With the maturity of the Internet of Things (IoT), and especially after the COVID-19 pandemic, online courses at all levels of education in Taiwan have become mainstream, often replacing in-person instruction. Improving the operational performance of e-learning systems can enhance learner satisfaction, attract more geographically dispersed learners, create more business and increase economic activity. Additionally, since learners can study from home, this reduces traffic impact and lowers carbon emissions, thereby alleviating environmental pollution. A solid evaluation and improvement decision-making model for e-learning systems helps system administrators understand user satisfaction across various service components. Therefore, this paper designed a questionnaire to investigate user satisfaction. A performance evaluation matrix was constructed using the satisfaction index of each service item as the horizontal axis and the influence index of the correlation between each service item and overall satisfaction as the vertical axis. Using statistical testing principles, we also propose an improvement decision model to identify critical-to-quality (CTQ) items. Under limited resources, this model can assist administrators in prioritizing which service items to improve to enhance overall learner satisfaction.

Improving the quality and yield of integrated circuit packaging processes is crucial for reducing scrap rates and component failures. Among all stages of the packaging chain, gold wire bonding is widely considered a critical process with a clearly defined role. According to the Taguchi loss function, insufficient gold wire bonding capability significantly degrades the performance of integrated circuit components, leading to premature failure. Due to the indispensable role of integrated circuit components in applications such as network communications, consumer electronics, and the automotive, industrial, aerospace, and defense sectors, any failure could disrupt operations, cause economic losses, and increase carbon emissions. This makes rapid decision-making essential and has led to a growing trend towards decision-making based on small sample sizes. Furthermore, the increasingly mature smart manufacturing environment and the continuous advancements in the Internet of Things as well as production data collection and analysis technologies have facilitated the accumulation of past professional data experience within the industry. This study thus proposes a confidence interval-based fuzzy evaluation method to establish a fuzzy evaluation model for the operational performance of integrated circuit components based on gold wire bonding process capability. Given that this method integrates previously accumulated professional data and experience and is based on confidence intervals, not only can the accuracy of decision-making be maintained, but the risk of misjudgment due to sampling errors even with a small sample size can also be reduced.

PurposeThis 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/approachFirst, 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.FindingsThe 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/valueThe 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.

This study examines how government policy tools shape consumer adoption of battery electric vehicles (BEVs) in Taiwan. By extending the Technology Acceptance Model (TAM) focusing on three external government policy factors—legislative direction, monetary incentives, and usage-based benefits—this study uses two factors, including perceived usefulness (PU) and perceived ease of use (PEOU), to evaluate behavioral intention to use (BI), or purchase, BEVs. Utilizing PLS-SEM, survey data from 238 respondents were analyzed. The results suggest that legislative direction had no significant impact on PU or PEOU, while monetary incentives influenced only PEOU. In contrast, usage-based benefits strongly predicted both PU and PEOU. In addition, PU also partially mediates the relationship between PEOU and BI. These findings extend the TAM by situating public policy as a measurable driver of technology adoption, especially in the case of BEVs. For Taiwan, the results suggest that governmental policies focused on increased visibility and accessibility are more attractive than abstract regulatory frameworks in encouraging BEV adoption.

Quality characteristics involving asymmetric tolerances occur in many production processes. Cpm '', a generalized Taguchi capability index, has been utilized to measure the manufacturing performance with asymmetric tolerances. Most investigations on this index assume a normal distribution when analyzing samples. In contrast, this study explored a natural estimator of Cpm '' using subsamples. The limiting distribution and associated large-sample properties of the estimator were examined for the general population using the fourth central moment. This study thus derived an approximate (1 - alpha)100% confidence interval of Cpm ''. However, collected data are often imprecise. To address this problem, this study derived a triangular fuzzy number for Cpm ''& lowast; by constructing a series of confidence intervals and employed it to develop fuzzy testing for determining whether the process is capable. A case study was given to illustrate how the proposed procedure was used for evaluating process capability.

PurposeWith the gradual maturity of the Internet of Things, various smart mobile applications (apps) have emerged, including smart transportation apps, that facilitate public carpooling and efficient shared transportation. This paper proposes a fuzzy performance evaluation and management model for urban bicycle-sharing operations with the objectives of lowering the bicycle failure rate and the social losses of the sharing economy.Design/methodology/approachThis paper proposes a performance evaluation index to monitor failure rates in urban bicycle-sharing. We derive the confidence interval of this index and define the triangular fuzzy number and its fuzzy membership function according to this confidence interval. We use these to propose a fuzzy evaluation and improvement testing model for urban bicycle-sharing. A practical application illustrates the efficacy of the proposed approach.FindingsSmart transportation apps help reduce the number of vehicles on the road, thereby decreasing traffic jams as well as total carbon emissions. Failures in urban bicycle-sharing operations thus cause both economic and environmental social losses. The proposed approach is aimed at enhancing the quality and reliability of bicycle-sharing to reduce losses in the sharing economy.Originality/valueThis paper presents a comprehensive fuzzy performance evaluation and management model for urban bicycle-sharing operations. It is among the first to focus on reducing the bicycle failure rate and associated social losses. The model aims to improve operational performance and diminish various social losses in the sharing economy.

A segment of the machine tool industry in Taiwan specializes in manufacturing equipment tailored to the semiconductor sector. Due to the stringent quality requirements inherent in semiconductor manufacturing processes, the machine tools employed in these processes are also subject to correspondingly rigorous quality standards. In fact, a machine tool is assembled from hundreds of individual components, each of which must meet the required standards to ensure that the final product adheres to overall quality requirements. Similarly, each component possesses multiple quality characteristics, all of which must individually satisfy the specified criteria to guarantee that the component achieves the required quality level. Clearly, without a comprehensive evaluation model, ensuring final product quality is difficult. To address this practical issue, this study employed the Process Capability Index (PCI), the most widely used process capability index in the industry, and based on statistical verification principles, constructed a quality assessment and analysis model applicable to products with multiple quality characteristics. This approach enables process engineers to simultaneously evaluate all product quality characteristics and determine whether they meet the desired quality standards. For products that do not meet the expected quality standards, improvement directions are proposed, and improvement decisions are made based on cost and economic benefits, thereby ensuring final product quality. This study concludes with a real-world case study to illustrate the application of the proposed model, making it easier for relevant industries to apply the model.

Six Sigma indices are widely used in the manufacturing industry for quality assessment, analysis, and improvement. The Six Sigma quality index and the Six Sigma Taguchi index can simultaneously reflect quality levels as well as process capability. However, although process yield is a key factor of process quality, neither the Six Sigma quality nor Taguchi indices have a one-to-one mathematical relationship with process yield. Therefore, process yield must be assessed separately. This paper thus proposes a Six Sigma process yield index, which not only reflects the process quality level and degree of process capability but also has a one-to-one mathematical relationship with process yield. In addition to discussing characteristics of the proposed index, we make statistical inferences and provide an evaluation model for industrial use.

The Six Sigma quality index simultaneously reflects process yield, process capability, and Six Sigma quality standards. It is an effective communication tool between industry professionals and customers. An estimator with small bias and variance can enhance the accuracy of an estimate. Furthermore, many industries often face challenges in decision-making due to timeliness and cost considerations, leading to large confidence intervals from small sample sizes. This results in significant sampling errors and inconsistent evaluation outcomes. To address these issues and improve assessment accuracy, this article proposes a Six Sigma quality index estimator with small bias and variance. Based on this, a confidence-interval-based fuzzy test is also introduced. The proposed model relies on confidence intervals and utilizes a smaller bias estimator to mitigate the chances of misjudgments resulting from sampling errors. Meanwhile, this approach is also beneficial to the advancement of smart manufacturing practices, thereby boosting process quality and product value in the industry.

Surface-mounted technology (SMT process) is a crucial technology in the solder paste printing process of printed circuit boards (PCBs). The SMT process offers high stability, low welding defect rates, and good high-frequency properties. It also reduces electromagnetic and radio frequency interference. Thus, improving SMT process capabilities can increase production efficiency, reduce production costs, and stabilize the working environment of circuits, which in turn enables good technical support for subsequent processes. According to some studies, PCBs with severe solder paste misalignment are more likely to malfunction within their warranty period, thereby resulting in losses. The number of product failures generally follows a Poisson distribution; the current paper exploited this characteristic to propose a simple and easy-to-use product failure evaluation index. The proposed index is a function of the process capability index and has a one-to-one mathematical relationship with the product non-conformance rate. First, we derive the confidence interval of process capability index S_PK . Based on this, the confidence interval of the proposed index is derived. We then develop a fuzzy failure evaluation model for PCBs based on this confidence interval. The proposed approach assists the electronics industry in monitoring the impact of process capabilities on solder paste misalignment in PCBs. It further evaluates whether product failure rates are controlled within tolerable levels.

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.

Process capability indices (PCIs) are commonly applied assessment tools which enable the evaluation of process quality during production processes and also allow internal engineers to conveniently and effectively communicate with each other. Many studies have indicated that improving process capabilities not only increases product value but also reduces rates of scrap and rework and betters product availability. Furthermore, enhancing product quality also lengthens product lifespan and delays recovery. Clearly, quality is a crucial factor of corporate sustainability. The quality characteristics of many machine products have asymmetric tolerances, so PCIs with asymmetric tolerances are needed to evaluate these quality characteristics. Many researchers have stressed that sample sizes are not usually large due to cost and technical considerations as well as corporate demands for swift responses. Also, small sample sizes are associated with an increased risk of misjudgement. To address this, we developed a fuzzy evaluation method based on confidence intervals for PCIs with asymmetric tolerances. This approach incorporated expert experience and accumulated data to boost evaluation accuracy and diminish the likelihood of misjudgement resulting from sampling errors.

Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer’s process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap rate. As a result, maintenance costs will go up, economic value will decrease, and even carbon emissions can increase during the production process. In addition, the process capability index and the radar chart are widely used in engineering management and other fields. Since process indicators often contain unknown parameters, sample data are needed for evaluation. With the rapid development of the Internet of Things and big data analysis, many companies regard rapid response as a basic requirement for timeliness and cost consideration. Therefore, companies often have to evaluate the process quality of ten small samples and decide whether to make some improvements. In order to solve the above problems, this study proposed a fuzzy radar chart evaluation model for the process quality of multi-quality characteristic parts based on the process capability index. Using this model can help all parts manufacturers continue to improve the quality of their machined parts as well as reduce their rework and scrap rates. Meanwhile, carbon emissions can be lessened during the production process, and companies can fulfill their social responsibilities. This fuzzy radar chart evaluation model is based on confidence intervals. As the company’s past experience is incorporated, the evaluation accuracy can be maintained even with a smaller sample size. Furthermore, the fuzzy radar evaluation chart can simultaneously evaluate the process capabilities of all quality characteristics of the part. In addition to making it easier for manufacturers to master all quality characteristics, quality process capability can also help them seize improvement opportunities.

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors.

Axial active magnetic bearings (AMB) generally adopt solid structure, and solid structure will lead to obvious eddy current effect. Eddy current effect will make AMB system show fractional-order characteristics, resulting in lower efficiency. This paper proposes an eddy current effect suppression strategy based on active disturbance rejection control (ADRC). ADRC applies extended state observer (ESO) to estimate and offset the disturbance with a compensating current. However, ADRC only enhances the robustness of AMB system but does not improve its efficiency. To improve the efficiency of AMB system, ADRC can only compensate for disturbance caused by eddy current effect. A fractional-order mathematical model of solid AMB was established considering eddy current effect, and ESO was designed. By estimating the actual disturbance of AMB, disturbance caused by eddy current effect was obtained according to the fractional-order mathematical model, and disturbance caused by eddy current effect was compensated to improve the efficiency of AMB system. Simulation results prove the accuracy of estimation and the effectiveness of compensation.

A variety of process capability indices are applied to the quantitative measurement of the potential and performance of processes in manufacturing. As it is easy to understand the formulae of these indices, this method is easy to apply. Furthermore, a process capability index is frequently utilized by a manufacturer to gauge the quality of a process. This index can be utilized by not only an internal process engineer to assess the quality of the process but also as a communication tool for an external sales department. When the manufacturing process deviates from the target value T, the process capability index CPMK can be quickly detected, which is conducive to the promotion of smart manufacturing. Therefore, this study applied the index CPMK as an evaluation tool for process quality. As noted by some studies, process capability indices have unknown parameters and therefore must be estimated from sample data. Additionally, numerous studies have addressed that it is essential for companies to establish a rapid response mechanism, as they wish to make decisions quickly when using a small sample size. Considering the small sample size, this study proposed a 100 (1 − α)% confidence interval for the process capability index CPMK based on suggestions from previous studies. Subsequently, this study built a fuzzy testing model on the 100 (1 − α)% confidence interval for the process capability index CPMK. This fuzzy testing model can help enterprises make decisions rapidly with a small sample size, meeting their expectation of having a rapid response mechanism.

While the Taguchi capability index developed by Chan (J Qual Technol 20(3):162–175, 1988) takes the process targeting issue into consideration, it fails to account for processes with asymmetric tolerances, which are common in practice. Thus, Chen (Int J Reliab Qual Saf Eng 6(4):383–398) modified this index to include processes with asymmetric tolerances. This index is an important tool for the assessment of quality characteristics with asymmetric tolerances, which are common in practice. As the probability density function of the index is complex, statistical inference can be fairly difficult for quality or process engineers. Furthermore, sample sizes are often small in practice to increase decision-making efficiency, but this can decrease assessment accuracy. To address this issue, we employed a mathematical programming approach to make it more convenient for quality or process engineers to derive the upper confidence limit of the index. We also adopted the suggestion put forward by previous studies to incorporate historical data or expert experience in confidence-interval-based fuzzy testing. The proposed approach therefore has increased assessment accuracy, is convenient to apply in practice, and meets the need for swift responses.

Global warming has led to the continuous deterioration of the living environment, in which air quality directly affects human health. In addition, the severity of the COVID-19 pandemic has further increased the attention to indoor air quality. Indoor clean air quality is not only related to human health but also related to the quality of the manufacturing environment of clean rooms for numerous high-tech processes, such as semiconductors and packaging. This paper proposes a comprehensive model for evaluating, analyzing, and improving the operational performance of air cleaning equipment. Firstly, three operational performance evaluation indexes, such as the number of dust particles, the number of colonies, and microorganisms, were established. Secondly, the 100(1 – α)% upper confidence limits of these three operational performance evaluation indexes were deduced to construct a fuzzy testing model. Meanwhile, the accumulated value of ϕ was used to derive the evaluation decision-making value. The proposed model can help companies identify the key quality characteristics that need to be improved. Furthermore, the competitiveness of cooperative enterprises towards smart manufacturing can be strengthened, so that enterprises can not only fulfill their social responsibilities while developing the economy but also take into account the sustainable development of enterprises and the environment.