Rolling planning-based closed-loop supply chain logistics model under an unstable demand scenario

The supply chain management (SCM) network is struggling to enhance profitability while managing shorter-lived products, particularly electronic items like cell phones. This challenge arises because customers continuously upgrade to new models within a very short timeframe. As a result, maintaining an optimal inventory level in retail outlets becomes difficult to prevent stock-outs and overstock situations in a dynamic market. Furthermore, electronic waste (e-waste) increases significantly, raising additional environmental concerns. This study develops a new closed-loop supply chain (CLSC) logistics model to tackle these issues. This CLSC model is designed by integrating a new production approach known as rolling planning production schedules (RPPS). The main feature of the RPPS is that real market demands dictate the manufacturing quantity at the factory. Additionally, RPPS lead time is included in developing the proposed CLSC model, replacing simultaneous flow systems with periodic flow systems of goods from one stakeholder to its successive stages. Also, K -means algorithm was applied to cluster stores for efficient shipment. Afterward, a second alternative model was also developed to compare the performance of the suggested model, where a fixed production volume manufacturing approach was utilized instead of the RPPS. The findings indicate that the proposed model yields better results than the second alternative. Moreover, by adjusting the model's parameters, a sensitivity analysis is conducted to confirm the model's robustness and the reliability of the results. The sensitivity study demonstrated that the suggested model can produce consistent outcomes when a large-scale problem size and a recurrent simulation environment are employed. • A CLSC model was developed using Rolling Planning Production Schedules (RPPS). • The model minimizes logistics cost under unstable and dynamic market demand. • Lead time and periodic flow replace simultaneous delivery for realism. • K-means clustering optimizes store shipments and inventory distribution. • Sensitivity analysis confirmed model robustness across varying scenarios.