Why your forecasting model is accurate, but your stock levels aren't.

Building a good forecasting model is one thing. Making an impact is another. Your code checks out, the results look good, and yet a product goes out of stock or the warehouse is full. What’s going wrong?

The value of forecasting lies not in the model itself, but in what you do with it. Three things determine whether you make an impact:

1. seeing actual demand,
2. aligning interests across the organisation,
3. and improving continuously.

1. Are you seeing actual demand?

Many organisations only measure sales. That means they miss actual demand, or unconstrained demand: demand without stock limitations. For example, brand X sells out and sales of brand Y suddenly rise. That looks like a trend, but it’s actually substitution behaviour. Without this data, your forecast remains blind to what customers really want. A practical approach is to show “out of stock” only once a customer clicks on a product. That way, you know there was demand and can immediately offer an alternative. The result: insight into actual demand and a more reliable forecast.

2. Why can’t anyone agree on the forecast?

Sales, finance and supply chain all look at the figures differently. Sales wants growth, finance looks back, and supply chain prioritises certainty. Without a shared starting point, disagreements arise.

The solution is a data-driven forecast as a common starting point. Combine it with expertise from the business, judgemental forecasting, to add context to the figures. This creates a balance between data and experience, while building confidence in the outcome.

3. Forecasting doesn’t stop when you deliver a model

Delivering a model is just the beginning. The question is: does it add value? Are products out of stock less often? Are risks decreasing? You can measure this with Forecast Value Added (FVA). Continuous improvement requires structure. That’s where MLOps comes in: the discipline that keeps models running reliably and learning from new data. From automated deployment to monitoring and adjustments when data drift occurs, it keeps your forecast relevant and fit for the future.

From model to impact

The value of demand forecasting lies not in endless fine-tuning, but in organising it well. Measure actual demand, align interests and keep improving. That’s how you move from a good model to business impact.

👉 Want to know where to start? Or how to strengthen your existing processes with future-proof MLOps?
Book an introductory call, and we’ll be happy to explore the options with you.