AI Optimal Blending

vyan.ai blends all the candidate forecasts by allocating optimal weights to:

• each of the current cycle machine forecasts

  • across all forecast models

  • across all hierarchy levels

  • across all time buckets

• each of the previous cycle human forecasts

vyan.ai dynamically allocates weights to all machine or human forecasts for a given product / location / customer / lag based on optimizing n-period moving average error or bias during the training part of history (in-sample data). The optimal blending performance is tested during the validation part of history (holdout data). A validated model is then used to make predictions about the future.

This approach is flexible to include as many machine forecasts (traditional 'extrapolative' forecasts like exponential smoothing, advanced Machine Learning / Deep Learning based forecasts such as Neural Networks based, or demand driver based forecasts) and as many human forecasts (customer forecast, sales forecast, marketing forecast, demand planner forecasts, demand planning manager judged forecasts, consensus demand, etc.).

You need a state of the art engine that can perform this optimal blending at all hierarchy level combinations (product family - customer group vs. product group - customer region, etc.) and all time buckets (forecasts in daily/weekly/monthly/quarterly buckets) to autonomously choose the best level in terms of forecastability and still retain all the goodness of human overrides that have helped in the past.