Demand Planning for the Digital Supply Chain: How to Integrate Human Judgment and Predictive Analytics

Keywords

demand planning, human–machine integration, forecasting methods

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.

Original Publication Citation

Brau, R.I., Aloysius, J. & Siemsen, E. (2023) Demand Planning for the Digital Supply Chain: How to Integrate Human Judgment and Predictive Analytics. Journal of Operations Management, 69, 965-982. https://doi.org/10.1002/joom.1257

Document Type

Peer-Reviewed Article

Publication Date

2023

Publisher

Journal of Operations Management

Language

English

College

Marriott School of Business

Department

Marketing

University Standing at Time of Publication

Assistant Professor

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