A New Hybrid Exponentially Weighted Moving Average Control Chart with Repetitive Sampling for Monitoring the Coefficient of Variation

The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. employment SQC methodologies is frequently employed for monitoring identification process irregularities across fields. This research proposes implements a novel methodology agricultural areas. A control chart one the tools that facilitates real-time multiple activities, including yield, industrial hospital outcomes. Advanced charts with symmetrical data are being subjected to new method, which suitable this purpose. aims develop hybrid exponentially weighted moving average detecting coefficient variation (CV) using repetitive sampling method called HEWMARS-CV chart. It an effective tool mean variance simultaneously. used scheme generate two pairs limits enhance performance proposed compared classical HEWMA Shewhart regarding run length (ARL) when normal distribution. Monte Carlo simulation utilized approximate ARL values determine their performance. detects small shifts CV more effectively than existing An illustrative application related monitor wheat yield at Rothamsted Experimental Station Great Britain also incorporated demonstrate efficiency on real shows can detect shift process, it superior terms length.