Five Classes of Forecast Methods
The forecasting module is designed to catch all possible trends, including slow and fast movers, seasonal trends, or items increasing or decreasing in sale. This means that users do not need advanced statistical knowledge as the system handles the forecasting part automatically. As sales data arrives, the forecasting module automatically calculates sales forecasts based upon one of the following five classes of forecasting models to satisfy all mainstream business forecasting requirements. The best fitting forecasting method gets chosen automatically for an item depending on the nature of the product and the amount of historic data available. Below are the details of the five classes of forecasting models. You can see a more detailed description of these forecasting models in the previous chapter.
Simple Method Simple Method includes moving average models and is for very short or extremely volatile data. This is a common inventory management method - used by wholesalers and distributors for demand forecasting - to average the sales over the previous few months. This method can work well for items in consistent demand, but it does not work so well for others. Because different items can have a very different demand pattern, it is extremely important to choose the most relevant forecasting method for each item.
Curve Fitting Curve fitting is the process of constructing a curve, or mathematical function that has the best fit to a series of data points available. This method provides a quick and easy way to identify the general form of the curve that the demand is following. The forecast module supports four types of curves: straight-line, quadratic, exponential and growth (S-curve).
Low Volume Models Croston’s intermittent demand model and discrete data models are provided to accommodate low-level and random demand intervals like spare parts. The Croston’s model is specifically designed for data sets where the demand for any given period is often zero and the exact timing of the next order is not known. This method works by combining a smoothed estimate of the average demand for periods that have demand with a smoothed estimate of the average demand interval.
Exponential Smoothing Twelve different Holt-Winters Exponential Smoothing Models are provided to accommodate a wide range of data characteristics. Exponential smoothing models capture and forecast the level of the data along with different types of trends and seasonal patterns. The models are adaptive, and the forecasts give greater emphasis to the recent history verses the more distant past. The robustness of exponential smoothing makes it ideal when there are no leading indicators, and when the data is too short or volatile for Box-Jenkins.
Box-Jenkins (ARIMA) Models Box-Jenkins models are like exponential smoothing models in that they are adaptive, can model trends and seasonal patterns, and can be automated. They differ in that they are based on autocorrelations (stable data sets) rather than a structural view of level, trend and seasonality. Box-Jenkins models tend to perform better than exponential smoothing models for longer, more stable data sets and not as well for noisier, more volatile data.