A fuzzy approach to determine process quality for one-sided specification with imprecise data

Abstract

Process quality is a crucial determinant of client satisfaction and affects a product’s value; therefore, maintaining quality control is a vital aspect of corporate sustainability and development. Many researchers have developed evaluation models for process quality. The majority of such research assumes that collected measurement data are precise, but fuzziness and stochastic uncertainty are unavoidable features of any collected data. When the measurements of a quality characteristic are insufficiently precise, a crisp-based approach is not suitable for the assessment of process quality. This study endeavored to use one-sided Six Sigma quality indices as measurement tools to accurately reflect process yield and quality levels. Taking Buckley’s approach into consideration, we extend the crisp estimators from the indices into fuzzy estimators. We then develop a fuzzy hypothesis testing method for one-sided Six Sigma quality indices, with the intent of increasing reliability of evaluation for process quality levels. Finally, we present a real-world case to illustrate implementation of the proposed approach, demonstrating its effectiveness and practical applicability.