What is tolerance lane in SAP SCM-APO-FCS - Demand Planning?

SAP Term: tolerance lane

• Component: SCM-APO-FCS

• Component Name: Demand Planning

• Description: Term used in outlier correction. The tolerance lane is the range of values in which a historical value should lie. If a value is outside the tolerance lane, the system deems it to be an outlier. How the tolerance lane is determined depends on which outlier correction method you are using. Ex-Post Method The tolerance lane depends on the MAD measure of error. Tolerance Lane = Ex-Post Forecast +/- Sigma x MAD where Sigma is a factor that you enter in the forecast profile. Median Method The tolerance lane depends on the expected value as calculated using the median forecast model. Tolerance Lane = Expected value x Sigma for mor information, see the SAP Library documentation for Outlier Correction under .

Smart SAP Assistant

• Key Concepts: 
  Tolerance lane is a feature of SAP's Demand Planning component of the Supply Chain Management-Advanced Planner and Optimizer (SCM-APO-FCS). It allows users to define a range of values for a given forecast, which can be used to identify and adjust forecast errors. This range is referred to as the "tolerance lane". 
  
  How to use it: 
  To use the tolerance lane feature, users must first define the range of values for the forecast. This can be done by setting upper and lower limits for the forecast. Once these limits are set, any forecast values that fall outside of this range will be flagged as an error. 
  
  Tips & Tricks: 
  When setting the upper and lower limits for the tolerance lane, it is important to consider the expected variability of the forecast. If the expected variability is too low, then errors may not be identified. On the other hand, if the expected variability is too high, then too many errors may be flagged. 
  
  Related Information: 
  The tolerance lane feature can be used in conjunction with other features of SAP's Demand Planning component, such as statistical forecasting and demand sensing. This allows users to identify and adjust forecast errors more quickly and accurately.