Summary
The forecast error tab of Forecast Manager allows you get some information on how accurate your operational and statistical forecasts are for a given hierarchy node selection.
It is located primarily in the bottom half of the Forecast Manager screen, in amongst the other settings tabs.
Forecast Error Sections
This tab is broken up into three sections, each reporting the same values. Here are the sections:
- Forecast Error - Current - This shows errors measured using all available current data, looking backwards over whatever is your error length horizon, typically 1 year. So, if today was June 1st, it is measuring error from forecast periods May of this year, to June of last year.
- Forecast Errors @ Lead Time- This shows errors measured from one lead time ago, referred to as your "Lead Time Lag", based on the lead time or average lead time of products of the selected hierarchy node. This gives you the ability to compare how the error of your forecast is changing over time, using the same backwards-looking measure. So, if today is June 1st, and your lead time is 3 months, meaning one lead-time-ago is March 1st, it is calculating errors from February of this year, to March of last year.
- Forecast Errors @ Lead Time vs Current Actuals - Since the backwards looking method can under-report future forecast error due to fitting, the "vs Current Actuals" variant calculates the error of your lead-time-ago snapshot using current data, which now will include data points not known at the time the snapshot was taken. These numbers will therefore typically be higher error than the backwards-looking values, since there is more unknown. So if today is June 1st, lead time is 3 months, so the lead-time-ago snapshot is from March 1st, then it is calculating error from May of this year, to June of a year ago, meaning 3 of the 12 actual sales months included in the error calculation (March, April, May) were not known at the time the snapshot was taken.
Forecast Error Values
Each grouping shows the same table of values. Each row has the same columns available.
Rows
- Benchmark Model - Shows the measures of the benchmark / naive forecast model.
- Stat. Model - Measures for your current statistical model
- Operational Forecast - Measures for your operational forecast
Columns
- % - Shows the error percent of the row in question, measured over the error length. This is typically a MAEP (Mean Absolute Error Percent) calculation, although StockIQ Supports RMSEP.
- Units - Shows the units corresponding with that error percentage
- $ - The enterprise value of the error. If your currency is not USD, the appropriate symbol is displayed.
- Bias % - Shows the Forecast Bias Percent of this forecast series
- FVA - Shows the Forecast Value Added of this forecast series relative to its predecessor, such as the statistical FVA relative to the Benchmark forecast
Error Calculation Factors
This shows some of the factors and options going into the error values, configurable in the Global Demand Forecast Settings screen.
- Error Calculation - whether we are using MAE or RMSE as the error calculation
- Error Sample Length - displays the period over which the average error is being calculated, typically 6 months to 1 year is best.
- Avg Lead Time Used - Shows the lead time being used to determine what is 1 lead time ago for this node. Since you may have selected a level that incorporates several items with varying lead times, an average is taken of the item-sites within your selected hierarchy node.
Troubleshooting
- Forecast Errors @ Lead Time is actually a snapshot of Forecast Error - Current looked like at a lead time ago snapshot.
- Keep in mind the Safety Stock Calculations by default use the Stat. Model Error
- At the beginning of an implementation project StockIQ may not have enough history to calculate all of the error metrics listed in the Forecast Error Tab. Calculations are limited to start of the Operational Forecast.
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