Process capability indices (PCIs) are commonly applied assessment tools which enable the evaluation of process quality during production processes and also allow internal engineers to conveniently and effectively communicate with each other. Many studies have indicated that improving process capabilities not only increases product value but also reduces rates of scrap and rework and betters product availability. Furthermore, enhancing product quality also lengthens product lifespan and delays recovery. Clearly, quality is a crucial factor of corporate sustainability. The quality characteristics of many machine products have asymmetric tolerances, so PCIs with asymmetric tolerances are needed to evaluate these quality characteristics. Many researchers have stressed that sample sizes are not usually large due to cost and technical considerations as well as corporate demands for swift responses. Also, small sample sizes are associated with an increased risk of misjudgement. To address this, we developed a fuzzy evaluation method based on confidence intervals for PCIs with asymmetric tolerances. This approach incorporated expert experience and accumulated data to boost evaluation accuracy and diminish the likelihood of misjudgement resulting from sampling errors.
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