Variable Sample Size Based EWMA Control Chart for Mean with Integer Linear Function

This paper introduces the Adaptive Exponential Weighted Moving Average (VAEWMA) Control Chart which has a Variable Sample Size technique to improve process monitoring. Our new method involves an integer linear function that will dynamically change the sample sizes based on the AEWMA statistic value using the smoothing constant, lambda, of the EWMA chart to enhance responsiveness and efficiency. Substantial simulations are made between the VAEWMA and conventional fixed sample size EWMA, Variable Sample Size EWMA (VEWMA) and fixed sample size adaptive EWMA control charts. The proposed Dynamic Sample Size EWMA (VAEWMA) chart proposes the new combination of integer linear sampling adjustment and an adaptive exponential smoothing mechanism. The VAEWMA chart optimizes both variables in the same framework as opposed to the past research that had studied either variable sample size or adaptive weighting alone. This two-fold flexibility improves sensitivity to small and medium changes and uses the economical sampling efficiency, which is evident with obvious benefits over the current EWMA-type control charts. Findings indicate that VAEWMA has a high level of detection, reduced false alarms, and high overall performance. The usefulness and best functionality of our suggested chart is supported by a practical example based on real-life data and, therefore, has a potential to be incorporated in the contemporary quality management practice.