International Journal of Production Economics最新文献

Prefabricated construction is increasingly applied in the construction industry. Recent academic and industrial efforts highlight that Digital Twin as a Service (DTaaS) is an innovative solution to strengthen the advantages of prefabricated construction by enabling continuous monitoring, precise control, and optimized maintenance of each unique module. However, the complexity of digital twins presents significant challenges for both platforms and subcontractors to apply and fully understand this technology. It is, therefore, crucial to develop a well-designed sharing environment where stakeholders volunteer to share their digital twin resources for synergistic benefits. This research proposes an innovative blockchain-based fine-grained digital twin sharing framework, with detailed architecture focused on two key stages of prefabricated construction: on-site construction and logistics involving warehousing and transport. An incentive mechanism is introduced to encourage all the stakeholders engaged in digital twin sharing framework to contribute and share more digital twin resources. Evolutionary and cooperative games explore the effective settings of the incentive policies, aiming to ensure the feasibility and sustainability of the proposed system by motivating users to share digital twins with fine granularity. Mathematical and numerical results demonstrate the optimal subsidy policies with/without budgetary constraints and further highlight the effectiveness of well-crafted incentives. Findings reveal that better performance in terms of profitability and digital twin sharing is achieved under the proposed incentives. Subsidies are primarily allocated to offset costs with a certain budget, whereas the emphasis shifts towards quality improvement under an ample budget. This study lays a solid foundation for digital twin reuse and development.

In the realm of e-commerce, various return freight modes are available, such as platform-bearing return freight (PP strategy), retailer-bearing return freight (RP strategy), and consumer-bearing return freight (CP strategy). The return freight strategy adopted by platforms plays a crucial role in responding to consumer behaviors. To analyze how platforms determine the optimal return freight strategies, we examine a supply chain comprising a platform, a retailer, and heterogeneous consumers, employing a Stackelberg game model to capture their decision dynamics. Our findings suggest that the platform should opt for the PP strategy only when the product profit margin is relatively low. If the platform refuses to adopt the PP strategy, then the RP strategy is preferable when consumers face extremely high consumer return freight costs; otherwise, the CP strategy is the equilibrium result. We also prove that the selection of the return freight strategies leads to changes in the property of the equilibrium. Under the CP strategy, how the product fit probability affects the optimal price depends on the procurement cost; while under the RP and PP strategies, the optimal price decreases with the fit probability. Moreover, under the PP strategy, an increase in the product fit probability does not always benefit the platform. Further, our analysis reveals that, while the PP strategy can reduce the retailer’s risk, it might negatively impact supply chain performance, which implies that cost sharing in the supply chain does not necessarily lead to a higher channel profit. We also investigate how the platform’s return freight strategies affect consumer surplus and identify regions for Pareto improvement where all parties benefit from the PP strategy. Finally, by exploring extensions like positive salvage values, partial refunds, platform competition, and differentiated return freight costs, we confirm the robustness of our findings and unveil new insights.

Quality function deployment (QFD) is a team-based product development approach for identifying critical engineering characteristics according to customer requirements to improve customer satisfaction. Nonetheless, there are some limitations in the current QFD methods when used in real-world situations. For example, most of them cannot handle experts’ conflict opinions regarding the correlation evaluations between customer requirements and engineering characteristics, and only focus on how to acquire a complete importance ranking of engineering characteristics which is unnecessary in many realistic cases. In this study, we aim to develop a new QFD approach by using the cooperative game consensus reaching process and three-way decision theory. First, the cooperative game-based consensus mechanism is applied to help experts reach a consensus on the correlation assessments between customer requirements and engineering characteristics. Then, an extended three-way decision method is adopted to obtain the importance classifications of engineering characteristics and identify the critical ones for product improvement. Finally, the proposed QFD approach is illustrated with a case study regarding renewable energy storage technology improvement, and its efficiency and advantages are verified via comparative analysis and simulation analysis.

Crowdfunding is a new financing method that is widely utilized by startup firms to substitute or complement bank loans. We consider the optimal financing problem for a profit-maximizing startup firm, given that both the crowdfunding demand and retail market demand are uncertain and correlated. We find four types of financing strategies: single-source (crowdfunding only) and dual-source (crowdfunding and bank loan) financing, with prudent or aggressive goal-setting in crowdfunding. The four strategies each can be optimal for a specific parameter range with respect to the uncertainty ratio of crowdfunding and retail markets and the cost of crowdfunding. Upon deriving optimal strategies, we analyze comparative statics for the strategies and optimal profits. We show that, among other interesting results, market expansion (through either crowdfunding or the retail market) always calls for a higher crowdfunding goal but may raise or reduce the optimal bank loan depending on which market expands, and market expansion generally increases optimal profit except when the crowdfunding market grows too large which intensifies the overfinancing risk. Moreover, a high correlation between crowdfunding and the retail market does not necessarily favor the use of crowdfunding capital, nor does it always increase profits, and the impacts must be co-examined with the underfinancing and overfinancing risks.

The semiconductor industry, a cornerstone of modern technology, has been crucial in driving globalization and supporting various sectors, from consumer electronics to automotive industries. However, in recent years, the industry has faced substantial challenges threatening its ability to meet the surging demand for semiconductor chips. Disruptions at any point in the supply chain, from raw material sourcing to end-product delivery, can substantially influence the semiconductor ecosystem. The intricate nature of such SCs makes them highly vulnerable to various disruptions, emphasizing the critical need for building resilient and reliable supply chain strategies. This article presents comprehensive research aimed at addressing critical gaps in the understanding and management of resilience and reliability within the semiconductor supply chain (SSC). This study proposes a multi-objective mixed-integer non-linear programming (MO-MINLP) model to configure an SSC while considering reliability and resilience measures. It emphasizes and draws attention to the importance of resilience and reliability in managing SSC disruptions during a pandemic and potential future epidemic outbreak. Exploring the precise breakdown of batch transportation between two sites shows how disruption can affect product flow along the SC. The applicability of the proposed method is demonstrated through a numerical example of an SSC, solved using the LINGO solver. Finally, a sensitivity analysis is conducted on the model's parameters to assess the capability and effectiveness of the results from managerial viewpoints.

Implementing blockchain technology to improve traceability can enhance consumer trust in green supply chains. However, the associated high fixed costs can pose challenges for companies when choosing the implementation mode. In this study, we investigate the value of blockchain adoption and explore a manufacturer’s choice between third-party or self-implementation. Specifically, we consider the traceability level and the greenness degree in a supply chain comprising a manufacturer, a retailer, and a third-party platform providing blockchain services. Our findings indicate that self-implementation by the manufacturer increases the degree of greenness, and the retailer’s involvement in decisions about traceability levels improves the manufacturer’s traceability level. The highest profits for supply chain members are achieved when both parties opt for self-implementation. Additionally, self-implementation outperforms third-party platform implantation when incentivizing the manufacturer to invest more in green and blockchain technologies. Consequently, blockchain technology’s value exceeds its costs, encouraging supply chain members to select self-implementation whenever possible. However, when the fixed costs or the cost coefficient of green investment or traceability are excessively high, the manufacturer may need to outsource blockchain services to a third-party platform. Furthermore, through comprehensive system dynamics analysis, we find that blockchain’s adoption or non-adoption and imposing an investment cap on the product’s green degree have a little effect on profitability for suppliers, manufacturers, logistics providers, and third-party platforms.

With the diffusion of Industry 4.0 technologies, firms can decentralize operational decisions, fostering a data-driven Digital Transformation (DT) across all organizational levels. Digitalized shopfloors can leverage an unprecedented availability of data for better and faster decision-making, resulting in enhanced operational performance. However, limited research has investigated the organizational and individual implications for those who run the manufacturing lines: production managers, supervisors, team leaders, and workers. By adopting a socio-technical framework, this study aims to disentangle the effects that digitalization has on shopfloors’ organizational structures, decision-making processes, and individual competencies, as well as the interdependencies among them. An exploratory approach was adopted, based on an empirical cross-country study involving 34 semi-structured interviews conducted in the Italian and Spanish automotive sectors. Analyzed through the lenses of information-processing, knowledge-based, and dynamic capability theories, our findings reveal through five propositions how digitalization induces a “polarization” of operational decision-making: shopfloors are run by knowledgeable data-empowered production managers and autonomous information-processing team leaders on the front line, with a reduced importance of supervisors. Upskilling needs appear for team leaders but not production workers, whose involvement, however, emerges as a key factor for a successful digitalization and overall performance in initial DT stages. This study contributes to literature on digitalization by exposing managerial tensions and dynamic capabilities, along with a deeper understanding of the micro-foundations of DT in terms of implications for shop-floor decision-makers. Managerial implications are directed at creating awareness about the centrality of production team leaders, and future research avenues are proposed.

This study examines the resilience and sustainability of supply chains amid global disruptions, with a particular focus on the essential role of reverse logistics. Through a game-theoretic approach, we explore manufacturer decisions to source from either reliable but expensive raw materials or cost-effective yet riskier recycled or recyclable materials from the reverse logistics channel. Our analysis outlines three primary sourcing strategies: sourcing exclusively from suppliers (SS), sourcing solely through retailer reverse channel (RS), and a balanced dual sourcing (DS) approach. Our findings reveal the economic viability that recycling outsourcing is influenced by market demand and disruption risks. Notably, in scenarios of constrained market potential, the cost advantage of using recycled materials from less reliable reverse logistics channels surpasses the risks associated with supply chain disruptions, suggesting a complex cost-benefit landscape amidst supply uncertainties. Moreover, the stability of suppliers emerges as a pivotal factor in strategic sourcing decisions, underscoring the need to consider both economic efficiencies and supply reliability. The study also evaluates the dynamic competition between manufacturers and retailers, shedding light on how strategic adjustments driven by sustainability and resilience goals can enhance profitability and sustainability. It was found that despite the threat of disruptions, manufacturers benefit more from engaging with risky reverse channels under specific conditions, underscoring the nuanced decision-making required in uncertain supply scenarios. This research advances sustainable supply chain management by highlighting strategic complexities and the need for understanding economic efficiencies and supply stability, offering insights for navigating disruptions and fostering resilient, sustainable supply chains.

Semiconductor manufacturing enterprises face potential risks of supply disruption owing to economic and political factors. To enhance supply stability and industry sustainability, group manufacturing enterprises can establish internal suppliers to supply components exclusively to internal OEMs. However, under the competition from external suppliers and OEMs, it is unclear how group manufacturing enterprises decide on procurement strategies. We construct a two-echelon supply chain game model consisted of four supply chain stakeholders and analyze the group manufacturing enterprise's decision-makings on developing an internal supplier and procurement strategies. We obtain some remarkable findings. The optimal procurement decisions of group manufacturing enterprise, the internal OEM, and the internal supplier are different. Decision makers should consider which enterprise's profit is more important and choose the procurement strategy preferentially. Group manufacturing enterprise, the external supplier, and the competitive OEM cannot achieve a triple-win situation. Even if the internal supplier's technical level is high, the group manufacturing enterprise and the internal OEM cannot gain competitive advantages in the internal-sourcing scenario. Group manufacturing enterprise needs to pay more attention to the environmental impact. Furthermore, we extend the basic model and analyze the robustness of the conclusions.