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 language | English |
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Pages (from-to) | 965-982 |
Number of pages | 18 |
Journal | Journal of Operations Management |
Volume | 69 |
Issue number | 6 |
Early online date | 15 May 2023 |
DOIs | |
Publication status | Published - 30 Sept 2023 |
Keywords
- behavioral experiment
- demand planning
- digitization
- field study
- forecasting
- human judgment
- machine learning
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering