A positive patient safety culture plays a major role in reducing medical errors and increasing productivity among healthcare staff. Furthermore, understanding staff perceptions of patient safety culture and effective patient safety factors is a first step toward enhancing quality of care and patient safety. The objectives of this study were to assess patient safety culture in hospitals in the United States and to investigate the effects of hospital and respondent characteristics on perceived patient safety culture. An analysis of 67,010 respondents in the 2018 Agency for Healthcare Research and Quality (AHRQ) comparative database was conducted with partial least squares structural equation modeling (PLS-SEM). The results revealed that perceptions of patient safety culture had a positive influence on the overall perceptions of patient safety and frequency of event reporting. Moreover, staff position, teaching status, and geographic region were found to have varying influence on the patient safety culture, overall perceptions of patient safety, and frequency of event reporting.

Recently, engineering graduates who acquired different skills, as well as leadership capabilities, are very much demanded to maintain high-level innovation, dealing with emerging technologies, and fulfilling the various needs of the public. Consequently, it is necessary that academicians in engineering programs develop appropriate curricula to achieve excellence in engineering education. Being accredited from the Accreditation Board for Engineering and Technology (ABET) is an indication that an academic program has fulfilled international standards in engineering education. The main element in ABET evaluation process is Student Outcomes (SOs) as they incorporate several capabilities and skills expected by stakeholders such as employers. In this study, we used Interpretive Structural Modeling technique (ISM), which is a soft operational research technique, to prioritize SOs and develop a precise study plan for the Industrial Engineering program at Department of Industrial and Management Systems Engineering (IMSE) at Kuwait University (KU). This plan showed an intelligible depiction of the relationships between the SOs and a clear direction on how to merge them within the engineering study plan. For data collection, surveys were developed and conducted to faculty members at IMSE Department at KU in addition to a set of employers within the country. The results indicated that ISM model facilitates a clear understanding of the relationships among SOs within the Industrial Engineering study plan at IMSE at KU. In addition, it recommended that students should understand the professional and ethical responsibilities at early stages of the study plan and that should be followed by training them on effective communication techniques.

Incorporating Artificial Intelligence (AI) into the Kano model presents significant possibilities for improving quality management, increasing customer satisfaction evaluations, and refining product or service development plans. This systematic literature review explores the current research on the integration of AI with the Kano model, highlighting significant trends, advantages, and obstacles. The data analytics capabilities of AI allow for the automation and enhancement of Kano's feature classification, hence enabling more accurate identification of customer preferences and dynamic feedback mechanisms. This study classifies the reviewed publications into two groups: those leveraging textual data (e.g., customer evaluations) and those adopting alternative analytical tools to augment the Kano model. Research demonstrates that AI-driven methodologies enhance the efficiency and precision of Kano’s framework, providing real-time insights and predictive functionalities. However, problems including quality of data, questionnaire biases, and integration issues are acknowledged, underscoring the necessity for additional study to mitigate these limitations and fully realize the potential of this integrated method.

Industry 4.0 technologies are transforming manufacturing worldwide. Many companies struggle to successfully implement these technologies. To drive successful implementation, it is useful to understand Industry 4.0 technology applications and successful strategies. Industry 4.0 technologies utilize data and successful applications must involve data collection and management, data analytics, and automation. The value proposition of Industry 4.0 is in turning data into usable information in such a way as to improve business results. Review of case studies shows that quality applications are common cases of success for Industry 4.0 technologies. The data driven nature of Industry 4.0 lends itself to the skills of quality professionals. Taking a quality driven approach to implementation may be a successful strategy for those businesses with quality as a strategic focus for competitive success. Quality professionals already in possession of data skills and continuous improvement mindsets are well suited to drive digital transformation. A model for a Quality 4.0 implementation strategy is developed through thematic analysis of the literature on Industry 4.0 and case study analysis of successful Industry 4.0 implementations.

Industry 4.0 technologies are transforming manufacturing worldwide. Many companies struggle to successfully implement these technologies. To drive successful implementation, it is useful to understand Industry 4.0 technology applications and successful strategies. Industry 4.0 technologies utilize data and successful applications must involve data collection and management, data analytics, and automation. The value proposition of Industry 4.0 is in turning data into usable information in such a way as to improve business results. Review of case studies shows that quality applications are common cases of success for Industry 4.0 technologies. The data driven nature of Industry 4.0 lends itself to the skills of quality professionals. Taking a quality driven approach to implementation may be a successful strategy for those businesses with quality as a strategic focus for competitive success. Quality professionals already in possession of data skills and continuous improvement mindsets are well suited to drive digital transformation. A model for a Quality 4.0 implementation strategy is developed through thematic analysis of the literature on Industry 4.0 and case study analysis of successful Industry 4.0 implementations.

This presentation is aimed at reviewing the current approaches, practices and methods in cost engineering. Given the complexity of any product design endeavor it is paramount to have accurate cost data to fully understand the costs involved for the creation of a cost estimate prior to the start of the design phase. The three principal areas reviewed cost engineering methods, cost engineering practitioners and cost or product cost management systems. The literature is rich with studies and reviews on cost engineering methods such as activity-based costing, standard costing, value costing as well as many others, so this presentation will discuss the current state of each these methods. What is more difficult to find in literature is the role of cost engineering practitioners in design project methodologies that may be associated with DFSS or other DfX practices. The third approach to be review is the newest player in cost engineering-product or cost management systems (PCM or CMS). These systems can provide immediate access to costs relating to the sourcing of parts and materials, labor rates for different skills and locations, manufacturing costs for different processes as well as other costs needed for decision making. This paper is the result of an extensive literature review of peer reviewed journal articles that have been published in journals related to how cost data is developed, maintained, and then used in projects that create new products and the redesign of existing products.