Six Tips for Success Using Machine Learning for Demand Planning
Guidance on applying machine learning to demand planning, including objectives, data dimensions, and operationalization for supply chain teams.
This white paper outlines six practical tips for integrating machine learning into demand planning processes. It covers setting business objectives, starting with probability forecasting before adding external data, managing data volume, granularity, quality and variety, operationalizing self-adaptive models, and involving the right people. Includes a case example from dairy producer Granarolo showing forecast and inventory improvements.
Define specific business objectives and baseline metrics before starting machine learning projects
Begin with probability forecasting on historical data, then layer external causal factors
Address four data dimensions: volume, granularity, quality, and variety for model effectiveness
Use self-adaptive models integrated into planning systems rather than isolated science projects
Combine machine learning automation with planner domain expertise for sustainable results
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