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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 propagation heuristic, the time-series-based supply planning heuristic fulfills all demands, even if there is insufficient supply. Consequently, inventory levels might be negative in the resulting plan. The algorithm can be called in batch mode as well as interactively from the SAP IBP, add-in for Microsoft Excel.
Key Concepts: Time-series-based supply planning heuristic is a component of the SAP SCM-IBP-PLA Planning Framework. It is a planning algorithm that uses historical data to predict future demand and supply. It takes into account factors such as seasonality, trends, and other external influences to create an accurate forecast. The algorithm then uses this forecast to generate a supply plan that meets the demand while minimizing costs. How to use it: The time-series-based supply planning heuristic can be used in the SAP SCM-IBP-PLA Planning Framework. To use it, you must first enter the historical data into the system. This data should include factors such as seasonality, trends, and other external influences. Once the data is entered, the algorithm will generate a forecast based on this data. The forecast can then be used to create a supply plan that meets the demand while minimizing costs. Tips & Tricks: When using the time-series-based supply planning heuristic, it is important to ensure that the historical data entered into the system is accurate and up to date. This will ensure that the algorithm generates an accurate forecast and a supply plan that meets the demand while minimizing costs. Additionally, it is important to regularly review and update the historical data in order to keep the forecast accurate. Related Information: The time-series-based supply planning heuristic is just one component of the SAP SCM-IBP-PLA Planning Framework. Other components include inventory optimization, demand forecasting, and inventory replenishment. Additionally, there are other algorithms available for use in the framework such as linear programming and Monte Carlo simulation.