To retain old customers and promote sales, firms offer trade‐in programs in which consumers bring in an old product and receive a trade‐in rebate when buying a new one. However, after buying the new product, the consumer who has traded in (the “trade‐in consumer”) may return the new product and claim a refund for it if she/he is not satisfied with it. In this situation, under a full‐trade‐in‐return (FTR) policy, trade‐in consumers receive a generous refund that includes a trade‐in‐rebate for them to redeem if they purchase again in future. Alternatively, some firms have a partial‐trade‐in‐return (PTR) policy under which trade‐in consumers who return a newly purchased product only receive a refund for the amount of money they paid (without including the trade‐in‐rebate). In this study, we build stylized analytical models to explore the optimal choice of a trade‐in‐return policy. We find that there is no difference to the firm between an FTR and a PTR policy when no trade‐in consumers keep unsatisfactory new products. In the case of a relatively medium residual value of the used product, FTR is always the better choice for the firm. When some trade‐in consumers keep unsatisfactory new products, we show that FTR (PTR) is the better choice when the used product's durability is sufficiently low (high). We also show that the firm may not reduce its trade‐in rebate when the “average new product satisfaction rate” of trade‐in consumers increases. In the extended models, we find that, the firm is more likely to prefer PTR to FTR under the online–offline dual‐channel retailing mode, but tends to prefer FTR to PTR when there is a competitive secondhand market, and should make the same optimal trade‐in return policy when there are two selling periods.

Big data analytics is critical in modern operations management (OM). In this study, we first explore the existing big data‐related analytics techniques, and identify their strengths, weaknesses as well as major functionalities. We then discuss various big data analytics strategies to overcome the respective computational and data challenges. After that, we examine the literature and reveal how different types of big data methods (techniques, strategies, and architectures) can be applied to different OM topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis. We also investigate via case studies the real‐world applications of big data analytics in top branded enterprises. Finally, we conclude the study with a discussion of future research.

In the Industry 4.0 era, automation and data analytics emerge as the major forces to enhance efficiency in operations management (OM). Disruptive technologies, such as artificial intelligence, robotics, blockchain, 3D printing, 5G, Internet‐of‐Thing, digital twins, and augmented reality, are widely applied. They potentially will bring a radical change to real world operations. In this study, we first explore several major disruptive technologies, examine the corresponding OM studies, and highlight their current applications in the industry. Then, we discuss the pros and cons associated with the use of these technologies and uncover the potential human–machine conflicting areas. After that, we propose measures which may be able to achieve human–machine reconciles in the coming Industry 5.0 era. A concept of “sustainable social welfare” which includes worker welfare, privacy, etc. is proposed and the roles played by policy makers are also discussed. Finally, a future research agenda, which covers topics in both the Industry 4.0 and Industry 5.0 eras, is established.

Resale platforms such as Swappa and ThredUP, which provide a channel for product‐holders to sell used products, have become common. Interestingly, in the presence of resale platforms, some firms, such as Apple, set lower rebates for the trade‐in‐for‐upgrade (TU) mode instead of implementing the trade‐in‐for‐upgrade‐and‐cash (TUC) mode as Huawei does. In this paper, we build game‐theoretical models to explore how a firm should adjust its trade‐in strategy (e.g., choose pricing and mode selection between TU and TUC) in reaction to the emergence of third‐party resale platforms. We derive several insights. First, we find that using the TU mode helps to encourage consumer repurchases, whereas the TUC mode may have a greater promotion effect on consumers’ first purchases. Second, we show that in the TUC mode, the amount of the trade‐in rebate is not affected by the presence of the resale platform. Differently, in the TU mode, whether the firm should provide a more generous trade‐in rebate depends on the unit product cost when the resale platform is present. Third, in response to the resale platform, the firm should choose the TU mode to take advantage of the platform's promotion effect if the unit product cost is high and choose the TUC mode to avoid the platform's cannibalization effect if the unit product cost is low. To verify the robustness of our findings, we consider the effects of reduced consumer uncertainty and the dynamic pricing mechanism in the extended models. Our main findings concerning trade‐in rebate and mode selection remain valid.

The recent outbreak of Coronavirus disease 2019 (COVID‐19) has posed serious threats and challenges to global supply chain management (GSCM). To survive the crisis, it is critical to rethink the proper setting of global supply chains and reform many related operational strategies. We hence attempt to reform the GSCM from both supply and demand sides considering different pandemic stages (i.e., pre, during, and post‐pandemic stages). In this research paper, we combine a careful literature review with real‐world case studies to examine the impacts and specific challenges brought by the pandemic to global supply chains. We first classify the related literature from the demand and supply sides. Based on the insights obtained, we search publicly available information and report real practices of GSCM under COVID‐19 in nine top global enterprises. To achieve responsiveness, resilience, and restoration (3Rs), we then propose the “GREAT‐3Rs” framework, which shows the critical issues and measures for reforming GSCM under the three pandemic stages. In particular, the “GREAT” part of the framework includes five critical domains, namely, “government proactive policies and measures,” “redesigning global supply chains,” “economic and financing strategies under risk,” “adjustment of operations,” and “technology adoption,” to help global enterprises to survive the pandemic; “3Rs” are the outputs that can be achieved after using the “GREAT” strategies under the three pandemic stages. Finally, we establish a future research agenda from five aspects.