Glossary

Tracking Signal

Tags: Glossary

A statistic that reveals instances where parameter estimates in a forecasting model are not optimal. For example, a tracking signal might be based on a graph of the ratio of the cumulative sum of the differences between the actual and forecast values to the mean absolute deviation. If the tracking signal exceeds a certain value, the series can then be flagged for examination. This concept has been used successfully in quality control. It seems sensible also for forecasting, although little research supports its use. An alternative is to use successive re-estimation.

What is Tracking Signal?

Tracking Signal

In the field of logistics and forecasting, tracking signal is a valuable statistic that helps identify instances where parameter estimates in a forecasting model are not optimal. It provides a means to flag series that require further examination and potentially improve the accuracy of forecasts.

To understand tracking signal, let's consider an example. Imagine we have a forecasting model that predicts the demand for a particular product. The model generates forecast values, and we compare these forecasts to the actual values. The tracking signal is calculated by graphing the ratio of the cumulative sum of the differences between the actual and forecast values to the mean absolute deviation.

When the tracking signal exceeds a certain value, it indicates that the forecasting model is not performing optimally. This means that the model is consistently overestimating or underestimating the demand for the product. By flagging these series for examination, we can investigate the reasons behind the discrepancies and make necessary adjustments to improve the accuracy of future forecasts.

The concept of tracking signal has been successfully used in quality control, where it helps identify deviations from expected performance. It makes sense to apply this concept to forecasting as well, although there is limited research supporting its use in this context. An alternative approach to addressing forecasting errors is successive re-estimation, where the forecasting model is continuously updated based on new data.

In conclusion, tracking signal is a valuable tool in logistics and forecasting. By monitoring the tracking signal, we can identify instances where parameter estimates in a forecasting model are not optimal and take corrective actions. While further research is needed to fully understand its effectiveness, it provides a practical means to improve the accuracy of forecasts and make informed decisions in supply chain management.