This paper develops a framework that provides a structured and comprehensive view of the logistics (sub)processes in e-commerce that can be enhanced through AI-based solutions, highlighting the benefits that their implementation can generate. Additionally, it outlines the major gaps in the related literature and proposes directions for future research. An integrated methodological approach is adopted, combining an initial and primary systematic literature review to classify and synthesise existing knowledge, with a second, interview-based phase conducted in collaboration with practitioners to validate, discuss, and enrich the results. The study illustrates the main logistics processes influenced by AI solutions, showing how they involve both long/medium-term decisions (plans) and operational activities supporting e-commerce order fulfilment (i.e. picking, packing, delivery and return). Furthermore, it discloses the main efficiency factors enhanced by these solutions, and it identifies and classifies the different types of costs that can consequently be reduced. Finally, it suggests directions for future research.

Artificial intelligence applications in e-commerce logistics: mapping process impacts and future research directions

Seghezzi, Arianna;Mangiaracina, Riccardo;Perego, Alessandro
2025-01-01

Abstract

This paper develops a framework that provides a structured and comprehensive view of the logistics (sub)processes in e-commerce that can be enhanced through AI-based solutions, highlighting the benefits that their implementation can generate. Additionally, it outlines the major gaps in the related literature and proposes directions for future research. An integrated methodological approach is adopted, combining an initial and primary systematic literature review to classify and synthesise existing knowledge, with a second, interview-based phase conducted in collaboration with practitioners to validate, discuss, and enrich the results. The study illustrates the main logistics processes influenced by AI solutions, showing how they involve both long/medium-term decisions (plans) and operational activities supporting e-commerce order fulfilment (i.e. picking, packing, delivery and return). Furthermore, it discloses the main efficiency factors enhanced by these solutions, and it identifies and classifies the different types of costs that can consequently be reduced. Finally, it suggests directions for future research.
2025
Logistics
B2C e-commerce
artificial intelligence
digital
innovation
operations
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1309569
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