Service quality variation monitoring using the interquartile range control chart

Abstract

Su-Fen Yang*a & Ting-An Jiangaa Department of Statistics, National Chengchi University, Taipei, TaiwanSu-Fen Yang is a Distinguished Professor at National Chengchi Univeristy, Taiwan. She received a Ph.D. in Statistics from the University of California, Riverside, U.S.A. She is the author of one book on Quality Management and has been a member of the Quality Management Committee of the Ministry of Economic Affairs Bureau of Standards, Taiwan; Committee of Chinese Society for Quality; an Associate Editor for Journal of Quality and JCIIE. Her research interests are mainly in statistical process control, quality engineering, data analysis and applied statistics.Ting-An Jiang received a M.S. degree from National Chengchi University, Taiwan, in 2014. She has been an Engineer at a DMS Corporation since 2017. Her research interests are in big data analysis and AI.CONTACT Su-Fen Yang yang@mail2.nccu.tw Department of Statistics, National Chengchi University, Taipei, TaiwanABSTRACTControl charts are effective tools for signal detection for both manufacturing and service processes. Much of the data in service industries come from processes exhibiting non-normal or unknown distributions, for which Shewhart variables control charts are not appropriate. This paper thus proposes a new exponentially weighted moving average (EWMA) interquartile range control chart with single sampling and double sampling schemes, respectively, for detecting the out-of-control variance/standard deviation of a critical quality characteristic that exhibits a non-normal or unknown distribution. We explore the sampling properties of the new monitoring statistics, and calculate the average run length of the proposed chart to compare the out-of-control detection performance with existing nonparametric variance charts/standard deviation for cases in which the critical quality characteristic follows the normal and non-normal distributions. Comparison results show that the proposed double sampling EWMA interquartile range control chart exhibits better detection performance than that of the single sampling EWMA interquartile range control chart. Moreover, the former always outperforms existing control charts for small and medium shifts in process variance/standard deviation. Finally, we use a numerical example with service data from a local bank branch to illustrate the application of the proposed control chart.

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