You might notice the spreadsheet calculations for production are always wrong. The higher the variability in the system, the lower the accuracy. What’s worst is the spreadsheet always says production should be much higher. Some of the calculations are off by more than 50%. Predictions of proposed system performance aren’t even close. Why?
Most people generally don’t know what to do with variability, ignoring it. Instead, they take the average arrival quantities, processing times, and resource availability and use the calculation to arrive at a prediction. Unfortunately, these numbers have been so wrong that management has lost confidence in the projections.
Let’s illustrate the problem with two distributions (formulas accounting for time variation) next to each other. The most straightforward system like this is a single queue feeding a single server. People arrive at the line according to a variable rate and process at the server at a variable rate.
If people arrive faster than the server can process, they wait in the line – their production time extends. However, production capability is lost if people enter the line slower than the server handles each customer. That production capability never recovers – it’s lost forever.
Open the “variability kills” model and simulate using averages. Then change the model to include the distributions shown on the layout. Answer the following questions.
|What is the expected production for 40 hours?||_______||_______|
|What is the maximum number of people in line?||_______||_______|
|What is the longest wait time expected?||_______||_______|
There is more to discuss regarding variability, including how to create distributions, replications, and understanding the output report.
This example shows a problem with only two distributions in the simulation. Can you imagine the potential variability problems from arrivals, resource availability, processing times, and different types of items working through an extensive process?
Variability is crucial to understanding the behavior of a system and in developing models that predict production capability.