TY - JOUR
AU - J. Gaylord May
AB - Purpose – The purpose of this paper is to devise a robust statistical process control methodology that will enable service managers to better monitor the performance of correlated service measures. Design/methodology/approach – A residuals control chart methodology based on least absolute value regression (LAV) is developed and its performance is compared to a traditional control chart methodology that is based on ordinary least squares (OLS) regression. Sensitivity analysis from the goal programming formulation of the LAV model is also performed. The methodology is applied in an actual service setting. Findings – The LAV based residuals control chart outperformed the OLS based residuals control chart in identifying out of control observations. The LAV methodology was also less sensitive to outliers than the OLS approach. Research limitations/implications – The findings from this study suggest that the proposed LAV based approach is a more robust statistical process control method than the OLS approach. In addition, the goal program formulation of the LAV regression model permits sensitivity analysis whereas the OLS approach does not. Practical implications – This paper shows that compared to the traditional OLS based control chart, the LAV based residuals chart may be better suited to actual service settings in which normality requirements are not met and the amount of data is limited. Originality/value – This paper is the first study to use a least absolute value regression model to develop a residuals control chart for monitoring service data. The proposed LAV methodology can help service managers to do a better job monitoring related performance metrics as part of a quality improvement program such as six sigma.
TI - Monitoring service quality with residuals control charts
JF - Managing Service Quality
DO - 10.1108/09604520910943161
DA - 2009-03-20
UR - https://www.deepdyve.com/lp/emerald-publishing/monitoring-service-quality-with-residuals-control-charts-PU4XD5dBQS
SP - 162
EP - 178
VL - 19
IS - 2
DP - DeepDyve
ER -