Experiment Types

There are 4 types of experiments defined in ChiAha Express:

• Single Run

• Multi Run

• Loss Gain

• Buffer Tradeoff

Single Run

A single run is the simplest experiment you can run in ChiAha Express. It is a good way to benchmark your model against your real-life process or just see how the model is running. In a single run, you specify the time period for the simulation to run, and the simulation runs only once. This is the fastest way to get results and can give a good estimate of how the simulation is performing.

Multi Run

Multi run experiments are a collection of identical single run experiments. For this experiment, the setup is very similar to single run but with the addition of a parameter to specify the desired number of replications. Because of the random nature of simulations, each run has the chance to give different results. An easy way to control for this variability is to run multiple replications of the same experience and view the results as a whole. The more replications run, the higher confidence you can have in the output. The tradeoff is runtime. Each replication will increase runtime slightly, so it is advisable to make sure the simulation is giving reasonable results for single run or a low number of replications before committing to an extremely high number of replications.

Loss Gain

The loss/gain experiment is used to help identify the biggest opportunities for OEE improvement. This experiment looks at all of the different interrupts in the model and shows their impact on OEE. Output allows the user to see which interrupts are causing the highest OEE losses, as well as which interrupts would result in the highest OEE gains if eliminated. This lets users intelligently target different interrupts to get the best return from any investment in addressing interrupts.

Buffer Tradeoff

The buffer tradeoff experiment is used to help identify the optimal sizes for the buffers in the system. This experiment will focus on a single buffer. The simulation is run multiple times with different sizes for the buffer being studied. Output allows the user to see the effect each buffer size would have on OEE.