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.

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.