Comparative service quality system analysis using the Six Sigma benchmark

Abstract

PurposeThis paper aims to show that the extent to which convergence/divergence of a company's quality policies and practices towards/away from those of Six Sigma benchmark policies and practices mirror and anticipate the divergence of its sigma metric (SMs) from quantitative Six Sigma benchmarks. Further, the paper proposes to evaluate the robustness of the quality processes of these three companies and to compare them to that of the Six Sigma benchmark by subjecting these processes to the twin performance shocks of the benchmark Six Sigma 1.5σ allowance for process drift and a 25 percent tightening of customer requirements.Design/methodology/approachUsing a novel methodology more appropriate to the critical quality characteristics of typical service industry companies, the paper computes a set of SMs for each company that is richer and broader than the metrics found in standard Six Sigma tables. This new methodology is based on the empirically observed defect rates that are currently being generated by a service process. Further, based on the available empirical data, the paper compared these metrics to the Six Sigma benchmarks.FindingsFirst, the paper shows that it is possible to compute a broad array of Six Sigma metrics for service businesses based on defect rate data. Second, the results confirm the central proposition of the research to the effect that the divergence/convergence of the qualitative characteristics of a company's quality system from benchmark Six Sigma policies and practices mirror and anticipate the convergence/divergence of the company's quality metrics from the Six Sigma benchmark. Third, the research produced the unanticipated result that the quantitative quality performance of high‐performing service businesses on the Six Sigma metrics are much lower than anticipated and below what is normally achieved by their manufacturing counterparts. The results were also used to do an evaluation of the Taguchi robustness of service processes.Originality/valueFirst, the paper demonstrates that traditional Six Sigma computational methodology for generating Six Sigma metrics that is prevalent in manufacturing applies equally to service businesses. Second, the parallel convergence of the qualitative characteristics of a company's quality system towards Six Sigma practices and its quantitative metrics towards the Six Sigma benchmark means that primacy must be given to quality practices as the drivers of quality improvement. Third, the fact that high‐performing service businesses achieve Six Sigma measures that are so low compared to their manufacturing counterparts seems to point either to some key measurement challenges in deploying Six Sigma in service industries or to the need to further change Six Sigma methodology to make it more applicable to these businesses.