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Robust Optimisation of Processes and Products by Using Monte Carlo Simulation Experiments

Robert Anderson, JMP Senior Statistical Consultant, SAS robert.anderson

Scientists and engineers often need to find the best settings or operating conditions for their processes or products to maximise yield, performance and conformance to specifications. Most people will be familiar with the term “maximise desirability” in the context of process optimisation, but simulation experiment is a little-known gem within the JMP Prediction Profiler. Somewhat surprisingly, the particular settings that are predicted to give the highest yield or best performance will not always be the best place to operate that process in the long run. Most processes and products are subject to some degree of drift or variation, and the best operating conditions need to take account of that. Simulation experiment does exactly that and goes beyond what maximise desirability can achieve by finding the most robust process settings that will minimise variation in the yield or performance. It also ensures that the process or product conforms as closely as possible to any specifications. Using a case study, this paper will illustrate how simulation experiment achieves this and will demonstrate how – in certain circumstances – simulation experiment can provide a more robust solution than maximise desirability.

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