How to Reduce Your Forecasting Error by 35%

This article presents a clear step-by-step approach to reducing your forecasting by up to 35%: collect and clean your data, use machine learning, and enrich your forecasts using the forecast value added framework.

Poor forecasts result in excess inventory, obsolete products, inappropriate long-term capacity planning, supplier tensions, shortages, and emergency expeditions. In short, they harm profitability, frustrate your teams, and waste resources. On the other hand, accurate forecasts will allow you to produce more effectively the required products, ship them on time where they are needed, improve long-term supply planning, and increase your service levels while reducing your inventory levels.

Alchemist, Philips Galle, from Pieter Bruegel (I), 1595–1633.

Thanks to our experience creating models, training, and coaching professionals, we could boil down our technique to improve forecasting accuracy into three main steps, as highlighted in the figure below.

Figure 1 Three steps to reduce your forecasting error by 35%.

Step 1: Data Collection