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Thomas' (2003) Six Sigma DMAIC (define, measure, analyze, improve and control) quality control system was used to establish a system to enhance consumer satisfaction with the service quality of products marketed on the Internet. This study included five steps. First, we defined importance and satisfaction in service quality. Second, we measured customer acceptability using a survey (containing dimensions of importance and satisfaction), and then constructed a service quality performance matrix to measure low importance, high satisfaction, and high importance, low satisfaction service items. In the third step, analysis, we constructed a Cause-and-Effect Diagram in which the defective service items serve as the major causal categories. We then identified minor causes within these major causal categories, the specific service strategies. For the fourth step, improvement, we used the Quality Function Deployment to locate critical service strategies as targets for improvement. After improvement, a new satisfaction assessment on the defective service items was performed, to confirm the complete improvement on all the defective services. The final step is control, with emphasis on the department of maintenance and marketing, standard operating procedures and knowledge management systems. This application of the Six Sigma quality control system for enhancing the service quality of products marketed on the Internet should reinforce consumer purchase inclination and enhance business performance.

The Taguchi capability index, which reflects the expected loss and the yield of a process, is a useful index for evaluating the quality of a process. Several scholars have proposed a process improvement capability index based on the expected value of the Taguchi loss function as well as the corresponding cost of process improvement. There have been a number of studies using the Taguchi capability index to develop suppliers’ process quality evaluation models, whereas models for evaluating suppliers’ process improvement potential have been relatively lacking. Thus, this study applies the process improvement capability index to develop an evaluation model of the supplier’s process improvement capability, which can be provided to the industry for application. Besides, owing to the current need to respond quickly, coupled with cost considerations and the limits of technical capabilities, the sample size for sampling testing is usually not large. Consequently, the evaluation model of the process improvement capability developed in this study adopts a fuzzy testing method based on the confidence interval. This method reduces the risk of misjudgment due to sampling errors and improves the testing accuracy because it can incorporate experts and their accumulated experiences.

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Many of the nominal-the-best quality characteristics of important machine tool components, such as inner or outer diameters, have asymmetric tolerances. An asymmetric tolerance index is a function for the average of the process and the standard deviation. Unfortunately, it is difficult to obtain the 100(1−α)% confidence interval of the index. Therefore, this study adopts Boole’s inequality and DeMorgan’s theorem to find the combined confidence region for the average of the process as well as the standard deviation. Next, using the asymmetric tolerance index for the target function and the combined confidence region for the feasible region, this study applies mathematical programming to find the confidence interval as well as employs this confidence interval for statistical hypothesis testing. Lastly, this study demonstrates the applicability of the proposed approach with an illustrative example.