The purpose of this paper is to assess and benchmark Six Sigma strategies in services sector, namely, the telecom field, by establishing tables of fallouts of non-conforming services and their associated costs along with a custom data envelopment model for benchmarking the different strategic alternatives.
Under normality assumption, process fallout in Six Sigma is around 0.002/3.4 part per million for a centered/shifted process. By introducing Six Sigma to applications in services sector, normality assumption may no longer be valid; hence, fallouts of non-normal attributes are computed for different one-sided quality levels. The associated costs of strategy deployment, fallout and transaction completion are all considered. Data envelopment analysis model is also established to benchmark the Six Sigma strategic plans. The strategies are detailed down to processes and to quality characteristics which constitute the decision-making units. The efficiency of each service unit is computed using both CCR and super efficiency models.
The amount of efforts/costs needed to reduce the variation in a service may differ according to the targeted quality level. For the same Six Sigma quality level, services demonstrate different performance/efficiencies and hence different returns. In some scenarios, moderate quality levels could present high efficiencies as compared to services of higher levels. It was also found that the required improvement is less in the case of Log-normal as compared to normal distributions at some quality levels. This observation is also noted across the presented distributions of this study (Normal, Log-normal, Exponential, Gamma and Weibull).
The deployment of Six Sigma in services is mostly found in time-related concepts such as timeliness of billing, lifetimes in reliability engineering, queueing theory, healthcare and telecommunication.
The paper contributes to the existing research by presenting an assessment model of Six Sigma strategies in services of non-normal distributions. Strategies of different quality levels present diverse efficiencies; hence, higher quality levels may not be the best alternatives in terms of the returns on investment. The computed fallout rates of the different distributions can serve as palm lines for further deployment of Six Sigma in services. Besides, the combination of optimization and Six Sigma analysis provides additional benchmarking tool of strategic plans in both manufacturing and services sector.
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