Simulation Models for Reverse Logistics Decisions: Insights from a Case Study of Diesel Particulate Filter Remanufacturing

Abstract

Remanufacturing is vital in circular business models, offering a sustainable way to restore products and reduce resource consumption. In the automotive industry, remanufactured parts are commonly used as spare parts, although their volumes in the market remains limited compared to new spare parts. One main challenge in increasing adoption of remanufacturing is that economic and environmental effects of reverse logistics operations are unknown. This research develops a simulation-based decision support tool for assessing reverse logistics operations in the automotive aftermarket, evaluating the economic and environmental impact of remanufactured versus newly produced spare parts. Using a combination of agent-based and discrete event methods, the simulation analyzes the effectiveness of Scania’s reverse logistics network using diesel particulate filter as case study. The findings demonstrate remanufacturing advantages in cost (-82%), carbon footprint emissions (-92%), and virgin material savings (-99%) over new production, therefore supporting the integration of remanufactured parts into circular business models.