Smart IP&O is powered by the SmartForecasts® forecasting engine that automatically selects the most appropriate method for each item. Smart Forecast methods are listed below:
- Simple Moving Average and Single Exponential Smoothing for flat, noisy data
- Linear Moving Average and Double Exponential Smoothing for trending data
- Winters Additive and Winters Multiplicative for seasonal and seasonal & trending data.
This blog explains how each model works using time plots of historical and forecast data. It outlines how to go about choosing which model to use. The examples below show the same history, in red, forecasted with each method, in dark green, compared to the Smart-chosen winning method, in light green.
Seasonality
If you want to force (or prevent) seasonality to show in the forecast, then choose Winters models. Both methods require 2 full years of history.
`Winter’s multiplicative will determine the size of the peaks or valleys of seasonal effects based on a percentage difference from a trending average volume. It is not a good fit for very low volume items due to division by zero when determining that percentage. Note in the image below that the large percentage drop in seasonal demand in the history is being projected to continue over the forecast horizon making it look like there isn’t any seasonal demand despite using a seasonal method.
Statistical forecast produced with Winter’s multiplicative method.
Winter’s additive will determine the size of the peaks or valleys of seasonal effects based on a unit difference from the average volume. It is not a good fit if there’s significant trend to the data. Note in the image below that seasonality is now being forecasted based on the average unit change in seasonality. So, the forecast still clearly reflects the seasonal pattern despite the down trend in both the level and seasonal peaks/valleys.
Statistical forecast produced with Winter’s additive method.
Trend
If you want to force (or prevent) trend up or down to show in the forecast, then restrict the chosen methods to (or remove the methods of) Linear Moving Average and Double Exponential Smoothing.
Double exponential smoothing will pick up on a long-term trend. It is not a good fit if there are few historical data points.
Statistical forecast produced with Double Exponential Smoothing
Linear moving average will pick up on nearer term trends. It is not a good fit for highly volatile data
Non-Trending and Non-Seasonal Data
If you want to force (or prevent) an average from showing in the forecast, then restrict the chosen methods to (or remove the methods of) Simple Moving Average and Single Exponential Smoothing.
Single exponential smoothing will weigh the most recent data more heavily and produce a flat-line forecast. It is not a good fit for trending or seasonal data.
Statistical forecast using Single Exponential Smoothing
Simple moving average will find an average for each period, sometimes appearing to wiggle, and better for longer-term averaging. It is not a good fit for trending or seasonal data.
Statistical forecast using Simple Moving Average
