Human-AI-Collaboration in the Context of Information Asymmetry – A Behavioral Analysis of Demand Forecasting

Abstract

Digitalization enables the full potential of Artificial Intelligence for the first time. This study deals with demand forecasting as a representation of supply chain planning. Statistical and judgmental approaches constitute the state-of-the-art in methodology, but also present drawbacks such as human mental capacity constraints or data biases. Teaming of humans and AI promises synergies and better solutions, but challenging questions on how to organize collaborative tasks remain. Information asymmetry states an unsolved issue, as digitalization is going to take more time to be holistically established. Deploying a behavior analysis of an industrial case study, this paper investigates the impact of two different forms of interactions on the forecasting performance and ability of human planners to compensate the lack of contextual information included in an AI-based prediction. The results indicate that information asymmetry limits the magnitude of the decision-making anchor provided by the algorithm and affects the accuracy depending on the specific interaction form. Overall, an asymmetric sequential interaction set-up outperforms the other forecasts. Finally, this study states implications and limitations for human-AI collaboration.