Growing environmental awareness and widening extended producer responsibility have heightened the need for economically, environmentally, and socially sustainable business strategies levered by digital technologies. As an extension, various take-back policies focusing on product waste and recovery are put in place by the high-tech manufacturing industry. With an attempt to increase sales while ensuring the environmental sustainability of products, trade-in programmes that incentivize consumers to exchange used goods for new and most recent technology products became a value-adding strategy for businesses. Due to the high unpredictability in the quality of returned devices however, determining trade-in margins is a challenging task for original equipment manufacturers (OEMs). This inevitably reveals the need for incorporating intelligent technologies into the formation of manufacturing and logistics architectures to simultaneously preserve OEMs profitability and ensure the sustainable development of the closed-loop supply chain activities. With this motivation, this study presents the use of IoT-embedded products in a blockchain-enabled disassembly-to-order system to determine the optimal trade-in-to-upgrade policy. A discrete-event simulation model is developed to obtain the expected cost of the disassembly-to-order system. Optimal incentives for varying product qualities are then computed by utilising this cost in the trade-in policy model.

Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.

The concept of efficiency has always been and will continue to be important for competitive business environments where limited resources exist. Owing to the growing complexity of organizations and economy in general, this trend is expected to continue to remain a high priority for organizations. Continuous performance evaluations that utilize both qualitative and quantitative information play a significant role in sustaining efficient and effective business processes. Therefore, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency in various industries. Majority of these models, however, focus solely on quantitative criteria, avoiding the interrelations and dependencies between qualitative and quantitative measurements. Furthermore, these methodologies tend to utilize discrete and contemporary information eliminating historical performance data. With these motivations, this paper proposes an integrated approach combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and artificial neural network (ANN) methodologies for performance evaluation. In the proposed model, DEMATEL and ANP methodologies are utilized in a group decision-making concept to obtain priorities of the evaluation criteria. Following this, an ANN model is designed and trained with historical performance data collected from the organization and the results of the fuzzy DEMATEL-ANP model. The outcomes include the relational data among the criteria and alternatives used in the model in addition to their relative rankings. A food industry case study is presented to demonstrate the steps of the proposed model.