Interaction Between Automation and Humans in Supply Chain Planning

Abstract

Automation is transforming supply chain planning (SCP) processes, replacing human tasks with technological solutions. While automation offers efficiency gains, the interaction between humans and automated systems presents behavioral challenges. This study investigates how decision-makers in SCP processes learn to correct errors when interacting with automated demand forecasting systems versus human planners. Drawing from psychology and behavioral theories, we examine the effects of interaction type (automated system vs. human) on learning, operationalized as performance improvement over time. Further, we analyze whether this relationship is moderated by cognitive psychological traits (positive attitude towards technology, technology anxiety/dependence) and socio-psychological traits (social influence based on subjective norms, social influence based on image) of the human decision-maker. Our article contributes to supply chain management research by introducing automation-human interaction, providing a temporal learning perspective on performance, and integrating cognitive and socio-psychological moderators. Insights are offered on how to facilitate effective human-automation collaboration by managing socio-psychological influences. Limitations and future research opportunities, including cultural contexts and artificial intelligence, are discussed.