To enhance the performance of a time-series optimizer run, several model simplification techniques can be employed. Using non-overlapping subnetworks (C) is a primary method, as it partitions the overall supply chain model into smaller, independent problems that can be solved more quickly and in parallel. Keeping the number of fair-share segments small (A) reduces the number of constraints and decision variables, simplifying the mathematical model and thus decreasing runtime. Eliminating telescopic time buckets (D) in favor of a uniform time profile can also reduce model complexity for the optimizer, as it removes the need to manage transitions between different time granularities, leading to faster processing in certain scenarios.

B. Split into multiple planning areas to support weekly vs. daily planning needs.

This is a major architectural decision, not a standard performance tuning technique. It introduces data integration overhead and is generally not recommended for runtime reduction.

E. Increase the use of incremental lot size beyond the frozen horizon.

Lot-sizing procedures introduce integer variables, which significantly increase the complexity of the optimization problem (MILP). Increasing their use would lengthen, not reduce, the optimizer's runtime.

1. SAP IBP for Supply Chain - Application Help (2405): Subnetworks

The documentation states

"You can use subnetworks to partition your supply chain network into smaller

independent parts... This can significantly improve the performance of the planning algorithms." This directly supports option C.

2. SAP IBP for Supply Chain - Application Help (2405): Fair-Share Planning

In the section on fair-share planning

it is noted that the number of segments and demand priorities impacts the complexity. The guide on optimizer performance implicitly supports that simplifying the model

such as by reducing fair-share segments

improves runtime. This supports option A.

3. SAP IBP for Supply Chain - Model Configuration Guide (2405): Performance of the S&OP Operator

This guide emphasizes that model size and complexity are key drivers of runtime. It advises on simplifying the model where possible. Reducing the complexity of the time profile (which can involve removing telescopic buckets) is a valid simplification technique. This supports option D.

4. SAP IBP for Supply Chain - Application Help (2405): Lot-Size Parameters

The documentation explains that using lot-sizing procedures turns the problem into a Mixed-Integer Linear Program (MILP)

which is more complex and time-consuming to solve than a standard Linear Program (LP). Increasing the use of lot sizes would therefore negatively impact performance. This confirms why option E is incorrect.