What is demand decomposition in SAP CA-DDF-RT - Demand Data Foundation for Retail?

SAP Term: demand decomposition

• Component: CA-DDF-RT

• Component Name: Demand Data Foundation for Retail

• Description: Generated as additional information from the demand forecast, decomposition is an analysis by demand influencing factor DIF of a calculated demand to better explain its underlying causes. The decomposition is additive; it indicates which portion of the demand can be attributed to baseline business and which portions are due to a specific DIF or combinations of DIFs tactic, seasonality, offer, price. A DIF's influence on the demand can be either positive or negative.

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• Key Concepts: 
  Demand Decomposition is a feature of the CA-DDF-RT Demand Data Foundation for Retail component of SAP. It allows users to break down demand into its component parts, such as customer segment, product type, and geography. This helps to identify trends and patterns in demand that can be used to inform decisions about inventory management, pricing, and marketing. 
  
  How to use it: 
  Demand Decomposition can be used by accessing the CA-DDF-RT Demand Data Foundation for Retail component of SAP. Once accessed, users can select the desired data points to be broken down and analyzed. The data can then be visualized in a variety of ways, such as tables, charts, and graphs. This allows users to quickly identify trends and patterns in demand that can be used to inform decisions. 
  
  Tips & Tricks: 
  When using Demand Decomposition, it is important to consider the different types of data points that can be broken down. For example, customer segmentation can help identify which customer groups are driving demand for certain products or services. Additionally, geography can help identify regional trends in demand that may not be visible when looking at overall demand data. 
  
  Related Information: 
  Demand Decomposition is just one of the features available in the CA-DDF-RT Demand Data Foundation for Retail component of SAP. Other features include forecasting, inventory optimization, and pricing optimization. These features can help users make more informed decisions about their inventory management, pricing strategies, and marketing campaigns.