Measuring Forecast Accuracy

WHAT IS A GOOD LEVEL OF FORECAST ACCURACY?

“What would you consider a good level of forecast accuracy in our business?” is probably the single most frequent question we get from customers, consultants and other business experts alike. Apparently, we think that it is not the right question to ask. Firstly, because in any supply chain planning context, forecasting is always a way to an end, not the end itself. We need to keep in mind that a forecast is relevant only in its capacity of enabling us to achieve other goals, such as improved on-shelf availability, reduced food waste, or more effective ranges.

Secondly, although forecasting is an important part of any planning activity, it still represents only one aspect in the planning machinery, meaning that there are other factors that may have a significant impact on the outcome. Sometimes the importance of accurate forecasting is truly critical, but from time to time other factors are more important to attaining the desired results.

We are not saying that you should stop measuring forecast accuracy altogether. It is an important tool for root cause analysis and for detecting systematic changes in forecast accuracy early on. However, to get truly valuable details from measuring forecast accuracy you need to understand:

1. The role of demand forecasting in attaining business results. Forecast accuracy is crucial when managing short shelf-life products, such as fresh food. However, for other products, such as slow-movers with long shelf-life, other parts of your planning process may have a bigger impact on your business results. Do you know for which products and situations forecast accuracy is a key driver of business results?

2. What factors affect the attainable forecast accuracy? Demand forecasts are inherently uncertain; that is why we call them forecasts rather than plans. In some circumstances demand forecasting is, however, easier than in others. Do you know when you can rely more heavily on forecasting and when, on the contrary, you need to set up your operations to have a higher tolerance for forecast errors?

3. How to assess forecast quality. Forecast metrics can be used for monitoring performance and detecting anomalies, but how can you tell whether your forecasts are already of high quality or whether there is still significant room for improvement in your forecast accuracy?

4. How the main forecast accuracy metrics work. When measuring forecast accuracy, the same data set can give good or horrible scores depending on the chosen metric and how you conduct the calculations. Do you understand why?

5. How to monitor forecast accuracy. No forecast metric is universally better than another. Do you know what forecast accuracy metrics to use and how?