How often should you revise your forecasting model to ensure your forecast is accurate?
Statistical forecasting predicts the future based on historical data analysis. There are many ways to use statistical forecasting. For instance, there are simple methods in which the values found in recent history are projected into the future. There are also more advanced methods which are used to understand the demand pattern which encompass a longer period of time. Surprisingly, advanced models do not always outperform simpler ones.
1. The computing power of Honeycomb
EyeOn can determine which statistical models perform best by doing a best fit analysis. During a best fit analysis, we run multiple statistical formulas which have a selection of settings to choose from to find the best fit. This is possible with the computing power of HoneyComb, EyeOn’s data analysis platform. It enables data handling of millions of records to generate future forecasts at low granularity (days, weeks) effortlessly.
2. Revising the best fit analysis?
Is just carrying out best fit analyses at the beginning of statistical forecasting sufficient? Demand patterns change over time and the model is only as good as its interpretation of the historical data. When the market is moving fast, you need to respond immediately. Products with a short lifecycle in a highly competitive market, such as electronic products, have demand patterns that change within months.
That’s why revising the best fit analysis regularly can improve forecast accuracy. But that raises the following question: How often should you run the best fit analysis?
3. Stability versus optimization of the forecast
There is a trade-off between putting a lot of effort in improving the forecast accuracy which takes up all the demand planner’s attention and automating the process so that the demand planner can focus his or her effort on where it really adds value. The forecast settings should be as optimally automized as possible, but there is also a need for stability and consistency in the forecast that is generated. This is why we decided to compare different frequencies when revising the best fit models.
We wanted to be able to determine which approach is the most suitable for each of our clients. The image above shows the forecast accuracy for three forecast scenarios. Shown here is the best fit analysis rerun at different frequencies; revisions once a year, every half year or every month.
4. Yearly versus monthly revision
The chart shows that monthly revisions in statistical forecasting outperform the other revision frequency scenarios. The forecast accuracy, especially in the months between December and June, is higher for the revised every month scenario. There is somewhat less than a %-point difference between the monthly best fit revision and the average forecast accuracy of the other two revision scenarios. It is up to the client to decide whether this point difference is significant enough to justify more frequent forecast model revisions.
Just how often should you revise your statistical forecast? Find out more by contacting us now at email@example.com.