Demand Forecasting Methods: From Statistical Models to AI-Powered Predictions

⚡ Key Takeaways

• No single method is universally best — A tiered approach using simple methods for slow-movers, ML for high-volume items, and demand sensing for short-term adjustments often produces the best results. 𝕏
• ML models outperform in complex scenarios — Gradient boosting and deep learning models handle nonlinear relationships, hundreds of features, and cross-product learning better than traditional statistical methods. 𝕏
• Process matters more than algorithms — Regular forecast reviews, bias measurement, and cross-functional collaboration between planning and sales drive more improvement than switching algorithms alone. 𝕏