Overview of Order-Based Supply Optimizer

In this chapter, we are providing an overview of the order-based optimizer. However we focus more on the Deployment Optimizer here. Time-series optimization is discussed in detail in the SAP IBP 700 course.

We have leveraged our long history in using optimization for SAP IBP, and currently use "one code" but offer specific features for different optimizers. Time-series based optimizer has been enhanced for use in order-based planning to consider master and transaction data specific to Order-Based Planning.

The optimizer enables cost-based planning. Independent of the use case, it always minimizes the total cost of the supply plan. During the optimization process, it searches through all feasible solutions to find the most cost-effective one in terms of total costs.

A solution is considered feasible by the optimizer when it respects all the planning constraints, for example, the source of supply options and available resource capacities. A feasible solution can contain non-deliveries, that is, not fully satisfied demands, safety stock constraint violations, or violate other constraints.

The most prominent costs are the ones for the source of supply decisions (production, transportation, and procurement) and the non-delivery costs for demands. The output is a feasible production, distribution, and procurement plan for the selected supply chain network.

When used in order-based planning, the planning run writes order results and pegging. To find the cost-optimal solution, the optimizer transforms the supply model into a mathematical representation. This representation is called Mixed-Integer Linear Program (MILP). This MILP can be solved, resulting in a mathematically proven optimal solution. For further details, you can refer to blog IBP for Supply Optimizer: the mathematics behind | SAP Blogs

• The main input to the optimizer is cost. Normally, we would use control costs. Business costs may be used, but they need to be established in such a way, that the business targets are achieved by the algorithm. Product costs can also be considered as penalty costs for the demand, with demand prioritization being an example. It is possible to structure costs in such a way, that demand from customer A is considered more important than demand from customer B.
• The optimizer will consider all the costs, most prominent from the demand-side being the Non-Delivery and Late Delivery Costs. From the Supply-side the most prominent costs are the Source of Supply Costs, that is, costs of Transportation, Production, and Procurement. For stock levels, the optimizer considers Safety Stock Violation costs.
• Detailed costs such as setup costs are not considered by the supply optimizer, as these costs become important in operational activities, such as Production Planning and Detailed Scheduling.
• Constraints that are considered by the optimizer include capacity, supplier constraint, lot size, and lot size horizons, and lead time. The optimizer does not consider allocation as a constraint.
• The output is a feasible production, distribution, and procurement plan for the selected supply chain network.
• The planning run writes order results and pegging which can then be referred to by the planners.

The following figure shows you the differences between Priority Heuristic and Order-based Optimizer.

For example, push production cannot be easily modeled by Priority Heuristics. This is because priority heuristics will try to fulfill the demand as late as possible or as close to the demand date as possible. For example, if you have stock at the production plant and there is demand at the DC, the Priority Heuristics will leave the stock at the production plant and the DC will pull the stock only when the demand fulfillment date is closer.

With the optimizer cost modeling, it is possible to push the stock out of the production plant as soon as its produced, and this product will be stored at the DC.

Another example of the different capabilities of the optimizer is the ability to prioritize the sources of supply by setting up the costs of alternate sources of supply.

Additionally, while using the optimizer, it is possible to strike a balance between pre-build and late delivery, whereas the Priority Heuristic by nature would prefer pre-build.

These capabilities of the optimizer are discussed in more detail in the SAP IBP 700 course.

The following figure below serves as a reference for the capabilities of Order-Based Optimization.

For example, if a question comes up, "Does the Order-Based Optimizer Support Inbound Quotas?" One could look at this figure to get the answer.

A Standard delivered SAP IBP Application Job Process template can be used to execute and schedule the optimizer runs and for reviewing results.

When triggering the application job, make sure that the Planning Algorithm selected in the planning job template, is "Optimizer".

When the optimizer run is complete, results can be seen in output key figures in the Excel UI, in the planner workspace, or in the Web apps such as Projected Stock.