Process capability analysis is an important aspect of quality control. Various process capability indices are proposed when the distribution is normal. For non-normal cases, percentile and yield-based indices have been introduced. These two methods use partial features of a process distribution, such as key percentiles and the proportion of non-conforming (PNC) to estimate the process capability. However, these local features may not reflect the uniformity of a process appropriately when the distribution is non-normal. In this paper, continuous ranked probability score (CRPS) is introduced to process capability analysis and a CRPS-based approach is proposed. This method can assess the dispersion of process variation across the overall distribution and is applicable to any continuous distribution. An example and simulations show that CRPS-based indices are more stable and accurate indicators of process capability than the existing indices in reflecting the degree of process fluctuation.
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Management Science and Operations Research