Reciprocal Learning in Production and Logistics

Integration of AI technologies and learnable systems in production and logistics transforms the concepts of work organization and task assignments to human and machine agents. Thus, the question arises of what intelligent machines and human workers may be able to achieve as teammates One answer may be guiding and training the workforce at the workplace to cope with emerging skill mismatches, emphasized by concepts of work-based learning. The extension of cyber-physical production systems towards becoming human-centered and social systems enabling human-machine interaction, creates opportunities for human-machine symbiosis by complementing each other's strengths. In this way, the concept of Reciprocal Learning (RL) between humans and intelligent machines has emerged, which is still rather ambiguous and lacks a profound knowledge base. Especially in production and logistics, literature is fragmented. Hence, the objective of this paper is to conduct a systematic literature review to elicit and cluster the knowledge base in RL represented by adjacent interdisciplinary fields of research, such as social and computer sciences. This work contributes to the literature by developing a comprehensive knowledge base on the concept of RL enabling to pursue future research directions towards the realization of human-machine symbiosis through RL in production and logistics. Copyright (C) 2022 The Authors.

Automated summary

The integration of AI technologies and learnable systems in production and logistics transforms the concepts of work organization and task assignments. Intelligent machines and human workers can guide and train each other in the workplace to cope with emerging skill mismatches. The concept of Reciprocal Learning (RL) between humans and intelligent machines has emerged, but the literature on this topic is fragmented, especially in production and logistics. This paper aims to conduct a systematic literature review to develop a comprehensive knowledge base on RL and contribute to future research on human-machine symbiosis in production and logistics.