Behavioral Analysis of Human-Machine Interaction in the Context of Demand Planning Decisions

Abstract

The trend of digitalization has led to disruptive changes in production and supply chain planning, where autonomous machines and artificial intelligence gain competitive advantages. Besides, the satisfaction of customers’ wishes has reached top priority for demand-driven companies. Consequently, companies implement digital applications, for instance neural networks for accurate demand forecasting and optimized decision-making tools, to cope with nervous operational planning activities. Since planning tasks require human-machine interaction to increase performance and efficiency of planning decisions, this analysis focuses on forms of interaction to determine the right level of collaboration. The paper outlines various levels of interaction and analyses the impact of human reactions in the context of an industrial demand planning algorithm use case at Infineon Technologies AG conducting a behavioral experiment. The results show that a variance in the levels of human-machine interaction has influence on human acceptance of algorithms, but further experiments need to be conducted to outline an overall framework.