Shift, narrow, and chop to improve process capability

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.