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These documents describe the Forecast error calculations in APO DP and internal  calculations for the same.

Applies to: Industries which have implemented “SAP SCM-Demand planning” (Release version from 4.1…).

Authors: G.K.Radhakrishnan & Pravin Ramchandran

Author Bio

G.K.Radhakrishnan (APICS-CPIM ), is working as a SAP APO-DP consultant in Accenture has a consulting experience of more than three years with a domain experience of 7years in supply chain.

Pravin Ramchandran is working as a SAP APO-DP consultant in Accenture has a consulting experience of more than five  years with a domain experience of 7years in supply chain.

Please have a look at the SAP help link below which covers the formulas for forecast errors.

http://help.sap.com/saphelp_46c/helpdata/en/a5/6320e843a211d189410000e829fbbd/frameset.htm

Introducton to error definitions:

MAD: The mean absolute deviation gives the mean average difference between the forecasted value and the historical value in the ex-post forecast.

MPE: Is the mean  percentage error between the forecasted value and the historical value in the ex-post forecast.

MAPE: Is the mean absolute percentage error between the forecasted value and the historical value in the ex-post forecast

MSE: Is the mean square error between the forecasted value and the historical value in the ex-post forecast.

RMSE: Is the root mean square error between the forecasted value and the historical value in the ex-post forecast.


Selecting forecast errors

MAD is used for low volume / sporadic demand pattern, whereas MAPE is for high volume / fairly consistent and regular demand pattern.
MAPE is a relative measure .So if a volume of a product is very low then minor errors in the  will also show huge % error. This may mislead the user.

MPE numbers may mislead planners as it considers the sign (+/- ). So the error gets net off due to positive and negative numbers getting added up resulting a smaller number.

Mean Square Error -MSE is a highly sensitive number ( as shown in the excel calculations ) due to the squaring effect.Even the small increase in the error will lead to a high increase in MSE.This can be used for Premium products as inventory and stock out cost may be very high( A class items in ABC classification ).

Sample calculations  

The below screen was taken on Oct 2012 .So we have history till Oct 2012 and Statistical forecast is calculated from Nov 2012

The Horizons for the same are show below

As Seasonal model was used so we have a initialization of 12 months.

Corrosponding calculations are:

Error Total=sum of (Difference of Actual and Ex-Post)
           = 539  (Minor differences is  due to rounding which are explained later  )

MPE= sum of (%  Difference of Actual and Ex-Post  vs actuals )/N+1
    =(-24.90)/13  
    =-1.91554817731288

      The function model takes N+1 as is shown in the debug screen later

MAPE=Sum of (Absolute value ((%  Difference of Actual and Ex-Post  vs actuals )/N+1
    =124.902126305067/ 13
    =9.60785587

MSE=Square of "Difference of Actual and Ex-Post"/N+1
   =1093163/13
   =84089.4615384615 (Minor differences is  due to rounding which are explained later  )

RMSE=SQRT(MSE)
    =SQRT(84089.4615384615)
    =289.981829669484 (Minor differences is  due to rounding which are explained later  )

Internal calculations of the Function module

Below steps are needed to understand the internal calculations of forecast errors.

1.Load the selection in planning book

2. Go to SE37 and put a break point in function module /SAPAPO/FCST_CALCULATE_ERRORS

3. Type /h in the command window

4.Click on the generate univariate forecast

5.This will take you to the debugging screen

Function module /SAPAPO/FCST_CALCULATE_ERRORS is used to calculate the errors except MAD.

The functional module is taking N+1 as number of periods .In this example we have ex-post forecast for 12 periods but all the error calculations are based on 13 periods

The internal calculated values are shown below

Internal calculations from I_FCSTVIEW

As shown in the above screen, the numbers are not rounded for greater accuracy but the expost forecast seen in the planning book are  rounded.

Relevent notes to correct the forecast error calculations are

SAP Note 1616763 - Incorrect forecast error calculation

SAP Note 1746920 - MAPE error calculation incorrect

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