Demand planning for the digital supply chain: How to integrate human judgment and predictive analytics
通过实验室实验和实地研究，比较了多种整合人类判断与统计算法的方法，提出并验证了“人类引导学习”方法在需求规划中的更高准确性。
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.