Smart Technologies in Reverse Logistics: A Simulation-Based Performance Analysis
Mots-clés :
Reverse logistics, Industry 4.0, Simulation, system modeling, Moroccan automotive industryRésumé
The growing integration of smart technologies is gradually transforming the supply chain by enhancing its efficiency, visibility and the quality of decision-making. Consequently, if implemented in reverse logistics – which involves managing returned, reused or recycled products – these technological solutions could significantly reduce the challenges associated with coordination, costs and sustainability, thereby improving efficiency. However, prior literature has largely examined these two concepts separately, leaving a significant research gap. With the aim of examining the effectiveness and potential impacts of integrating these technologies, this study uses a simulation model applied to the Moroccan automotive industry to compare different configurations of the reverse supply chain. The analysis begins with a conventional logistics chain operating without smart technologies, then compares this baseline with two smart configurations: one integrating AI-powered sorting (Scenario 2) and another deploying additive manufacturing (Scenario 3). The simulation results reveal performance variations ranging from -31.26% to +21.13%, illustrating the impact of these technologies on profitability, processing times, and environmental sustainability. Comparing the various integration strategies enables us to identify the most effective configurations for utilising smart technologies in the field of reverse logistics. These findings highlight the potential benefits of these innovations and offer food for thought for companies wishing to strengthen their sustainability initiatives whilst improving their operational performance. This study helps to expand the ever-evolving body of knowledge on the digital transformation of supply chain management and supports the development of data-driven strategies to promote circular economy initiatives.
JEL Classification: L2, L25, O33, Q53.
Paper type: Empirical Research
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© Ossama AOUANE, Nadir EL BOUBKARI, Abdelilah CHAHID, Salim GHOUBACH 2026

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