Recently, there's been a lot of buzz on LinkedIn about Ai and machine learning revolutionizing demand forecasting. Some experts claim these technologies can create forecasts far superior to human efforts, suggesting we should remove human input entirely. At the same time, others argue that simple techniques like six-month moving averages outperform complex models and even machine learning algorithms. But have we taken the concept of Forecast Value Add (FVA) too far?

In the above image, there is a forecastable historical pattern, which is a simple seasonal model that can forecast this very well. But the final forecast is essentially incorrect, and the forecast accuracy is zero because although the final forecast model and the forecasting efforts have been realized or resulted in predicting the right pattern, they are biased. 

They are shifted up exactly to the top and this is based on an upward forecasting bias in the process and the accuracy is zero. A six-month moving average is really a dumb forecast, but obviously it's unbiased. It really cuts through the middle of the data pattern, which results in a positive accuracy.  

The Forecast Value Add (FVA) of the final forecast when compared to the six-month moving average is going to be negative since the final forecast, although the patterns are right, it's biased up. Most of the forecast inaccuracy comes from bias and the bias from the process influences the modeler or it influences the demand consensus process. It has nothing to do with machine learning beating out the six-month moving average, a basic Holt-Winters model could achieve near-perfect forecast accuracy for such a pattern. A six-month moving average is at best a consolation prize.  

It's crucial to examine the details before declaring modeling obsolete or human intervention worthless. The key is understanding when and how to apply different techniques, including human expertise, to achieve the best results.

For the detailed information, please check out the video below:

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