FGS - Home

Key features

Implementation

Industries served

Clients

Undecided?

About E/Step Software

Services

Consulting

Education

Introductory Workshop

Intermediate Workshop

Outsource

Support

Articles

Contact

Forecasting

Forecast Model Elements

Outliers

Tracking Signal

Calendars

Fourier Series

Inventory

Compare service

Set service

Component SS

Database

Modules & Interfaces

Replenishment Planning

Distribution Planning

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.

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:

- Level: 712 units per month
- Trend: +15.9 units growth each month
- Seasonality: 1 cycle per year that peaks with a high in the spring and a low in the fall
- Selling days adjustment: the demand daily rate is weighted by the number of days in each forecast period

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

- Orange Level
- + Blue Trend Line
- + Green Seasonal Cycle
- + Red Adjustment for the 5-4-4 Calendar

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).