Response surface methodology: 1966–1988
Technometrics
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Leading six sigma: a step-by-step guide based on experience with ge and other six sigma companies
Leading six sigma: a step-by-step guide based on experience with ge and other six sigma companies
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When output random variables are a function (known as a transfer function) of input random variables, Monte Carlo simulation has often been used to examine the sensitivity of the outputs to changes to the inputs. An important and commonly used measure of the outputs is their process capability (the probability that an output is within specification limits). In this paper, we show how to efficiently conduct extensive analysis of the sensitivity of the process capability of outputs to changes to inputs. Specifically, we show how a single set of simulation replications can be used to efficiently estimate the process capability as a function of each input random variable's values, its parameters, and truncation of its values at chosen limits. The approach is extremely flexible; the effects of changes to the distributional form of an input variable alone or in combination with the previously mentioned changes are easily evaluated.