Generative Artificial Intelligence for Reverse Logistics and Closed-Loop Supply Chains

Gerald Schneikart () and Walter Mayrhofer
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Gerald Schneikart: Institute for Digital Transformation and Strategy, FHWien der WKW
Walter Mayrhofer: Institute for Digital Transformation and Strategy, FHWien der WKW

A chapter in Proceedings of the International Conference on Industrial Logistics (ICIL) 2025, 2026, pp 86-93 from Springer

Abstract: Abstract Generative artificial intelligence (gen. AI) is quickly improving its capabilities and constantly extends its areas of application, one of which is logistics. Major issues in logistics are a high environmental burden due to CO2 emissions and waste production from one-way transport items, e.g., cardboard-based boxes. While environmentally friendly circular economy solutions based on returnable transport items (RTI) exist, the adoption of suitable reverse logistics processes is sluggish. There is considerable potential to expedite a transition to RTI-based logistics, using gen. AI tools for planning and data analysis. This review paper provides an overview of the most recent scientific publications about the application of gen. AI in reverse logistics and closed-loop supply chains. It will identify the most promising gen. AI use cases for further consideration in research and development and provides a critical assessment of their potential impact.

Keywords: Closed-Loop Supply Chain; Generative AI; Reverse Logistics (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-14489-8_9

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DOI: 10.1007/978-3-032-14489-8_9

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