply chain coordination—supplier and buyer—play crucial mediating roles in connecting the aforementioned positive association. Surprisingly, supply chain dynamism amplifies the mediating roles of supplier and buyer coordination. We contribute to the existing AI-enabled SCM research by providing empirical support for the moderated mediation mechanism underlying the generative AI usage depth-supply chain performance link. We also offer practical guidelines for firms aiming to strategically leverage generative AI.

The semiconductor sector of China, which is an essential part of the global electronics market, is experiencing significant pressure to adopt green practices. Therefore, this study examines the role of green supply chain practices such as customer environmental collaboration, green purchasing and sustainable innovation in environmental performance. By leveraging the integrated perspective of the resource based-view, natural resource-based view and ability-motivation-opportunity as a theoretical lens, the study evaluates whether green practices lead to better environmental performance. In this study, energy efficiency is explored as a mediator between green purchasing, sustainable innovation and environmental performance, revealing its part in reaping the benefits of green initiatives. Additionally, green human resource practices are assessed as a moderator to identify how supportive such practices can be to amplify the relationship between customer environmental collaboration and firm environmental performance. Leveraging a PLS-SEM methodology, the findings reveal that environmental collaboration with customers, green purchasing and sustainable innovation directly lead to superior environmental performance. The study identifies the crucial role of energy efficiency as a mediator as it helps reinforce the effectiveness of green practices. The proven role of green human resource management as a moderator underscores the need to integrate human resource management with environmental objectives. Policymakers could take guidelines from the study to develop policies related to enhancing environmental performance using effective green supply chain practices and green human resource management practices.

Supply chain characteristics are an important driving force for innovation at the focal firm. Previous research has separately examined the impact of structural, relational, and capability dimensions of the supply chain on firm innovation. Relying on the structure-relationship-capability framework, this study explores the configurational impact of geographical distance, business concentration, and partner innovation of both the supplier-base and the customer-base on firm innovation. Necessary causality analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) are employed. Based on a dataset of 123 Chinese listed firms in high-tech manufacturing sectors, we provide novel insights on the antecedent configurations of the partner characteristics in both ends of supply chains for facilitating firm innovation. Our findings indicate that there are no necessary conditions for high firm innovation performance. We identify three distinct paths - namely, innovation-driven, customer-dominated, and relationship-driven configurations - that consistently lead to high firm innovation performance. From a configurational perspective, this research contributes to the innovation and supply chain literature and provides practical implications for managers to facilitate firm innovation.

This study focuses on the lot sizing problem with setup carry-over in multi-item multi-echelon capacitated production systems under uncertain customer demand for end items, as well as components. We investigate budget-uncertainty robust optimization and scenario-based stochastic programming, to address the uncertainty in customer demand. Three modeling strategies are proposed within the stochastic programming framework, exploring different decision stages for setup carry-over and production quantities. In our examination of the robust model, we explore different robustness parameters, specifically the uncertainty budget and the variation interval. Extensive numerical experiments are conducted to compare the average and worst case performance of the models on out-of-sample scenarios. We fit conditional inference trees to the evaluation results and predict the suitability of robust and stochastic approaches for the test instances based on their problem characteristics. The findings provide valuable insights, potentially enabling decision makers to estimate the most appropriate approach based on certain characteristics of the lot sizing problem that is addressed. Moreover they highlight the importance of choosing appropriate robustness parameters for robust optimization models and emphasize the value of flexibility in carry-over and quantity decisions.

Digitalization has played a key role in enabling the delivery of service-based solutions by facilitating customer-oriented needs-finding and personalized problem-solving capabilities. Artificial intelligence (AI) and Internet-of-Things (IoT) are decisive technologies in this process. This study evaluates how the use AI and IoT platforms impact solution delivery business models among servitized and non-servitized businesses. By running regression models on a sample of 213 Spanish businesses for 2023, it was found that the use of IoT platforms, rather than AI platforms, significantly assist companies down the analyzed solution delivery dimensions (i.e., customer embeddedness, operational adaptiveness, offering integratedness, and organizational networkedness). Additionally, results indicate that the combined use of IoT platforms when servitizing leads to greater customer embeddedness, whereas the use of AI platforms among servitized firms significantly contributes to improving operational adaptiveness.