- Forecast accuracy has a simple definition, but many important details.
- We cover the definition of forecast accuracy and its implications.
Forecast accuracy is deceptively easy to understand. And just by defining the forecast accuracy measurement, it does not answer the entire question of the forecast accuracy definition. The term, “the devil is in the details” certainly applies to forecast accuracy.
See our references for this article and related articles at this link.
What is the Definition of Forecast Accuracy
Forecast accuracy is the degree of difference between the forecasted values and the actual values. Forecast accuracy is never known until the event has passed. This is why all forecast accuracy measurement is historical. Future forecast accuracy can only be described in terms of accuracy probability.
Simply defining forecast accuracy is the easy part. The difficult part is getting into details of forecast accuracy. As we cover in the article How is Forecast Error Measured in All of the Dimensions in Reality?, the multiple dimensions of forecast accuracy must be defined before one can say that the forecast accuracy that is reported is understood.
Even beyond the topics raised in the article just references, there are also important distinctions to be understood regarding what “is” the forecast and what “is” the actual.
- Defining the Forecast: For the forecast, was this the forecast before lead time, or were changes made within lead time doing something like demand sensing? For a forecast accuracy measurement to be useful, it must not be altered after the time to respond to the forecast has passed.
- Defining the Actuals: For the actuals, is that the number that was sold, or the number that could have been sold if capacity has been available. However, authentic demand is not what was provided, which is a constrained demand. We cover this topic in the article How to Best Understand Measuring the Unconstrained Forecast.
A More Straightforward Approach to Forecast Error Calculation
Observing ineffective and non-comparative forecast error measurements at so many companies, we developed the Brightwork Explorer to, in part, have a purpose-built application that can measure any forecast and to compare this one forecast versus another.
The application has a very straightforward file format where your company’s data can be entered, and the forecast error calculation is exceptionally straightforward. Any forecast can be measured against the baseline statistical forecast — and then the product location combinations can be sorted to show which product locations lost or gain forecast accuracy from other forecasts.
This is the fastest and most accurate way of measuring multiple forecasts that we have seen.