Optimising bin-driven used product take-back supply chain: A circular economy approach to ESG performance

Businesses are increasingly adopting product take-back programmes as part of their environmental initiatives. A frequently used method involves setting up bins where consumers can return their used products. Despite their growing popularity, these programmes have received limited research attention. This research extends take-back literature by incorporating novel bin configuration considerations and evaluating the economic benefits of volume reduction technologies. We develop a detailed mathematical framework to determine the optimal reverse supply chain (RSC) strategies for take-back. We validate our model through a real-world case study implementation. Furthermore, by testing it across diverse global scenarios with various parameter combinations, we generate broadly applicable findings that advance both theoretical understanding and practical implementation. Our analysis reveals that optimal bin configuration varies by returns volume: mixed product streams in a single bin prove more economical at low-volume sites, while separate collection bins become advantageous at high-volume locations. The implementation of shredding technology emerges as a crucial cost-reduction strategy, that is particularly effective when deployed at high-volume storage facilities. For a wide range of experiments, transportation costs represent approximately half of optimised total expenses. Higher contamination levels in bins correlate with reduced transportation expenditure, but this brings increased disposal fees and negative environmental impact. Our findings recommend maintaining a mixed inventory of shredded and non-shredded materials at storage facilities, with selective transportation to recycling centres. The proposed models and recommendations provide practical decision-making tools for businesses seeking to optimise take-back operations, contributing to the broader implementation of circular economy principles for environmental performance.