How to Combine Supply Chain Optimization and Simulation

 

In our previous blog on Supply Chain Optimization Versus Simulation, we talked about the difference between optimization and simulation and their applications in modern supply chain design and analysis. The decision to use optimization or simulation has to be guided by the pressing needs and questions of your business. Optimization is better suited for situations when the goal is to determine “what’s best” while simulation should be reserved for when the goal is to understand “what is” so that “what if” questions can be addressed.

This distinction, however, does not preclude the combined use of optimization and simulation in addressing supply chain business questions. In fact, in many of our inventory optimization engagements, simulation has proven time and again to be an essential technique for testing the robustness of solutions from optimization models. This is especially useful because, while you can plan optimally, you will most definitely execute sub-optimally in practice. 

 

Use Simulation to Test the Robustness of Solutions from Optimization

A typical benefit from an inventory optimization project should be to reduce inventory by 10-30%. In many cases, the optimization model is built and ran using an off-the-shelf multi-echelon optimization software tool. These tools are designed to provide a target safety stock level and a committed service time for each product at each location in the supply chain such that the overall supply chain inventory holding cost is minimized while meeting pre-set service levels.

One of the first questions clients ask is “why should I trust the recommended safety stock from this software?” Most reasonably skeptical clients will not accept reasons such as “this software vendor is a reputable company, or "this software has been used by more than 1,000 customers, so you should trust the recommendation.” Clients often require an objective validation of the safety stock target provided by the optimization tool as a way to engender confidence in the recommendation before they are comfortable implementing it.

Here is where simulation comes to the rescue and optimization passes the baton; it’s a tag-team effort after all. In the case of the inventory optimization example, it is often a straight forward exercise to build a simulation model to check that the optimization model’s suggested safety stock level can achieve the desired fill rate. 

 

A Simple Simulation for Validating Inventory Optimization Solution

As an example, we present a simulation model that can be used to validate the recommended safety stock levels from an inventory optimization model. The simulation model takes the following as input: demand profile, order fulfillment process, inventory replenishment process, and essential data collection process. Data gathering and preparation should be easy as there is an almost perfect overlap between the data required for simulation and optimization.

Because of the stochastic nature of an inventory simulation model, a sufficient warm-up period should be allowed during run-time to allow the model to reach steady state. The simulation output should provide data that can be used to calculate metrics such as number of stock outs per year, average inventory level, and fill rate. The derived fill rate from simulation, which is based on the recommended safety stock levels, will confirm whether the solution is robust or not. Similarly the average inventory level from simulation can be used to confirm that the size of the inventory opportunity suggested by the optimization model is realizable.

 

Conclusion

Optimization and simulation are not mutually exclusive. While optimization provides the best solutions, simulation can be used to confirm optimization output and build confidence in recommendations before implementation. In fact, in many of our engagements, simulation is the tool of choice to insert confidence in recommendations from the optimization model.