Bandar Alzahrani , Haitham Bahaitham , Ahmad Elshennawy
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引用次数: 0
Abstract
Quality 4.0, a modern approach to quality management, applies Industry 4.0 principles to enhance product and service quality. By utilizing data-driven techniques like predictive analytics, machine learning, and artificial intelligence, Quality 4.0 aims to improve traditional quality management systems. Higher education institutions (HEIs) can benefit from Quality 4.0 by employing data-driven decision-making, automation, and analytics to identify process and service improvement areas. This study introduces a framework to evaluate the maturity level of HEIs in their transition to Quality 4.0, focusing on the process, people, and technology dimensions outlined in the LNS Research Quality 4.0 model. A survey consisting of 95 practices categorized into eleven Quality 4.0 axes was developed to assess the current level of HEI transformation efforts and pinpoint their strengths and weaknesses. Consequently, the participating HEIs were classified into one of the five maturity levels defined by the Quality 4.0 Maturity Scale (Q4.0-MS). The framework's validity was established by evaluating the maturity of Quality 4.0 implementation in a group of Saudi HEIs. The Friedman test was conducted to statistically confirm the framework's ability to differentiate between the observed maturity levels of Quality 4.0 adoption among the study participants. The results revealed the validity of the developed framework by assessing the maturity level of Quality 4.0 adoption within the participating HEIs. Particularly, the results showed that all 26 Saudi HEIs achieved scores ranging from the second to the fifth levels of the Q4.0-MS, indicating varying levels of Quality 4.0 adoption, from initial stages of building foundations to advanced implementation. This comprehensive understanding of Quality 4.0 implementation in the participating Saudi HEIs can be extended to other higher education institutions globally, as the framework's validity has been confirmed.
期刊介绍:
The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers.
The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.