Production quality and yield assurance for processes with multiple independent characteristics

W.l. Pearn*, Chien Wei Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Process capability indices have been widely used in the manufacturing industry providing numerical measures on process potential and process performance. Capability measure for processes with single characteristic has been investigated extensively, but is comparatively neglected for processes with multiple characteristics. In real applications, a process often has multiple characteristics with each having different specifications. Singhal [Singhal, S.C., 1990. A new chart for analyzing multiprocess performance. Quality Engineering 2 (4), 397-390] proposed a multi-process performance analysis chart (MPPAC) for analyzing the performance of multi-process product. Using the same technique, several MPPACs have been developed for monitoring processes with multiple independent characteristics. Unfortunately, those MPPACs ignore sampling errors, and consequently the resulting capability measures and groupings are unreliable. In this paper, we propose a reliable approach to convert the estimated index values to the lower confidence bounds, then plot the corresponding lower confidence bounds on the MPPAC. The lower confidence bound not only gives us a clue minimum actual performance which is tightly related to the fraction of non-conforming units, but is also useful in making decisions for capability testing. A case study of a dual-fiber tip process is presented to demonstrate how the proposed approach can be applied to in-plant applications.

Original languageEnglish
Pages (from-to)637-647
Number of pages11
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - 1 Sep 2006


  • Bootstrap sampling
  • Lower confidence bound
  • MPPAC control chart
  • Process capability indices

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