Organizations often face challenges that limit the effective use and adoption of AIML in demand planning systems. To address this issue, we focus on the role of such systems that analyze demand forecasts to make inventory order decisions. This study investigates the role of human intervention in AIML-driven predictions. By doing so, we distinguish between three different types of human-AIML collaboration: automation, adjustable automation, and augmentation. Based on a field experiment involving AIML-driven demand forecasts of about 1900 stock-keeping units in the retail industry, we rely on a multivalued treatment effect methodology to measure the effects of human-AIML collaboration on forecast accuracy and demand planning. In addition, we find that demand uncertainty has a strong influence on demand planning decisions. We discuss implications for extant theory and propose a framework outlining the conditions in which human intervention is most likely to add predictive value to human-AIML collaborations. Our study sheds light on AI contextualization and adoption in managerial practice and the role of algorithm aversion in Human-AIML collaboration.
Research Approach (methodology): Field Experiments in real settings, Causal Inference, Mediation-moderation Analysis.
References:
Saenz M.J., Revilla E., and Simon C. (March 2020). Designing AI Systems with Human-Machine Teams. MIT Sloan Management Review.