Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice

Abstract

Artificial intelligence and machine learning are key technologies for purchasing organizations worldwide and their usage is still in a nascent stage. This systematic review offers an overview of the state-of-the-art literature and practice, where 46 works meeting the inclusion criteria were interactively classified in 11 use case clusters. The work follows the content analysis approach where the material evaluation was empirically enriched with 20 interviews to assess the cluster's business value and ease of implementation through triangulation. This is the first systematic review in the area of operations and supply chain management utilizing the Computer Classification System as the de facto standard in computer science for clarity in the terminology of these emerging technologies. In matching the literature search with the interview results, a mismatch was found between the reviewed literature and the expert's assessments. For instance, the cluster cost analysis deserves higher research attention as well as supplier sustainability. Moreover, there seems to be a gap in the operational area, which many believe to be first considered due to data availability. The insights may guide researchers and executives to better understand the dynamic capabilities needed to successfully steer the organization in the transformation toward procurement 4.0.