A key issue faced within the manufacturing industry is determining how to measure quality characteristics and prioritise improvements to be made to all substandard quality characteristics of a product with respect to resource requirements and performance improvement potential. This study proposes a QCAC–Entropy–TOPSIS approach in order to address this issue. It combines the Quality Characteristic Analysis Chart (QCAC), entropy method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed method is not only helpful to measure and determine whether the quality characteristics meet 6σ, 5σ, 4σ or 3σ but also to rank improvements in all substandard quality characteristics of a product in light of resource requirements and potential for performance improvements simultaneously, making it suitable to all manufacturing industries. Moreover, it also can be a powerful tool for analysing the problems that lead to substandard quality characteristics due to poor accuracy and/or precision. Firstly, using the QCAC, the substandard quality characteristics of the product can be determined and the corresponding values of the Discrimination Distance (DD) can then be computed. Subsequently, all the substandard quality characteristics can be regarded as alternatives when conducting entropy and TOPSIS analyses. Secondly, the weights of the evaluation criteria can be calculated by using the entropy method. Lastly, the weights of the evaluation criteria and the values of DD can be substituted into the TOPSIS method. The manufacturer can then categorically prioritise improvement options for all substandard quality characteristics with respect to resource requirements, and consider potential for performance improvements simultaneously. An example is provided for a bicycle quick release manufacturer to illustrate in detail the calculation process of the developed approach. Finally, the advantages of the proposed method are also given through comparisons with Process Capability Analysis Chart and TOPSIS methods.

The convergence of robotic process automation (RPA) and generative AI (GAI) within the context of Internet of Everything (IoE) services represents a profound paradigm shift. This fusion of technologies not only streamlines routine tasks but also catalyzes innovation while harnessing the potential of interconnected devices. Such integration empowers organizations to achieve remarkable gains in efficiency and sustainability. This paper embarks on an exploration of these transformative services, designed to elevate productivity, and curtail wasteful practices in contemporary industries. By closely examining intricate case studies, we illuminate the multifaceted advantages of this integrated approach. Our investigation demonstrates how RPA accelerates the execution of repetitive processes, substantially diminishing the margin for human error and amplifying operational efficiency. In contrast, generative AI introduces a disruptive force, generating fresh ideas, designs, and solutions, thereby elevating the quality of products and services. The infusion of these cutting-edge technologies into the fabric of IoE services paves the way for organizations to attain unprecedented levels of automation, intelligence, and connectivity. Furthermore, this paper comprehensively addresses the intricate challenges and considerations associated with the proposed implementation. We delve into ethical concerns, security implications, and the necessary workforce adaptation to offer a balanced perspective on the adoption of these technologies. Additionally, we navigate through potential limitations and constraints, underscoring the imperative need for strategic planning and robust governance.

When all of the one-sided specification indices of each quality characteristic reach the requirements of the process quality level, they can ensure that the process capability of the product meets the requirements of the process quality level. This study constructs a fuzzy membership function based on the upper confidence limit of the index, derives the fuzzy critical value, and then labels the fuzzy critical value on the axis of the visualized radar chart as well as connects adjacent critical points to shape a regular polygonal critical region. Next, this study calculates the observed value of the index to estimate and mark it on the axis for forming a visualized fuzzy radar evaluation chart. Obviously, this fuzzy evaluation model not only reduces the testing cost but also makes the quality level quickly meet the requirements of the specifications. Further, the radar chart can reduce the risk of misjudgment attributable to sampling errors and help improve the accuracy of evaluation by a confidence-upper-limit-based fuzzy evaluation model. Therefore, this easy-to-use visualized fuzzy radar evaluation chart is used as an evaluation interface, which has good and convenient management performance to identify and improve critical-to-quality quickly. Improving the quality of the process before the product is completed will also have the advantage of reducing social losses and environmental damage costs.

To solve the problems of overconsumption and the increasing environmental pollution brought by rapid industrial development, governments around the world have been introducing a series of green manufacturing (GM) policies to reduce the negative effects of manufacturing. A high-quality product means less time wasted on interruptions and restarts in the production process as well as less waste and defective products. However, once the quality of the product reaches a certain level (e.g., five Sigma), it becomes harder to attain greater economic and environmental benefits. Clearly, both excessive and inadequate quality exert a profound impact on corporations, society, and the environment. This study presents an improved Q_pm multiple quality characteristic analysis chart (Q_pm MQCAC) to ensure that product quality meets a specified standard with minimum input. Quality optimization is used to improve quality characteristics which do not meet the standard by joint monitoring of possible shifts in the process mean µ and/or process standard deviation σ. A guide is also provided to ensure successful quality improvement. The proposed approach not only identifies the factors that influence production quality, but avoids excessive consumption of resources, thereby achieving GM goals. The proposed approach is demonstrated in a step-by-step manner using the example of a steering knuckle pin. Future works are also discussed.

Rapid advances in technology have shortened the upgrade and replacement cycles in industries such as electronics, household appliances, and communication technologies. Within these industries, high-voltage power film capacitors have become indispensable electrical components due to their good electrical performance and high reliability. The selection and evaluation of suppliers of these capacitors is therefore increasingly important. Suppliers play a crucial role in the electronics industry; the quality of their products determines the degree to which the quality of the final product can be guaranteed. Supplier quality also affects the ability of all the members in a supply chain to control costs. Evaluation by decision-makers is highly significant in the supplier selection process. However, when the opinions of multiple decision-makers are combined, issues such as cognitive differences, fuzzy linguistics, and uncertainty are common. This study presents a supplier performance index S_PL and derives the estimates of the index S_PL and its statistical properties. The proposed index is not only helpful for the accurate measurement of supplier performance; it can also reduce cognitive differences among evaluators in the decision-making process (that is, the sample variability associated with the Likert scale). Evaluation scores for each criterion for the linguistic labels are converted to triangular fuzzy numbers in order to reduce ambiguity. Subsequently, integrated crisp values are obtained by defuzzification in a fuzzy inference system. A real-world case study of the supplier selection of high-voltage power film capacitors is provided to illustrate the efficacy of the proposed method.

The electronics industry in Taiwan has achieved a complete information and communication technology chain with a firm position in the global electronics industry. The integrated-circuit (IC) packaging industry chain adopts a professional division of labor model, and each process (including wafer dicing, die bonding, wire bonding, molding, and other subsequent processes) must have enhanced process capabilities to ensure the quality of the final product. Increasing quality can also lower the chances of waste and rework, lengthen product lifespan, and reduce maintenance, which means fewer resources invested, less pollution and damage to the environment, and smaller social losses. This contributes to the creation of a green process. This paper developed a complete quality evaluation model for the IC packaging molding process from the perspective of a green economy. The Six Sigma quality index (SSQI), which can fully reflect process yield and quality levels, is selected as a primary evaluation tool in this study. Since this index contains unknown parameters, a confidence interval based fuzzy evaluation model is proposed to increase estimation accuracy and overcome the issue of uncertainties in measurement data. Finally, a numerical example is given to illustrate the applicability and effectiveness of the proposed method.