Machine learning in smart production logistics: a review of technological capabilities

Abstract

Recent publications underscore the critical implications of adapting to dynamic environments for enhancing the performance of material and information flows. This study presents a systematic review of literature that explores the technological capabilities of smart production logistics (SPL) when applying machine learning (ML) to enhance logistics capabilities in dynamic environments. This study applies inductive theory building and extends existing knowledge about SPL in three ways. First, it describes the role of ML in advancing the logistics capabilities of SPL across various dimensions, such as time, quality, sustainability, and cost. Second, this study demonstrates the application of the component technologies of ML (i.e. scanning, storing, interpreting, executing, and learning) to attain superior performance in SPL. Third, it outlines how manufacturing companies can cultivate the technological capabilities of SPL to effectively apply ML. In particular, the study introduces a comprehensive framework that establishes the technological foundations of SPL, thus facilitating the successful integration of ML, and the improvement of logistics capabilities. Finally, the study outlines practical implications for managers and staff responsible for the planning and execution of tasks, including the movement of materials and information in factories.