By Jeff Marthins, Senior Business Consultant at John Galt Solutions.
How can I tell if it is a good forecast or just another April Fool’s? ForecastX has many ways to discover the answer.
MAPE and WMAPE are typical ways to measure forecast error. These are standard outputs of ForecastX. However, with ForecastX, there are more ways to discover the secrets behind the forecast. First, you can look at the Coefficient of Variation to see if an item is “Forecastable”. Demand Variable Index (DVI) and Coefficient of Variation (COV) are two ways of looking at an item as forecastable or not. To calculate DVI or COV, you simply sum the absolute deviation and divide by the mean (or average). Typically, any items that have an index of 0.75 or higher is usually deemed unforecastable. Keep in mind, some items with a high DVI or COV may still be forecastable depending on the item. For example, a new item with fewer data points may have some high standard deviation and may need a different model such as a new product forecasting model. Lower indexes can show lower variability and time series statistical models typically fit. This is helpful with regards to trimming up your process with models or approaches to use for forecasting.
ForecastX also provides an Audit Report that can also give you more information around the forecast output. By using “Out of Sample” data, ForecastX will hold out data and compare to the actual sales data that you already have. This, too, will return a MAPE score to show you how the forecast scored against the recent actual data as a hold out set. This allows the user to see how the recent forecast is comparing to the actual sales data. Also, within the Audit report, ForecastX delivers information to the demand planner or data analyst to see trend and seasonality. Finally, the audit report shows the seasonal indexes to help the planner see the statistical models for future forecasting.
A naïve forecast is a simple year over year (YOY) forecast. Whatever happen last year will happen next year. ForecastX’s statistical models provide the industry’s most accurate forecasts. By comparing ForecastX’s forecasts versus a naïve forecast, the accuracy improvement is quite clear.
If you would like to learn more about ForecastX, visit Johngalt.com/forecastx
Don’t go through April’s planning cycle will a foolish forecasts.