Many important parts of tool machines all have the important smaller-the-better (STB) quality characteristics. The important STB quality characteristics will impact on the quality of the end-product. At the same time, supplier quality influences the quality and functionality of the end-product, so suppliers must be selected with caution. The six sigma quality index for the STB quality characteristics can directly reflect process quality levels. Besides, this index possesses a mathematical relationship with process yield. Nevertheless, the point estimation will cause the risk of misjudgment, due to sampling errors. As a result, this study applies the confidence interval of the index to a two-tailed fuzzy testing method, in order to select appropriate suppliers. Now that this method is on the basis of the confidence interval, the possibility of misjudgment caused by sampling errors will be reduced, while the precision of the selection will be enhanced. The method can help companies increase product quality, as well as the competitiveness of the industry chain as a whole. Finally, a numerical example is presented to show how to approach this method and its efficacy.

Reliability and product process quality are essential in meeting market demand and enhancing competitiveness in the machine tool industry. In addition, manufacturing time performance is also one of the important indices. Therefore, this paper focuses on process quality and manufacturing time and defines a manufacturing time performance index to feedback the acceptance rate of the manufacturing time. The process performance evaluation chart delineated the observations of variations in various workstations, and, hence, in controlling the stability of the work process. The Six Sigma quality indices are constructed by the process accuracy indices and precision indices are represented by X-axis and Y-axis respectively. The process quality evaluation chart evaluates the level of the process quality, as well as proposes the direction of improvement. The manufacturing time performance—Z-axis is used to assess whether the manufacturing time performance meets the requirements. The process performance evaluation chart constructed by this paper makes it easier for researchers to observe which workstations have a process variation in the process, to control the stability of the process effectively, to provide the improvement reference for staff on the scene and to enhance the competitiveness of the industry.

Purpose: This study aims to enhance estimation accuracy and address uncertainty in measurement data by deriving confidence intervals for the Six Sigma Quality Index based on statistical inference results. A fuzzy testing method is then proposed, utilizing confidence intervals as an evaluation framework for process quality. Design/methodology/approach: First, confidence intervals for the Six Sigma Quality Index are derived based on the statistical inference results. Next, these confidence intervals are employed to construct a fuzzy estimation of the index. Finally, fuzzy numbers and their membership functions are developed to create a fuzzy hypothesis testing model using the derived confidence intervals. Findings: The fuzzy testing method proposed in this study is grounded in the use of confidence intervals. It not only mitigates the risk of misjudgment caused by sampling errors but also offers a more comprehensive approach compared to traditional statistical testing methods. Originality/value: The Six Sigma Quality Index functions not only as a bridge between businesses and customers but also as a tool for internal engineers to assess and analyze processes and propose improvements. However, since the index involves unknown parameters, sampled data are employed for estimation. To improve estimation accuracy and address uncertainty in measurement data, this paper derives the confidence intervals of the Six Sigma Quality Index based on statistical inference results. Building on these results, it proposes a fuzzy testing method that utilizes confidence intervals as a novel approach for evaluating process quality.