A Comparison of Exponentially Weighted Moving Average Charts for Time and Magnitude Monitoring

Abstract

Science and technology advancements have transformed a wide range of working contexts, including manufacturing and healthcare. For processes to be accurate in relation to the specifications, an assignable cause must be quickly and precisely found. The aim of the study is to compare the EWMA control charts. Traditionally, EWMA charting method ology has been used to track processes with moderate to large shifts. We have modified the EWMA methodology to develop the memory-type rate based EWMA control chart for simultaneous monitoring of time and magnitude of an event. Specifically, the exponential distribution is considered combining with the gamma distribution to develop the suggested chart. In this comparison study, we have used existing Max-EWMA control chart with the suggested chart to claim best performer in the charting scenario. Numerous Monte Carlo simulations are used to access the performance of these charts utilising the ARL crite rion. In addition, the charting methodologies are scrutinised on real data sets. The results show that the Max-EWMA control charts is more efficient in detecting simultaneous small to medium-sized shifts with smaller smoothing parameter than the EWMA-Rate control chart. However, the EWMA-Rate chart can detect larger shifts with larger smoothing parameter more quickly. The real data sets have shown the fact precisely