Development of a Fuzzy Exponentially Weighted Moving Average Control Chart with an α-level Cut for Monitoring a Production Process

Statistical quality control is a useful approach that applies to statistical techniques for monitoring a production system. These charts are effective to monitor the process under certain conditions. On the other hand, the fuzzy set theory is an appropriate tool to deal with an uncertain situation. This paper is fourfold. First of all, triangular fuzzy numbers with an α-level cut technique are used for each sample. The α-level cut technique is sensitive to satisfy the process requirement. Second, a fuzzy exponentially weighted moving average (FEWMA) control chart is proposed with the α-level cut technique. The proposed FEWMA detects small shifts under uncertain situations while using a unity technique for samples. Third, the fuzzy target-focused process capability index (FCpm) index is proposed to measure the fuzzy process performance. Then, a case study is presented to monitor a pumice block plant using the FEWMA control chart with the α-level cut and measure the process performance with the FCpm index. Comparative studies are also presented. By using the proposed FEWMA control chart with the α-level cut, the accuracy and the flexibility of control specification limits are reported for the case study.