Service quality variation monitoring using the interquartile range control chart. Issue 5 (3rd September 2019)

• Record Type: Journal Article
• Authors: Yang, Su-Fen; Jiang, Ting-An
• Is Part Of: Quality technology & quantitative management. Volume 16:Issue 5(2019)
• Page Start: 613
• Page End: 627
• Publication Date: 2019-09-03
• Subjects: Control chart -- process variability -- interquartile range -- free-distribution -- average run length; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2018.1519921
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: Control 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.