The Time-Series-Based Supply Optimizer in SAP IBP is a powerful tool for supply planning, but its runtime can be significant due to the complexity of constraints and variables. Reducing runtime involves optimizing the problem size and configuration, as outlined in SAP’s performance best practices.
Option A: Keep the number of fair share segments smallThis is correct. Fair share segments (used in demand prioritization or allocation) increase the optimizer’s complexity by adding variables and constraints. Limiting segments (e.g., fewer priority tiers) reduces the computational load, a recommended practice in SAP IBP’s optimizer configuration documentation.
Option B: Split into multiple planning areas to support weekly vs. daily planning needsThis is incorrect. Splitting into multiple planning areas might simplify individual runs but doesn’t directly reduce the runtime of a single optimizer run. Planning areas are structural, not runtime-specific, and this approach addresses granularity needs, not performance.
Option C: Use non-overlapping networks by using Subnetwork ID maintained at Location-Products to reduce the size of the problemThis is correct. Subnetwork IDs (e.g., assigned to Location-Product combinations) partition the supply chain network into smaller, independent subproblems. The optimizer solves these separately, significantly reducing runtime by shrinking the problem scope, as per SAP IBP’s network optimization guidelines.
Option D: Eliminate the usage of telescopic time bucketsThis is correct. Telescopic time buckets (e.g., daily near-term, weekly mid-term, monthly long-term) increase complexity by requiring the optimizer to handle variable time granularities. Using uniform buckets (e.g., all weekly) simplifies the model and cuts runtime, a known performance tweak in SAP IBP.
Option E: Increase the use of incremental lot size beyond the frozen horizonThis is incorrect. Incremental lot sizes affect planning quantities, not optimizer runtime directly. Adjusting lot sizes might influence solution feasibility but doesn’t inherently optimize performance.
Thus, A, C, and D are proven methods to reduce time-series optimizer runtimes, per SAP IBP’s official performance optimization documentation.