“Don’t leave Money on the Table,” These words, or questions of a similar nature, are heard time and time again in most meetings where simulation has been presented. Most of the time systems are so complex, that finding the “right solution” is easier said than done. To find the exact combination of conditions that will give you the ‘best’ possible system performance, you need to examine multiple scenarios.
Every situation can require some modification of your simulation model. The sheer number of parameters and combinations can create thousands or even hundreds of thousands of possible experiments. You’re left facing an impractical task. And yet potential improvements are never realized due to a lack of time for experimentation.
“Great presentation! Are you sure you’ve found the best solution for our business?”
With ProcessModel, the task becomes a whole lot easier. ProcessModel has an optimization tool called SimRunner that automates the process of creating, running and analyzing scenarios. You give SimRunner a goal or an objective, and the software will adjust the parameters of the model to meet that goal. For example, you set a goal of reaching the highest throughput with the minimum number of resources and the lowest WIP (Work In Progress). SimRunner intelligently runs scenarios, changes the parameters, compares the output. and shows you the best settings of parameters to achieve your goal…while you work on another project!
When to Use Process Optimization
When should you use optimization? Well, you will need to run optimization when the number of choices exceeds the number of experiments you are willing to run to find the answer. A natural starting point is when the number of experiments exceed 15, then you should start setting up the model for optimization. It only takes a few minutes to set up and you can do other things while the computer is running experiments. In the case where there could be large numbers of experiments then you might want to let the optimizer run while. You can go to lunch or home for the night. When you return, the optimizer will have identified the best solution. It just doesn’t get any better than that!
How Many Experiments Might be Needed
It depends on the number of factors and the number of choices for each of those factors. To figure out the number of scenarios required, multiply number of possible options for one choice by the number of possible options of the next choice. For example if there are 3 job functions (factors) and each of the personnel in those job functions can vary by 5 (i.e. vary from 15 to 20) then the number of possibilities would be 125.
Think of it like this: you have 5 choices for the first factor. For each of those choices, there are 5 choices for the second factor, making 5 * 5 = 25. For each of those 25 combinations, there are 5 choices for the third factor, and so on. Thus, we get 125. It is easy to see how easily the number of experiments can get completely out of hand. If you have only 6 factors, with five choices each, then the number of experiments would be almost to 16,000! If two more factors are added then the number of possible experiments exceeds 390,000.
Design of Experiments
In a complex system, the design of experiments is used to limit the number of possible trials to save time and money. That means some experiments won’t be run because of an arbitrarily decision not to try those combinations. Rather than artificially eliminate possibilities, why not let the computer eliminate possible trials based on the response achieved? That is what simulation optimization does. It does a smart design of experiments and evolves along the way.
It is kind of like the selection process for the Tsetse fly. The weak ones die off and the strong one’s mate and proliferate. In an optimization you can perform over 400,000 possible experiments, but the intelligent optimization of ProcessModel may be able to find you a great solution only after running 270 experiments! That’s intelligent automated design of experiments.
Next, is design of experiments wasn’t really designed to handle a range of possibilities for each factor (i.e. 15 to 20). If a range of possibilities is used then the number of experiments grows dramatically. Now, we are back to analyzing large numbers of experiments to figure out which is best. This is a bad choice, because you still do the work to analyze the results. In a complex system this could mean hundreds of hours of pouring over data find the “best” result.