After a product has undergone a manufacturing process, it usually has several important quality characteristics. When the process quality of all quality characteristics meets the requirements of the quality level, the process quality of the product can be guaranteed to satisfy customers’ needs. A large number of studies have pointed out that good process quality can raise product yield and product value; at the same time, it can reduce the ratio of rework and scrap, achieve the effect of energy saving and waste reduction, and contribute to the sustainable operation of enterprises as well the environment. Since the six sigma method combines the statistical analysis method of manufacturing cost and production data, it is a useful tool for process improvement and process quality enhancement. Therefore, this paper adopted the six sigma-define, measure, analyze, improve and control (DMAIC) improvement process to lift the manufacturing process quality of multi-characteristic products. Besides, the Taguchi process capability index is one of the commonly used tools for quality assessment in the industry. Not only can it reflect the process loss, but it also can ensure the process yield when the index value is large enough. Consequently, this paper discussed the relationship between the Taguchi process capability index and the six sigma quality level. Meanwhile, the entire six sigma DMAIC improvement process was built on the basis of the process capability index and developed by the method of statistical quality control. Hence, the proposed method is very convenient for process engineers to apply, as well as is helpful for enterprises to move toward the goal of smart manufacturing and sustainability.

Taiwan is a major exporter and producer of machinery and machine tools in the world. There are at least hundreds of components for various machining machines. According to the concept of Taguchi loss function, when the process quality of the spare parts of machining machines is not good, the failure rate will increase after the product is sold, resulting in an increase in maintenance costs and carbon emissions. As the environment of the Internet of Things (IoT) becomes more common and mature, it is beneficial for manufacturers of machining machines to collect relevant information about process data from outsourcers, suppliers, and machining machine factories. Effective data analysis and application can help the machining machine industry move towards smart manufacturing and management, which can greatly reduce the average number of failures per unit time for all sold machines. Therefore, this paper developed a practical evaluation and improvement decision-making model for the machining operation performance to help machining machine manufacturers find out the components that often fail and improve them, so as to reduce the total loss caused by machine failures. This paper first defined the machining operation performance index for the machining machines and discussed the characteristics of this operation performance index. Subsequently, the confidence interval of the index was deduced, a fuzzy evaluation model based on this confidence interval was proposed, and decision-making rules regarding whether to make any improvement was established. The fuzzy evaluation and improvement decision-making model for the operation performance of machining machines proposed in this paper will contribute to various tool industries to boost their process quality, reduce costs, and lower carbon emissions, in order to achieve sustainable management of enterprises and the environment.

Numerous key components of tool machines possess critical smaller-the-better-type quality characteristics. Under the assumption of normality, a one-to-one mathematical relationship exists between the process quality index and the process yield. Therefore, this paper utilized the index to produce a quality fuzzy evaluation model aimed at the small-the-better-type quality characteristics and adopted the model as a decision-making basis for improvement. First, we derived the 100(1 $-$$\alpha$)% confidence region of the process mean and process standard deviation. Next, we obtained the 100(1 $-$$\alpha$)% confidence interval of the quality index using the mathematical programming method. Furthermore, a one-tailed fuzzy testing method based on this confidence interval was proposed, aiming to assess the process quality. In addition, enterprises’ pursuit of rapid response often results in small sample sizes. Since the evaluation model is built on the basis of the confidence interval, not only can it diminish the risk of wrong judgment due to sampling errors, but it also can enhance the accuracy of evaluations for small sample sizes.

The random variable X is used to represent the normal process containing two important parameters—the process average and the process standard deviation. The variable is transformed using Y = (X − T)/d, where T is the target value and d is the tolerance. The average of Y is then called the accuracy index, and the standard deviation is called the precision index. If only the values of the accuracy index and the process precision index are well controlled, the process quality level as well as the process yield are ensured. Based on this concept, this paper constructed a control chart for the accuracy index and the precision index and derived the confidence intervals of the accuracy index and the precision index using in-control data, as the process was stable. This paper aims to control process quality via monitoring the accuracy and precision of the process. At the same time, fuzzy tests are developed for the indicators of process accuracy and precision to evaluate whether the process quality can reach the k-sigma quality level, as well as offer suggestions about directions of improvement when it fails to reach the k-sigma quality level. Obviously, the model in this paper cannot only evaluate whether the process meets the requirements of the quality level; it can also provide a decision regarding whether the process should be improved. It is very helpful for the enhancement of enterprises’ process capabilities.

As global warming becomes increasingly serious, humans start to consider how to coexist with the natural environment. People become more and more aware of environmental protection and sustainable development. Therefore, in the pursuit of economic growth, it has become a consensus that enterprises should be responsible for the social and ecological environment. Regarding the manufacturing of electronic devices, as long as both component production quality and assembly quality are ensured, consumers can be provided with high-quality, safe, and efficient products. In light of this trend, enhancing product availability and reliability can reduce costs and carbon emissions resulting from repairing or replacing components, thus becoming a vital factor for corporate and environmental sustainability. Accordingly, enterprises enhance their economic benefits as well as have the effects of energy conservation and waste reduction by extending products’ service lifetime and increasing their added value. According to several studies, it takes a long time to retrieve electronic products’ lifetime data. Moreover, acquiring complete samples is often challenging. Consequently, when analyzing real cases, samples are usually collected using censoring techniques. The type-I right censoring data is suitable for industrial processes. Thus, this study utilized type-I right censoring sample data to estimate the lifetime performance index. It usually takes a large amount of time to access lifetime data for electronic products and it is often impossible to obtain complete samples since the size of the sample is usually small. Hence, to avoid misjudgment caused by sampling errors, this study followed suggestions from existing research and applied fuzzy tests built on confidence intervals to establish a fuzzy evaluation model for the lifetime performance index. This model helps relevant electronic industries not only evaluate the lifetime of their electronic components but also instantly seize opportunities for improvement.

Numerous advanced industrial countries emphasize green environmental protection alongside athletic healthcare. Many world-renowned sports brands are actively developing highly functional, environmentally friendly, and aesthetically pleasing products. For example, in the production of sports shoes, the eco-friendly yarn process is one of the important processes. This process involves multiple crucial larger-the-better quality characteristics closely tied to the functionality of sports shoes. Facing green environmental regulations and external competitors, it is evidently an imperative issue for enterprises to consider how to improve the quality of newly developed products, increase product value, and lower rates of both rework and scrap to accomplish the goals of saving energy and minimizing waste. Aiming to solve this problem, this study proposed a fuzzy evaluation model for products with multifunctional quality characteristics to assist the sporting goods manufacturing industry in evaluating whether all functional quality characteristics of its products meet the required quality level. This study first utilized the larger-the-better Six Sigma quality index concerning environmental protection for evaluation and then proposed product evaluation indicators for the eco-friendly yarn. Since the parameters of these indicators have not yet been determined, sample data need to be used for estimation. Enterprises require rapid response, so that the sample size is relatively small. Sampling error will increase the risk of misjudgment. Therefore, taking suggestions from previous studies, this study constructed the fuzzy evaluation model based on confidence intervals of quality indicators for the eco-friendly yarn. This method incorporated previous experience with data, thereby enhancing assessment accuracy.