How to win (with the best possible Statistical Forecast)
Just as a strong start is crucial in a 100-meter dash, having the most accurate statistical forecast is key for effective demand planning. Are you confident that your current statistical forecast is the best it can be?
Why 'Pick-Best' fails: Many forecasting systems use a 'pick-best' approach, selecting a specific model x or y or z (that showed the least error in historical tests). However, this method often falls short because past performance (plagued by overfitting) is no guarantee to deliver similar good results in future. In practice, a composite forecast—blending various models (e.g., 10% of model x, 20% of model y, 30% of model z)—tends to deliver better results.
Why Composite Forecasting does not scale: While your forecasting software might offer such a composite forecasting option, it often requires manual input to define forecast model specific weights. This is impractical at scale, given numerous SKU/location level time series. Moreover, these weights should be dynamic and updated each cycle. This is just not designed to be a task for humans to deliver.
How to succeed: To achieve the best possible statistical forecast, you need an AI system that can autonomously generate time series-specific and lag-specific weight blends every forecast cycle. This needs to be done for all the forecasts in your Forecast Universe (which means all the human forecasts as well as all the machine forecasts from all models at all levels in all time buckets). This AI Optimal Forecast approach can significantly reduce avoidable errors compared to current approaches to statistical forecasting.
Learn more: Join us for our upcoming webinar on AI Optimal Forecasting on September 24th, 2024. You can register here.
