Purpose
– The purpose of this paper is to develop an yield-based process capability index (PCI), C
py
, to overcome the shortcomings of existing PCIs that limit their use and lead to inaccurate measures of quality conformance under a variety of common conditions.
Design/methodology/approach
–C
py
is developed conceptually to flexibly and accurately reflect conformance and then used to numerically measure inaccuracies of C
pk
.
Findings
–C
py
overcomes many of the problems associated with existing PCIs, including C
pk
. The degree of process distribution non-normality, level of quality (the sigma level), and whether the process is centered or shifted left or right affect the direction and size of process capability error produced by C
pk
. The accuracy of C
pk
can be greatly affected by process data that deviate even slightly from normality.
Practical implications
–C
py
offers numerous advantages compared to existing PCIs. It accurately reflects process conformance regardless of the process distribution. It is applicable even if the process has multiple characteristics and with both variable and attribute data. Its calculation is relatively simple and the necessary data for it are likely already captured by most organizations.
Originality/value
– The main contributions are the development of a new PCI, C
py
; a conceptual analysis of its advantages; and a numerical analysis of the improved accuracy of C
py
as compared to C
pk
for shifted and non-shifted process means for normal, nearly normal, and highly non-normal distributions over a range of process variability levels.
