Process Capability Analysis via Continuous Ranked Probability Score

Liangxing Shi, Hongye Ma, Dennis K.J. Lin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2823-2834
Number of pages12
JournalQuality and Reliability Engineering International
Issue number8
StatePublished - Dec 1 2016

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


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