FGS - HomeDemand ForecastingInventory PlanningDatabase ManagementReplenishment PlanningDistribution Planning
The Finished Goods Series
from E/Step Software Inc.
  Elements of a Forecast Model
Forecast Module
FGS - Home
    Key features
    Industries served

About E/Step Software

        Introductory Workshop
        Intermediate Workshop



  Forecast Model Elements
   Tracking Signal
   Fourier Series

  Compare service
   Set service
   Component SS

   Modules & Interfaces

Replenishment Planning

Distribution Planning

An example illustrates the elements of a forecast model

This is an example of an FGS forecast.  It uses actual data from an industrial service parts business from 1982 to 1984.  This item has all of the elements of a forecast model.

In green you see the demand history in units per month on a 5-4-4 calendar for this example.  This 5-4-4 calendar is a typical fiscal calendar that has 5 weeks in the first fiscal month of the quarter, then 4 weeks in the next month, and 4 in the third.  Since a calendar like this can cause false seasonality, internally FGS converts demand history to a daily rate before performing its calculations.

Seasonal SKU

The blue line is the trend line for the forecast.  It is projected into the future as the forecast (red). The elements of this forecast model are:


Seasonal Elements

In the graph you can see these four model elements add up to the total forecast model:


Seasonal SKU ErrorResidual forecast error

The forecast model is backcast (i.e., projected backwards) and is compared with the demand history.  The difference is called the residual error.  Initially, this is the error used when you calculate safety stock.  The screen to the right shows this item's error distribution . FGS compares the green histogram of the residual errors with the normal error distribution curve (blue).   Obviously these errors are normally distributed. 

SKUs with low, lumpy demand often fit the exponential distribution (red) requiring different safety factors for safety stock.  Getting the right error distribution is essential to predicting service and inventory levels.  Alternatively, this SKU could be put on a less-frequent calendar, for example quarterly (calendar analysis).