International Journal of Production Economics最新文献

Spatial-temporal visibility and traceability in the digital twin enable semiconductor supply chain great transparency and efficacy, but also poses challenges to information disclosure. There is growing evidence that investment in digital twin technology is becoming crucial for improving environmental, social, and governance (ESG) ratings and mitigating risks within a sustainable semiconductor supply chain. Nonetheless, how fairness concerns impact the decision-making processes among different stakeholders remains inadequately understood. This paper presents an analytical analysis of the dynamic strategic interplay among semiconductor manufacturers, consumers, and government regulatory agencies (GRAs) utilizing the evolutionary game model. The aim is to investigate how long-term ESG strategies and fairness-oriented behaviors respond to government incentives. The findings imply that substantial investment demands can dissuade semiconductor manufacturers from delivering ESG services, while low tax rates may lead to their complacency and a subsequent lack of investment in ESG initiatives. Meanwhile, semiconductor manufacturers are more inclined to provide ESG services when the order loss ratio is low, and the tax rate is high, with strict regulations further amplifying this probability. Furthermore, consumers valuing fairness and equity tend to give positive feedback for basic services but is critical of ESG services due to issues with transparency in digital twin technologies. Addressing these issues extends beyond the scope of market incentives alone, requiring interventions from third parties such as government regulations, policy incentives, and enhanced data security measures.

The individual research domains of assembly line balancing and feeding have received considerable attention in recent years, furthered by a continuing trend towards mass customization. This research extends the limited literature on the simultaneous consideration of line balancing and feeding while substantially broadening the scope concerning the number of assembly line feeding policies and the incorporation of facility sizing decisions. This large-scale problem is modeled using mathematical programming techniques and solved using a logic-based Benders’ decomposition. We used a combination of real-world and re-engineered data from a car manufacturer to conduct numerical experiments. The major findings reveal that integrated decision-making may lead to a substantial cost reduction of up to 20% in this case. Furthermore, the study explores subsets of feeding policies to reduce the amount of different material flows within the factory while considering their associated costs. Our findings reveal a surprising difference in the importance of individual line feeding policies, specifically identifying boxed-supply as a pivotal policy for ensuring feasibility and reducing costs.

Despite the potential of business analytics (BA) in supply chains, many firms struggle to effectively integrate and leverage BA in their supply chains, leading to underutilized data and missed opportunities for optimization. Therefore, this study utilizes an affordance perspective to study the actualization of BA affordances within supply chains. We conduct a multiple case study analysis, interviewing 28 individuals across nine companies. Our investigation uncovers a hierarchy of intertwined affordances facilitating supply chain information management, cooperation, and customer-focused solutions. Significantly, we highlight the unique influence of technology, organization, and environmental (TOE) factors in shaping affordance actualization. As a result, we argue against generic prescriptions for BA affordance actualization, instead promoting a more tailored approach informed by specific TOE factors tied to each individual supply chain. Our work advances technology affordance research by explaining affordance actualization within supply chains and highlighting the dynamic interplay between TOE factors and BA affordance actualization. Subsequently, our research offers a nuanced understanding of BA implementation, demonstrating how tailored BA strategies can enhance supply chain optimization, cooperation, and customer-centric solutions, ultimately driving value creation for supply chains.

Variables Quick-Switch Sampling Systems (VQSSs) that involve both normal and tightened inspections for lot dispositions have attracted significant attention and discussion. However, existing VQSS models exhibit limitations, particularly in their rule-switching mechanism. Practitioners may encounter situations where multiple rounds of tightened inspection are necessary to ensure submission quality when suspicions of process degradation arise. Current VQSS models do not effectively address this need. To overcome this limitation, we present VQSSG by integrating a VQSS with a generalized rule-switching mechanism and the widely used capability index C_pk. VQSSG offers practitioners the flexibility to specify the number of successive lot acceptances under tightened inspection before transitioning to normal inspection. Our research includes the derivation of operating characteristics and average sample number functions for VQSSG, based on the C_pk index. Through in-depth investigations, we construct nonlinear optimisation models to determine the optimal system design and identify distinct types and settings for VQSSG, highlighting its strengths and weaknesses. Additionally, we provide managerial insights and recommendations for the effective implementation and application of VQSSG. To validate the practicality of this system, an industrial case study is presented. This paper significantly enhances the efficiency and adaptability of sampling systems by introducing VQSSG. The findings and managerial suggestions contribute to a deeper understanding of potential real-world applications, thereby enhancing quality control and decision-making processes.

To cope with counterfeit drugs reaching consumers all over the world, pharmaceutical firms increasingly regard blockchain technology (BCT) as a promising solution to achieve drug traceability. This study proposes a game-theoretic model to investigate the impact of BCT adoption on pharmaceutical firms’ pricing and performance in a competitive market, where one firm offers a branded drug and the other offers a generic version. We consider three contrastive cases with no BCT adoption, unilateral BCT adoption of the branded drug firm and bilateral BCT adoption and characterize consumer traceability awareness, which represents the fraction of consumers sensitive to drug traceability. We find that when BCT is unilaterally adopted, a high consumer traceability awareness level may lead to either higher or lower drug prices. The bilateral adoption of BCT always benefits the generic drug firm due to the enhanced drug traceability, but may damage the branded drug firm attributed to the mitigated differentiation between the two drugs. Compared with the unilateral adoption of BCT, bilaterally adopting BCT can lead to a win-win or lose-lose outcome, depending on the trade-off of drug traceability, drug differentiation and pricing competition. In addition, the adoption of BCT improves consumer welfare when consumers hold a high level of traceability awareness.

This paper considers the problem of integrating cross-docks (CDs) into a given retail supply network with the aim of minimizing related logistics costs. These comprise transportation costs, costs for setting up and operating CDs, inventory holding and purchasing costs. We present a novel mixed integer program (MIP) for optimizing the number and locations of CDs within the supply network as well as the selection of flow type (cross-docking or direct-to-warehouse shipping) and the associated delivery pattern tailored to a specific supplier. A numerical study shows that the original problem is difficult to solve for real-sized instances using a standard MIP solver. We therefore develop a hierarchical decomposition approach that achieves cost-efficient results compared to heuristic approaches, which assume linear transportation costs. In order to demonstrate the model’s applicability to real-life instances and to gain further practical insights we conducted a case study using data from a major European retail company. In this study we found that more than 6% of related logistics costs can be saved by setting up CDs within the supply network. We also show that existing approaches in literature may lead to unfavorable results in certain settings due to an overestimation of transportation bundling effects. Extensive numerical experiments and scenario analyses provide further managerial insights into the problem characteristics.

Climate events are increasingly affecting supply chains, leading to frequent and costly impacts. Managers lack a systematic approach to evaluate risks to individual facilities and employees. We propose a decision support methodology to help quantify the exposure of both to ten most common climate hazards. Using both historical and scenario-based climate data, the methodology distinguishes three dimensions for understanding climate risk: anomaly, extreme variability, and acceleration, applied to each peril from historical to projected data. This approach allows for the isolation of the components of climate change by peril, facilitating a better understanding of each component. Furthermore, it enables the development of adaptative responses tailored to each of the climate dimensions. A case study of a logistics group with more than 200 warehouses across 181 locations in eight European countries illustrates the approach, demonstrating its practicality and effectiveness. Our methodology offers firms, large and small, the opportunity to reinforce their resilience in the face of multiple physical risks. The metrics and scores presented in this paper can be extended to assess the growing issues of climate risks as they apply to occupational health and safety as well as natural resources management.

Shocks caused by COVID-19 pandemic have compelled manufacturers to decrease their reliance upon just-in-time (JIT) and embrace a more just-in-case (JIC) approach. This study clarifies the right configurations of JIT and JIC under low/high upstream and downstream shocks. Drawing upon contingency theory and configuration theory, a framework is developed to differentiate configurations of JIT/JIC under low/high magnitude SC shocks. Survey data from China's manufacturing industry, which experienced SC shocks due to COVID-19, are analysed by regression and sub-group analyses. The results show that only upstream shocks have a negative impact on operational performance. The effect of JIC (but not JIT) on operational performance is strengthened by upstream and downstream shocks. When shocks are high, increasing JIC is effective only when JIT is low. These empirical findings demonstrate that manufacturers can improve operational performance by emphasising JIC models under high SC shocks. Those with high JIT benefit from low JIC under low shock settings.

Curtailing supplier opportunism is a critical concern for both business practitioners and supply chain researchers. However, ambiguity remains regarding the effective deployment of digital technologies (DTs) within supply chain practices to address this issue. Drawing on the asset orchestration perspective, we consider two DT deployment manners (i.e., breadth and depth) and examine their impacts on supplier transparency and supplier opportunism. We investigate 323 Chinese firms and use structural equation modeling to test the proposed hypotheses. We also conduct a scenario-based experiment involving 144 business managers to improve the robustness of our results. We find that although two DT deployment manners positively affect suppliers' cost and relationship transparency, only relationship transparency exhibits a statistically significant negative association with suppliers’ weak opportunism, rather than strong opportunism. More interestingly, of the two DT deployment manners, relationship transparency merely mediates the breadth – weak opportunism association. We advance research on supply chain governance by elucidating the nuanced role of DT deployment in curbing supplier opportunism. We also offer significant insights for firms seeking to reassess their digital supply chain plans, improve engagement with suppliers, and curb unethical practices.