Developing New Methods to Monitor the Fuzzy Logistic Regression Profiles in Phase 𝐈𝐈 (A case study in health-care)

In real quality control applications, the performance of a process or the quality of a product is described by the relationship between a non-metric response variable and one or more control variables. Furthermore, the quality characteristic of a product or process is vague, unreliable, and linguistic and cannot be accurately expressed in most practical applications. This study was carried out aimed to provide a method for monitoring the fuzzy logistic regression profile in Phase II. In these circumstances, there is a need for special diagrams to monitor the performance of this fuzzy data. To this aim, some powerful control charts including Fuzzy exponentially weighted moving average (FEWMA), fuzzy T2 (FT2) control chart have been developed. In addition, to show the performance of the proposed control charts, the fuzzy hypothesis test along with average Run Length (ARL) criterion is used in Phase II. In addition, to show the efficiency of the proposed control chart in real applications, a real case study in health-care has been applied.