Use of Fuzzy Sets in Process Capability Analysis: A Comparative Literature Analysis

Process capability analysis (PCA) is the assessment of a process’s ability to meet customer requirements specified by constraints. It is a crucial aspect of statistical process control used to evaluate process variability. PCA provides data on both conforming and nonconforming production rates, which represent the quantity of items that meet and do not meet SLs, respectively. Processes can be categorized as “capable”, or “incapable” based on their process capability indices (PCIs). The fuzzy set theory can effectively address ambiguity and enhance flexibility and sensitivity in classical PCIs. To achieve this goal, upper and lower specification limitations can be represented using linguistic variables. Fuzzy process capability indexes (FPCIs) are generated by utilizing fuzzy mean and fuzzy variance. This study aims to provide an extensive literature evaluation on publications about FPCIs. The research was assessed based on many parameters. The study presented classifications such as FPCI, application area, fuzzy parameters, and type of fuzzy sets. We aimed to create an outline for researchers in this subject and discuss recent advancements in FPCIs and fuzzy PCA.