Smart IP&O offers automated statistical forecasting that selects the right forecast method that best forecasts the data. It does this for each time-series in the data set. This blog will help a laymen understand how the forecast methods are chosen automatically.
Smart makes many methods available including single and double exponential smoothing, linear and simple moving average, and Winters models. Each model is designed to capture a different sort of pattern. The criteria to automatically choose one statistical method out of a set of choices is based on which method came closest to correctly predicting held-out history.
Earlier demand history is passed to each method and the result is compared to actuals to find the one that came closest overall. That “winning” automatically chosen method is then fed all the history for that item to produce the forecast.
The overall nature of the demand pattern for the item is captured by holding out different portions of the history so that an occasional outlier does not unduly influence the choice of method. You can visualize it using the below diagram where each row represents a 3-period forecast in held out history, based on different amounts of the red earlier history. The variances of each pass are averaged together to determine the method’s overall ranking against all other methods.
For most time series, this process can accurately capture trends, seasonality, and average volume accurately. But sometimes a chosen method comes mathematically closest to predicting the held-out history but doesn’t project it forward in a way that makes sense.
Users can correct this by using the system’s exception reports and filtering features to identify items that merit review. They can then configure the automatic forecast methods that they wish to be considered for that item.
