A Review on the Use of Artificial Intelligence in Reverse Logistics

Abstract

This study investigates the role of machine learning (ML) and artificial intelligence (AI) in reverse logistics (RL) within the scope of e-commerce and e-waste. RL is a process to plan and manage the flow of products from consumer to provider with the purpose to generate value. From the bottom line of sustainability, it is important to manage and improve the product return and end-of-life product. Past research studies have presented several techniques to optimize the cost, schedule, and route. However, the integration of ML and AI tools in information technology systems is limited and insufficient. The purpose is to present the research gap for further study. The article is organized as follows: Section 1 provides an introduction to reverse logistics, ML, AI, e-commerce, and e-waste. Section 2 presents a comprehensive review of the current research, and Section 3 concludes the article.

A system for anomaly detection in reverse logistics: an application into an e-commerce company

“…Finally, in the fifth stage, the returned and inspected products that are refurbishable go from the inspection center to the refurbishing center, whereas those ones that are recyclable go from the inspection center to the recycling center. Sinha et al (2023) investigated the role of machine learning and artificial intelligence in reverse logistics within the scope of e-commerce and e-waste. Their study aims at overcoming the traditional techniques that optimize the cost, schedule and route of returned goods by highlighting how modern technologies exploiting machine learning and artificial intelligence can support reverse logistics process.…”

“…In this way, the consolidated research devoted to optimize the execution of reverse logistics can be complemented by new research efforts devoted to prevent the use of reverse logistics. Indeed, limited studies have been conducted to analyze the flows of data and information generated by the reverse logistics process to reduce the number of returned products by reorganizing the supply chain and increasing the quality of decision-making (Dutta et al, 2020;Krapp et al, 2013), especially by adopting artificial intelligence techniques (Sinha et al, 2023). Such preventive approach to reverse logistics allows to introduce new performance indicators, whose measuring and improvement is highly complex (Davidavi cien _ e and Al Majzoub, 2021; Shaik and Abdul-Kader, 2018;Sudarto et al, 2017;Euchi et al, 2019).…”

“…Sinha et al (2023) investigated the role of machine learning and artificial intelligence in reverse logistics within the scope of e-commerce and e-waste. Their study aims at overcoming the traditional techniques that optimize the cost, schedule and route of returned goods by highlighting how modern technologies exploiting machine learning and artificial intelligence can support reverse logistics process.…”