What is time-series-based supply propagation heuristic in SAP SCM-IBP-PLA - Planning Framework?

SAP Term: time-series-based supply propagation heuristic

• Component: SCM-IBP-PLA

• Component Name: Planning Framework

• Description: An algorithm that calculates an infinite supply plan in which the data is planned in a series of time periods. In contrast to the time-series-based supply planning heuristic, the time-series-based supply propagation heuristic propagates only the available supply downstream through the supply chain. Therefore, the algorithm does not fulfill all demands if there is insufficient supply. The algorithm can be called in batch mode as well as interactively from the SAP IBP, add-in for Microsoft Excel.

Smart SAP Assistant

• Key Concepts: 
  Time-series-based supply propagation heuristic is a component of the SCM-IBP-PLA Planning Framework. It is an algorithm that uses historical data to predict future supply and demand. It takes into account factors such as seasonality, trends, and other external influences to create a forecast of future supply and demand. 
  
  How to use it: 
  The time-series-based supply propagation heuristic can be used to create a forecast of future supply and demand. This forecast can then be used to plan for future production, inventory, and other related activities. The algorithm can be used in conjunction with other forecasting methods to create a more accurate forecast. 
  
  Tips & Tricks: 
  When using the time-series-based supply propagation heuristic, it is important to ensure that the historical data used is accurate and up-to-date. Additionally, it is important to consider external factors such as seasonality and trends when creating the forecast. 
  
  Related Information: 
  The time-series-based supply propagation heuristic is part of the SCM-IBP-PLA Planning Framework. Other components of this framework include demand forecasting, inventory optimization, and production planning. Additionally, there are other forecasting methods that can be used in conjunction with the time-series-based supply propagation heuristic to create a more accurate forecast.