As part of advanced planning system implementation, it is vital to understand what is expected in terms of system performance from the business point of view and what needs to be done to meet these expectations. Optimization is just one of the facets of SAP IBP processes, but it is one of the most complex facets and can contribute significantly to the extension of the processing time for the calculation of supply plans overall.
Numerous variables are in play for the overall performance. Some of them might be associated with the organization's IT landscape as a whole and, therefore, are beyond the control of the SAP IBP implementation team. When the initial model is being considered for SAP IBP, it is important to be realistic about the overall model size and its complexity.
As an example, an implementations where sales, inventory and replenishment need to be modeled for a retail stores network will have significantly more location products combinations than implementations where the model is concerned with the network of plants and distribution centers only. Similarly, some organizations might have a reverse logistics component, which can result in effectively doubling the model size and increasing its complexity.
Estimating the time that will be needed for the optimizer run to complete is quite difficult. Results achieved in test systems with limited arrays of data, might not translate well to runtimes in production, because the relationship between the number of planning combinations and run time, for example, is not linear.
Best practices include, thorough testing with a good set of data and with different scenarios (for example, different horizons, with and without fair share) and being aware of the overall (common sense) principles.
In a situation, where the overall model size is significant and the supply chain problem is highly complex, the project team can utilize a number of options to try to break the problem into smaller problems and reduce complexity.
