The Power of Optimizer Constraints in SAP IBP

The optimizer in SAP IBP can be a very powerful tool for supply planning, but it’s easy to get frustrated with it at first. 


For one, the optimizer is rigid in terms of the algorithm itself (which is what makes it such a powerful tool), so without proper tuning it may not come up with results that make sense for your business. However, you can unlock the strength of the optimizer by leveraging the flexible modeling capabilities within IBP.


Running the supply optimizer will always result in the lowest cost plan, within the realm of the constraints. Behind the scenes, the objective function of the optimization model (the overall goal of the solver) is set to minimize all costs. Constraint-based planning enables you to model your requirements and preferences upfront. Let’s jump into how the cost-minimization model and the constraint-based planning fit together.


IBP breaks constraints up into hard, pseudo-hard, and soft constraints:

Hard constraints are those which are never violated by the optimizer. This includes Min/Max Lot Sizing, Sourcing Validity, Production Resource Capacity, and more. Behind the scenes, they are set up as an inequality (i.e. for each resource, the total Resource Usage should be less than or equal to the total Resource Capacity). Therefore, when the solver is running, if a solution doesn’t adhere to these inequalities, it’s not a valid solution.


If hard constraints are solid boundaries, pseudo-hard constraints are more like dotted lines. These are things that are not preferred decisions, but if necessary, can be done. If there is no solution, the optimizer may violate a pseudo-hard constraint. This includes Minimum Transport, Minimum Production, Storage Resource Capacity, and more. Violating a pseudo-hard constraint results in a large penalty cost, so the optimizer is not inclined to do so seeing as the objective function is to minimize overall costs. Because the penalty costs are so high, these constraints are typically not violated unless there is an unavoidable limitation.


To model the financial impact of violating constraints, you have soft constraints. In this category you have Late Delivery Cost Rate, Inventory Target Violation Cost Rate, Capacity Supply Expansion Cost Rate, and more. Violating a constraint, such as late-delivery, will have a financial cost to the business, which contributes to the total overall cost in the optimizer. The cost rates here model those financial costs. The optimizer will run through the different solutions to see whether causing these financial costs ends up resulting in lower overall costs.


There are other costs that factor into the optimizer, such as Transportation Cost Rate or Inventory Holding Cost Rate. These are used to model preferences, such as preferred transportation modes or preferred resource usage. The costs rates are not financial costs, but more like weights applied to the corresponding key figures (i.e. the external receipts cost rate would act as a weight on the External Receipts key figure). These costs also add to the total overall cost that the optimizer is trying to minimize, so these costs have the same effect as the soft constraints.


It’s easy to look at the results and think something is wrong with the optimizer when you don’t see results you were expecting. However, the optimizer is just math, and math never fails. In reality, insight into how the optimizer works and proper tuning of the model can make the tool much more effective for your business. Take a look at the video to see optimizer constraints in action.

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