Demand planning for the digital supply chain: How to integrate human judgment and predictive analytics

Abstract

Our research examines how to integrate human judgment and statistical algorithms for demand planning in an increasingly data-driven and automated environment. We use a laboratory experiment combined with a field study to compare existing integration methods with a novel approach: Human-Guided Learning. This new method allows the algorithm to use human judgment to train a model using an iterative linear weighting of human judgment and model predictions. Human-Guided Learning is more accurate vis-à-vis the established integration methods of Judgmental Adjustment, Quantitative Correction of Human Judgment, Forecast Combination, and Judgment as a Model Input. Human-Guided Learning performs similarly to Integrative Judgment Learning, but under certain circumstances, Human-Guided Learning can be more accurate. Our studies demonstrate that the benefit of human judgment for demand planning processes depends on the integration method.