The computational methods described in DIN (Deutsche Industrie Norm) 55319 (see Deutsches Institut fuer Normung e.V., 2002) and ISO 21747 (see ISO, 2006) provide rules for computing the appropriate process capability indices given 1) different types of distributions of individual measurements (observations) within sample, 2) distributions of sample means (locations), standard deviations (dispersion), and distribution shapes over time, and 3) resultant distributions (given the distribution of measurements within each sample, and of samples across time).

These methods are applicable for situations where consecutive samples of observations are taken from an ongoing production line, and the goal is to estimate the process capability with respect to one or more quality dimensions measured in those samples. Specifically, these standards summarize different distribution "models" for how 1) the observations within each sample can be distributed, 2) how the moments (locations, dispersions, etc.) of consecutive samples can be distributed (e.g., normal, non-normal) over "time," and 3) how best to estimate the process capability based on the resultant distribution (given the distribution of measurements within sample, and across samples/time).

See Overview of Time-Dependent Distribution Models in the Process Analysis module topics for additional details.