Why your forecast model is correct, but your stock is not.

Building a good forecast model is one thing. Realizing impact is something else. Everything is correct in your code, the results look good and yet a product runs out of stock or the warehouse is full. Where does it go wrong?

The value of forecasting is not in the model itself, but in what you do with it. Three elements determine whether you make a real impact:

1. see the real question,
2. align interests within the organization,
3. and continue to improve.

1. Do you see the real question?

Many organizations only measure sales. In doing so, you miss the real demand, the unconstrained demand, or demand without inventory constraints. Example: brand X is sold out, brand Y suddenly increases in sales. That seems like a trend, but in reality it’s substitution behavior. Without this data, your forecast remains blind to what customers really want. Smart approach: only show “out of stock” once a customer clicks on a product. That way you know there was demand and can immediately offer an alternative. Result: insight into real demand and a more reliable forecast.

2. Why does no one agree on the forecast?

Sales, finance and supply chain all look at numbers differently. Sales wants growth, finance looks back, and supply chain opts for certainty. Without common ground, discussion ensues.

The solution: a data-driven forecast as a common starting point. Combine that with expertise from the business, judgemental forecasting, to enrich numbers with context. This creates balance between data and experience plus grows confidence in the outcome.

3. Forecasting does not stop at delivering a model

Delivering a model is only the beginning. The question is: Does it add value? Are products less likely to be out of stock? Are risks decreasing? You measure that with Forecast Value Added (FVA). Continuous improvement requires structure. That’s where MLOps comes in: the field that ensures that models continue to run reliably and learn from new data. From automatic deployment to monitoring and adjusting in case of data drift, this keeps your forecast relevant and future-proof.

From model to impact

So the power of demand forecasting lies not in endless fine-tuning, but in smart organization. Measure actual demand, align interests and continuously improve. This is how you make the step from a good model to business impact.

👉 Want to know where best to start? Or how to strengthen existing processes with future-proof MLOps?
Schedule an introductory meeting, we’d love to think with you.