From linear to circular: transitioning supply chains using advanced logistics and closed-loop supply chain theory

This study explores how logistics optimisation can accelerate the transition from linear to circular supply chains, supporting global sustainability goals. Grounded in closed-loop supply chain (CLSC) theory, it investigates how advanced logistics models—such as route planning algorithms, fuel consumption prediction, and emission reduction techniques—can enhance resource efficiency, reduce waste, and close material loops. The research combines five years of historical data on vehicle performance, routes, and fuel use with real-time information on traffic and weather, integrated through public APIs. Using regression analysis for fuel prediction, multi-criteria optimisation for balancing cost and emissions, and dynamic routing algorithms responsive to live conditions, the study identifies strategies for improving both efficiency and sustainability. Results reveal measurable reductions in fuel consumption, emissions, and logistics costs, demonstrating the value of data-driven optimisation in implementing circular economy (CE) practices. By addressing trade-offs between cost, performance, and environmental impact, this research provides actionable insights for organisations seeking to shift from traditional linear models toward more resilient and resource-efficient circular frameworks. Overall, it highlights the critical role of predictive analytics and optimisation tools in operationalising CLSC principles and advancing sustainable logistics management.