Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics

Maintaining high levels of process quality is crucial to the competitiveness of manufacturing firms in today's increasingly global marketplace. To ensure the quality of manufactured products meets customer needs, process capability indices (PCIs) are widely used to analyze the process performance of various processing characteristics. Products characterise by processing characteristics of both unilateral and bilateral specifications are common in the current sales market. Manufacturing firms must often adopt multiple PCIs to analyze the process performance of a single product, which is inefficient in practical applications and management. Yield-based index is not subject to this limitation. For this reason, we employed to evaluate process performance and the effectiveness of improvement measures. In practice, is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the confidence interval of and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of and develops the fuzzy judgement model.