Why forecasting policy should start simple — but not stay simple forever

When starting a forecasting policy, there is a tendency to choose the latest and greatest model in the world.

This is where failure happens, as people generally do not consider the governance overhead.

The goal in demand forecasting is not just model performance. The goal is to create a system that can be understood, trusted, monitored, and improved.

This is where simple models help the most.

They are easier to explain, benchmark, and monitor. Simple models give the organization a baseline. They also make the cost of complexity visible when complexity is added later.

If a simple policy performs well enough, then adding complexity may not create much value. However, once the system starts to show where the simple policy fails, staying simple might become expensive.

As shown in the previous articles, one such place where a simple policy can fail is when demand patterns are not the same across products.

Different demand patterns lead to different forecasting problems.

A simple model may be good for one segment and weak for another.

What happens in the maturity process of a forecasting policy is a shift in the question being asked.

It shifts from:

What is the simplest forecasting policy that works?

to:

Where is the simple policy creating avoidable business cost?

This shows that the forecasting policy is maturing.

If the system starts complex too early, it creates governance burden without knowing where complexity is actually needed.

If the system stays simple for far too long, then it ignores obvious avoidable costs like inventory, service-level risk, or planner trust.

This is why the forecasting process has to evolve from simple defaults to deliberate segmentation.

It has to start with a simple policy, then measure where it works and where it does not.

Complexity should be added only where it supports a business decision.

The aim at the start of a policy is to create trust with the least governance overhead.

Adding complex AI models will not help at the start of a forecasting policy.

Like all things, one needs to evolve the forecasting policy as per need, so that complexity is not added for sophistication, but for improvement.