Using Monte Carlo Simulation to Understand the Sensitivity of a Complex System

Can averages be a dangerous measurement on which to base your decisions?

According to Britt Calloway, Research and Development Engineer for Bastian Solutions, they can be. In Britt’s case, he used a toy robot to model target throughput of an actual robot in a palletizing operation, and his concerns over uncertainty in this test lead him to @RISK.

Britt discovered that errors in picking up pieces of puzzle parts with his robot arm could drastically affect the cycle time he was measuring. For example, he would bungle the grab for a part, and it would slide away from the robot. He initially thought he would reject the mistakes in the test, but then realized those types of anomalies happen all of the time in the real world, and they would need to be represented as uncertainty in a model using Monte Carlo Simulation.

Britt elaborated . . . “In a robotic palletizing system, there can be box flaps missing, vacuum cups that my need to be replaced, human error, maintenance downtime, and other factors that affect throughput . . . Monte Carlo methods are a way to use engineering insight and more qualitative assessments of your inputs to define a quantitative output.”

Fortunately for Britt, the robot was a toy, but as you can see in his blog, Monte Carlo simulation is no toy when time and money are concerned.

See Britt’s blog here:

Published by shunt27

I am a Lean Six Sigma Black Belt at Palisade Corporation.

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