The circular economy meets artificial intelligence (AI): understanding the opportunities of AI for reverse logistics

Abstract

Purpose

Technology is an important force in the entrepreneurial ecosystem as it has the potential to impact entrepreneurial opportunities and processes. This paper explores the emerging technology of artificial intelligence (AI) and its implications for reverse logistics within the circular economy (CE). It considers key reverse logistics functions and outlines how AI is known to, or has the potential to, impact these functions.

Design/methodology/approach

The paper is conceptual and utilizes the literature from entrepreneurship, the CE and reverse logistics to explore the implications of AI for reverse logistics functions.

Findings

AI provides significant benefits across all functions and tasks in the reverse logistics process; however, the various reverse logistics functions and tasks rely on different forms of AI (mechanical, analytical, intuitive).

Research limitations/implications

The paper highlights the importance of technology, and in particular AI, as a key force in the digital entrepreneurial ecosystem and discusses the specific implications of AI for entrepreneurial practice. For researchers, the paper outlines avenues for future research within the entrepreneurship and/or CE domains of the study.

Originality/value

This paper is the first to present a structured discussion of AI's implications for reverse logistics functions and tasks. It addresses a call for more research on AI and its opportunities for the CE and emphasizes the importance of emerging technologies, particularly AI, as an external force within the entrepreneurial ecosystem. The paper also outlines avenues for future research on AI in reverse logistics.

Citation

Wilson, M., Paschen, J. and Pitt, L. (2022), "The circular economy meets artificial intelligence (AI): understanding the opportunities of AI for reverse logistics", Management of Environmental Quality, Vol. 33 No. 1, pp. 9-25. https://doi.org/10.1108/MEQ-10-2020-0222