Different Algorithms in IBP- how they help in Supply Chain Process
We all know the importance of having a robust Supply Chain Planning Process which became all the more relevant during the sudden disruption caused by Covid-19 pandemic. SAP has come a long way in this from SAP APO to present day of SAP IBP solution. IBP offers an integrated platform by operating on a single data model – right from product review to demand and supply planning and S&OP executive meetings.
Amongst the many features of IBP, we will particularly talk about the different algorithms which are available under various modules of Demand planning, Supply planning, Inventory optimizer and Response management for a better supply chain planning. These algorithms which are unique in their respective areas help to fine tune the outcome of the various processes in demand and supply.
Demand planning algorithms– Statistical forecasting models are divided into following 3 categories. All the below three put together helps in generating a statistical forecast for the consensus demand planning.
- Pre processing algorithms allow you to manage the transactional data for statistical forecasting engine. Elements like outlier correction and missing value sub station algorithms are part of this.
- Forecasting algorithms calculate a statistical forecast from the corrected demand history in pre-processing step.
- Postprocessing algorithms calculate the forecast accuracy based on the ex post forecast.
Supply planning algorithms– After the consensus demand finalization, the supply planning algorithms helps in generating the internal demand flow in the supply network.
- The S&OP heuristic is an unconstrained, sourcing rule-based, decision-supporting algorithm. It means whatever the demand requirement is, it will try to get the supplies and load the resources and supplies in an unconstraint fashion.
- The S&OP optimizer is a profit-maximization decision-making algorithm. It uses a financial model as the basis of a profit optimization and helps the planners to decide about which demands to pursue and what flows to utilize in the supply network.
Inventory optimization– As the name suggest, we can leverage this optimizer engine to manage the safety stock levels.
- Single-echelon inventory optimization calculates the safety stock for every location independently.
- Multi-echelon inventory optimization calculates the inventory for all the locations in the supply network by considering the strategic placements and inventory pooling solution.
Response management– It delivers order-based algorithms that help with operational planning by generating supply elements on an order level, like planned orders, purchase requisitions, and stock transfer requisitions. It also help improve a company’s margins by allocating the available constrained supply to the prioritized products and customers.
- Constrained planning will use the open forecast, combine it with open supply elements like purchase requisitions, planned orders and sock transfer requisitions to build a constrained operational plan.
- Order confirmation will consider the open sales orders and the forecast in a prioritized fashion to generate the same supply elements. It also supports the recalculation of order confirmation date to communicate to customers. This helps a lot in case of change in supply plan due to various constraints.
- The deployment algorithms will generate deployment stock transfer requisitions to improve the internal distribution of stock aligned with the prioritized demands.
Let us know your feedback on this topic and if there is any further input you would like to add.
My next article will be about the different statistical models available under Demand planning and S&OP.
Good sharing , thanks