International Journal of Production Economics最新文献

Collective agreements provide all workers with identical break schedules, irrespective of their individual differences in susceptibility to fatigue and recovery speed. In addition, companies tend to limit breaks which is also detrimental to productivity. We consider workers performing a repetitive task, each endowed with an individual fatigue and recovery function. Fatigue slows down the unit processing time which can be restored by appropriate breaks, therefore allowing for the daily production amount to be optimised. The optimised production of each worker can actually be reached for a multitude of break schedules. We therefore develop an algorithm that lists all possible break schedules (enumeration), and selects those leading to the maximum daily output, and the maximum welfare. Welfare is defined by the break duration and their timing flexibility, since we allow breaks to be also taken at the beginning or end of the workday, enabling workers to spend less time in the workplace. Having a variety of schedules would help in meeting at best the workers’ preferences and facilitate a consensus on a specific schedule between managers and workers. The resulting optimised break schedules are compared with common break practices in collective agreements so as to assess their impact on productivity and on worker’s welfare. None of the break scheduling models in the literature considers the option of leaving the workplace earlier, which is nevertheless highly appreciated by workers, nor do these models provide a variety of optimised break schedules to best accommodate workers’ preferences.

The increased interconnection among supply chain participants brought about by globalization has made supply chain management more crucial than ever for gaining a competitive advantage. We examine how supply chain participants respond to feedback on firm performance, with a focus on the effect of contagion on bargaining power. When a company's performance is below expectations, suppliers and customers tend to increase their bargaining power and potentially take actions to safeguard their interests. This is attributed to the decline in the focal company's product market power, resulting from negative performance feedback. Contrarily, positive performance feedback does not cause significant changes in the bargaining power of supply chain firms. Moreover, we explore the role of other stakeholders, such as creditors and investors, in influencing supply chain negotiations. The distrust of other stakeholders leads to the further improvement of the bargaining power of supply chain companies. We analyze the motivation of supply chain stakeholders to improve their bargaining power under performance feedback and develop a complete causal chain and solution on how to enhance future performance prediction to lessen the increase in bargaining power of the supply chain. This paper is both a valuable contribution to the body of knowledge on the financial impacts of business stakeholder decision-making and a cautionary tale for companies that urgently need to cluster their supply chain activities.

No fault found (NFF) is a reported fault for which the root cause cannot be found. An NFF event not only reduces the availability of the equipment to customers but also results in financial losses to the manufacturer. To date, risk due to NFF events is not considered separately in aircraft Maintenance, Repair, and Overhaul (MRO) planning. In this paper, we have studied the NFF event risk identification, distribution, and optimization in an aircraft MRO process based on data collected over three decades of aircraft maintenance. For framework development and formulating the critical NFF risk drivers, this paper applies soft system methodology with detailed semi-structured interviews of industry experts. For NFF risk distribution and optimization, we have used agent-based simulation modeling. The quantitative assessment of more than 5000 NFF cases in an aircraft MRO organization shows that NFF events are significantly non-deterministic during the life cycle of an aircraft. The three identified critical NFF risk drivers in the research are the MRO process stage, aircraft life cycle stage, and human working skills. The MRO process stage is the time of the NFF event during repair activity. The life cycle stage represents the timeline of the NFF event in useful aircraft life. Human working skill represents the role of manpower in the NFF event solution. Further theoretical elaborations of the research lead to the optimized NFF risk break-up at the contract preparation stage and risk driver's sensitivity analysis by the aircraft assembler, operator, and sub-contractor at the commercial bid submission stage.

The manufacturing industry leverages the traceability and visibility of Industrial 4.0 to integrate digital and physical twins for solving the intricacy of a production system. Physical entities can be converted into digital twins with smart IoT (Internet of Things) devices and computational techniques. Research efforts have generally focused on the development of digital twin models for basic practical applications. Advanced applications of digital twins have not been widely reported. An innovative use of digital twins for inventory management has only been analysed theoretically and reported as meta-inventory management for the first time by Wang and Huang (2023). This paper extends the novel concept of meta-inventory by using a theoretical Newsvendor model for original equipment manufacturing (OEM) and own brand manufacturing (OBM) factories. The impacts of meta-inventory on both supply chain members, including the factory and its downstream retailer, are investigated through two kinds of response models (e.g., hybrid and separate response models). Analytical and numerical results show that a factory achieves better performance by using a separate model since it clarifies the responsibility of digital twins. The hybrid response model holds a higher proportion of digital inventory, but its final profits are less than that in the separate model due to fewer orders and higher prices and costs of uncertainty. OBM can better leverage the advantage of digital twins than OEM. Also, both supply chain members benefit from the implementation of meta-inventory out of profit increase, price reduction, and risk hedging. This research provides guidance for manufacturing sites to implement digital twins and reduce their concerns on investing in meta-inventory.

In today’s competitive business landscape, adopting business ecosystems has emerged as a pivotal strategy to enhance supply chain efficiency. This study employs a game-theoretic framework to investigate the dynamics of a manufacturer establishing a business ecosystem by collaborating with a partner on a supply chain platform. The partner, in turn, offers complementary products to consumers, thereby fostering a symbiotic relationship within the ecosystem. Motivated by the need to understand the implications of such business ecosystems, this study explores their effects on firms operating in the competitive market. Additionally, we examine the impacts of two distinct advertising strategies: product-focused and platform-oriented advertising strategies. Our analytical results reveal several interesting findings. First, forming a business ecosystem based on a supply chain platform increases profits for both the manufacturer and the partner. Furthermore, our findings demonstrate that the manufacturer should selectively adopt a platform-oriented advertising strategy based on the magnitude of the platform advertising effect. Moreover, we uncover that the partner’s product-focused advertising strategy fosters a win-win outcome for all firms in the market, ensuring economic sustainability, particularly in the face of intermediate-level market competition. This research provides insightful recommendations for understanding the dynamics of the supply chain, shedding light on the strategic decisions and advertising strategies that drive efficiency within such ecosystems.

Digitalization has revolutionized the supply chain networks but also introduces vulnerabilities to the cyber threats. Notably, in past two years, the cyber-attacks on different organizations worldwide have increased at an alarming rate resulting in significant financial loss to supply chains, intellectual property breaches and supply disruptions. As a result, companies are making significant investments in their cybersecurity. However, many incidents were reported where threat actors attacked a company using the shared digital systems with its suppliers. Therefore, it is important that suppliers are selected based on their cyber resilience. This paper identifies the cyber resilience criteria and proposed a multi criteria decision making based framework to evaluate the cyber resilience of a supply chain partner. It has been also realized that investment in organizational cybersecurity alone is not sufficient to protect supply chains. Companies are now investing in increasing their supply chain cyber capabilities. In this direction, paper also proposes a mixed integer linear program (MILP) to jointly optimize supplier selection and cyber investment decisions in a supply chain based on the supplier's current cyber resilience and potential return on the cyber investments made in selected suppliers. Computational experiments are conducted to study the tradeoffs and impact of sourcing strategy, supplier capacity, cyber security investment decisions on supply chain cyber resilience. The findings underscore that an integrated decision making of supplier selection and cyber investments maximizes the supply chain cyber resilience.

Tracking and tracing devices can provide real-time information from the supply chain and enable digital transformation in the logistics and supply chain industry. In this connection, Logistics 4.0 refers to the potential for information technology and smart connected assets to be used in logistics in the same way as the Industry 4.0 concept which has been applied in operations and manufacturing. This paper analyses current tracking and tracing-focused applications that can provide value for logistics operations through a case study approach.

This study employs an exploratory multiple-case study approach, which is based on interviews with development project stakeholders. The paper analyses three industrial case studies and how tracking/tracing applications are connected to value processes.

The findings of this study show that the value of Logistics 4.0, through the deployment of tracking/tracing applications, is delivered in terms of operational efficiency, visibility, transparency, and safety/security. The payback depends on the volume of transactions, the possibility of reusing the tracking tags, the duration of trips, and the supply chain structure. The paper provides insight into how Logistics 4.0 technology can enhance logistics performance value. Based on the analysis, the study proposes the following potential application domains: (1) intermodal tracking of the shipments for operations control, (2) asset management of containers, and (3) certification of the process steps and authenticity.

Additive Manufacturing (AM) enhances the flexibility of manufacturing networks. In this paper, we present a Location-Production-Routing (LPR) problem designed for a distributed manufacturing platform, where the manufacturing facilities are distributed in different locations with the support of AM technologies. The proposed LPR problem encompasses three different types of decisions: location-allocation, production planning, and product delivery routing decisions. This is one of the first studies that analyzes integrated logistics and manufacturing optimization under distributed and resilient manufacturing platforms. To efficiently solve the complex problem, we design a novel solution method called the Neural Genetic Algorithm (NGA). The numerical experiments show that the proposed method can attain near-optimal solutions, achieving an average gap of 3% with a standard deviation of 1.4% and a 99% improvement in computational time compared to the CPLEX solver. The sensitivity analysis illustrates the high impact of the unit shortage cost on the customer service level and on the distribution of the AM facilities. Moreover, our results for a given instance show that through the periodic reconfiguration of AM supply chains using the proposed LPR model, we can achieve an average cost reduction of up to 25% in the supply network.

To develop novel products in a competitive market, firms are under pressure to reduce their time-to-market by adopting a “concurrent process” that involves an upfront investment to speed up the process by conducting the final stage of development and production simultaneously. While the concurrent approach provides a time advantage, it involves a financial risk because, unlike the “sequential process” under which the production process will begin only after the new product passes the requisite tests, the firm cannot recoup the upfront production-related investment should the product fail the qualification or market test. Given this trade-off and the uncertain success of the product, should a firm adopt the concurrent process in a competitive market with asymmetric market shares and impatient consumers? Also, how would a firm’s development strategy change if the probability of product success can be controlled through research investments?

We provide a 2-stage (or 3-stage) duopoly game for the case when the product success probabilities are exogenously given (or endogenously determined). For both settings, in equilibrium, the concurrent process may be adopted by either one, both, or neither of the firms. Also, even when firms are symmetric, asymmetrical equilibria can emerge that have exactly one firm adopting the concurrent process. When the market-laggard firm has a higher exogenous success probability, a “catch-up” strategy can emerge that has the laggard firm adopt the concurrent process and the market-leading firm adopt the sequential process. This catch-up strategy cannot be sustained as a unique equilibrium when the success probabilities are endogenously determined.

Semiconductor industry plays a critical role for the global economy. Semiconductor industry provides various necessary technologies such as IoT, AI, modern fabrication technologies and so on to various industries including automotive industry, electronic and communication industry, healthcare industry, construction and building industry, space industry, and so on. However, semiconductor supply chain experiences various supply chain related risks and challenges because of its procedural complexities, global supply chain integrations, government policy and regulations, competitiveness, technological complexities, and so on. Not many studies available which investigated the risk, resilience, and complexities regarding green supply chain adoption by semiconductor industry. In this context, the objective of this study is to examine the risks, resilience, and complexities for managing the green supply chain adoption for higher sustainability in the semiconductor industry. Utilizing the TOE framework (Technology-Organization-Environment) and DCV (Dynamic Capability View), we developed a research model to achieve this purpose. Subsequently, this model was validated through structural equation modelling, involving 356 respondents affiliated with the semiconductor industry. This study highlights that technological risk aspects comprising of technological turbulence and risk, compatibility and complexity, organizational dynamic capabilities, and resilience along with appropriate policy and regulations could help successful adoption of green supply chain management in the semiconductor industry.