New Designing Mixed Double Moving Average-Cumulative Sum Control Chart for Detecting Mean Shifts with Symmetrically and Asymmetrically Distributed Observations

Abstract

Herein, we present a new control chart called the mixed double moving average-cumulative sum control chart (DMA-CUSUM: MDC) used for detecting shifts in the process mean when symmetrically and asymmetrically distributed. The performance of the MDC chart is compared with shewhart, cumulative sum (CUSUM), double moving average (DMA) and mixed cumulative sum-double moving average (CUSUM-DMA: MCD) control charts by using average run length (ARL) and median run length (MRL) with the monte carlo simulation (MC). The research results show that the proposed (MDC) control chart was more efficient than the Shewhart, CUSUM, DMA and MCD charts for all distributions tested. We apply the MDC chart to real sets of data: I) the tensile data of single carbon fiber and II) the survival times of guinea pigs infected with virulent bacilli.