Abstract Service quality is a critical issue in the healthcare sector due to its vital importance and significant impact on people’s lives. Therefore, organizations should continuously improve their service quality to ensure providing an optimal healthcare service as possible. In this study, healthcare service quality assessment methodology was proposed to investigate and assess the level of patient’s satisfaction through analysis and measure the gap between the perception service quality and the expectation services quality from patients point view. The proposed methodology integrates Percent Point Score method and Stepwise Weight Assessment Ratio Analysis Method SWARA Method and based six SERVQUAL dimensions namely; Tangible, Responsiveness, Reliability, Assurance, Empathy and, Administrative Procedure with 42 service quality criteria. Questionnaire was developed to capture the perception and the expectation service quality from patients point view was assessed. The perception and the expectation service quality have assessed by Percent Point Score method. SWARA Method was used to identified the weight of significant of each criterion. The proposed methodology was verified in real life at a healthcare center located in Baghdad and opinion of 100 patients have obtained for two months from 1/10/2024 to 1/12/2024. Results showed that waiting time for receiving the medical service (D22) and the booking system (D53) are the weakest service quality criteria that led to patients unsatisfaction, while protecting patients’ privacy, criteria (D46) achieved a high level of patient satisfaction (-0.23). The proposed methodology can help the medical center to investigate the level of quality of their various services to focus their resources in the improvement process to improve the weak services to achieve a high level of patients’ satisfaction. Various dimensions of quality service can be addressed for future works; in addition, a road map of improvement for the weak dimensions can be proposed to increase patient satisfaction.

Abstract The aim of the work was to assess the possibilities of using the QFD method and selected Lean Manufacturing tools in the process of designing and optimizing the production of concrete raised garden beds. The theoretical part discussed the basics of the QFD methodology and selected Lean tools. The research was conducted in a small enterprise producing concrete products for construction. In the first part of the analysis, the QFD method was used as a tool to identify customer needs regarding concrete raised garden beds. The information collected as a “house of quality” served as a starting point for further research using selected Lean Manufacturing tools such as 5S Practices, the SMED method, the Kaizen philosophy and FMEA method. The results indicated that combining the QFD method with Lean Manufacturing tools can increase the quality of the offered product, better suited to the needs of end users and improve its production process in terms of quality, efficiency and costs.

Abstract The aim of this study is to identify and evaluate the specific characteristics, preferences, and needs of different generations in the workplace to support management that is more effective, motivation, and employee retention. The research, conducted in 2022 and 2023, utilized a survey among 488 respondents aged 18 and older to enable relevant generational comparisons. Factors examined for motivation to switch employers included higher financial compensation, more interesting job content, work environment, career growth, the option to work from home, benefits package, development and training, and changes in leadership. Statistical analysis, including t-tests and correlation matrices, revealed significant differences and relationships between motivational factors across generations (Baby Boomers, X, Y, and Z). Results show that across generations, key motivators are career growth associated with interesting job content, development opportunities, and a higher benefits package. Baby Boomers emphasize social interactions within the workplace and opportunities for continuous education. Generation X prefers a combination of stimulating work with flexibility for working from home and more extensive company benefits. For Generation Y, significant factors include career growth linked to financial compensation and work-from-home flexibility. Generation Z prioritizes higher financial compensation for career growth and engaging job content with opportunities for development and training. This study brings a key new insight by revealing how the combination of flexibility (such as remote work options), employee benefits, and opportunities for professional development significantly contributes to job satisfaction across different generations. These findings provide practical guidance for designing motivational strategies tailored to the specific needs of each generation in the workplace.

Abstract The continuity of the flow of materials needed for correct operation of manufacturing systems can be achieved using different means and control methods. These objectives can be achieved through the use of Total Productive Maintenance (TPM). Specific effects can also be achieved by using additional capacitive elements in device systems (manufacturing lines). In this paper to be considered is a system with a serial structure and an additional capacitive element (the position within the system and the capacity of the element are determined). The capacitive element divides the system into two subsystems: the part delivering the material to the buffer (DP) and the part receiving the material (RP). The time lost due to unplanned interruptions in the operation of the production system equipment is described by the MTTR (Mean Time to Repair) indicator. The analysis of the system’s operation used the failure index, which is directly related to the MTTR index. To study the system, a method involving the analysis of states and a digital simulation are used. To assess the system with an additional capacitive, a production performance indicator is used. The obtained results allow for conclusions on the possibilities for improving the effectiveness of manufacturing systems using the proposed method.

Abstract Planning a production process in terms of correctly defining its efficiency, duration and the level of generated costs is a difficult task. Manufacturing processes are characterized by a high level of complexity and are exposed to a large number of external factors that are difficult to predict. In many cases, one of the tools supporting decisions in the analysis of manufacturing process parameters is the use of computer simulation both at the process planning stage and in real time during its implementation. In this article, a computer simulation tool (FlexSim simulation software) was used to analyze the parameters of a selected production process. The conducted analyses allowed for indicating the direction of actions in the use of computer simulation as a decision support tool at selected stages of production process planning in the area of evaluating solutions aimed at implementing improvements.

Abstract This study explored the role of Circular Economy (CE) strategies in small and medium-sized enterprises (SMEs), with a particular focus on integrating Artificial Intelligence (AI) to optimize CE performance. The research aimed to identify the key determinants influencing CE indicators by using Principal Component Analysis (PCA) and regression modeling. The findings revealed that factors such as employment in CE sectors, resource productivity, and effective waste management practices significantly impact circularity outcomes. These factors were found to be crucial for SMEs striving to enhance sustainability and reduce environmental impact through circular economy practices. The study primarily focused on general Circular Economy strategies, meaning the results may vary across different industries, particularly those with varying waste streams and resource challenges. For instance, certain sectors might face specific hurdles in waste management or resource efficiency, making the application of CE strategies more complex. Additionally, the study uncovered the complexity of systemic interactions within CE implementation, such as the negative correlation between municipal recycling rates and circular material use, which requires further exploration. These findings suggest that understanding the broader systemic factors affecting CE is essential to fully realizing its potential. Moreover, the integration of AI in CE strategies emerged as a promising avenue for optimizing resource management, improving waste reduction, and enhancing productivity. AI can play a critical role in identifying inefficiencies, predicting trends, and streamlining operations in SMEs. This study contributes to the growing body of knowledge on CE in SMEs, emphasizing the importance of AI in advancing sustainability and efficiency in circular practices. Further research is needed to explore industry-specific challenges and systemic interactions in greater detail.

Abstract The three primary branches of ergonomics/human factors (E/HF), concentrate on the physical, cognitive, and organizational dimensions, have developed over time. Generally, E/HF is the scientific discipline concerned with the design and arrangement of work environments, systems, and products to fit the physical, cognitive, and emotional needs of the people who use them. The goal of E/HF is to optimize human well-being and overall system performance by improving comfort, safety, efficiency, and productivity while reducing the risk of injury and strain. E/HF is increasingly relevant in the context of Industry 4.0 characterized by automation, cyber-physical systems, and interconnected technologies. The integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, and augmented reality (AR) within manufacturing environments presents both opportunities and challenges for worker well-being. Development of industrial technologies requires also a rethinking of traditional E/HF principles to address new human-machine interactions, cognitive demands, and the physical layout of workplaces. E/HF benefits and the threats of Industry 4.0/5.0 technologies must be considered in an integrated manner. The importance of designing systems that promote user-friendly interfaces, reduce mental and physical strain, and support sustainable work environments now becomes critical. In this context, this review paper explores the connection between E/HF and Industry 4.0/5.0, emphasizing the need for a holistic approach to designing work systems that enhance both human performance and technological innovation.

Abstract Development is an integral part of research, since the study of specifics and characteristics in such a unique topic as the quality of processed products cannot be done without extraordinary equipment, fixtures and a laboratory. Laboratory equipment has been created that combines all hardware aspects of the development of the quality of processed products, where it analyzes the quality of workpieces that depend on the processing technology. However, this depends largely on the technological system used. The paper includes measurements, data acquisition and back-checking of the logic of the study of grinding operations. In the introduction, it must be shown that the correct selection and strength characteristics of the tool associated with the cutting mode of grinding determine the accuracy and quality of the surfaces of the ground part. The impact on the service life of the grinding tool was also monitored. The 4th partial result is also the knowledge that the correct selection of the tool characteristics and cutting mode predicts the quality of the workpiece surface. The operating conditions of the manufactured parts significantly affect the operating values of the production parameters. By appropriate diagnostics in the research process and precise measurements using sensitive sensors, deviations can be minimized. Without this apparatus, it is possible that deviations increase significantly during the operation of the technological system. The size of the deviation during grinding is explained in practice by the presence of vibrations in technological processes. They cause instability of the parameters of the technological system. To stabilize the quality indicators, a mathematical apparatus was also used, which takes into account changes in the technological regime. They are determined by designing adverse conditions, e.g. restoration of the cutting properties of a worn grinding wheel. Therefore, its replacement is logically carried out earlier than its replacement is planned.

Abstract In contemporary enterprises, customer satisfaction analysis has become a critical area of concentration. Being able to understand and predict customer satisfaction is becoming more and more important as companies try to develop and launch new products. Leveraging customer data intelligently and employing robust data analytics techniques are essential for meeting this imperative. With this objective in mind, the study proposes a machine learning-based approach to analyze and discern the variables influencing customer satisfaction. Specifically, the study utilizes agglomerative clustering for data segmentation and feature identification, followed by a Random Forest Classifier as machine learning (ML) model for prediction. Performance metrics such as accuracy, recall, precision and F1-score are employed for model evaluation, ensuring robustness and reliability in the predictive process. Furthermore, it aims to predict the impact of enhancing specific product attributes on customer satisfaction. To provide a tangible demonstration of the proposed methodology, a comprehensive case study is conducted. By systematically integrating clustering techniques into the feature selection and modeling process, this framework furnishes a structured methodology for data-driven decision-making and predictive analytics. This holistic approach not only enriches the comprehension of intricate datasets but also facilitates the development of resilient predictive models characterized by enhanced accuracy and interpretability. By segmenting customers based on their responses, we discerned specific areas of satisfaction and dissatisfaction, providing actionable insights for targeted strategies aimed at improving overall satisfaction. The insights and customer clustering derived from this study can guide these targeted strategies to enhance customer satisfaction and inform future product development initiatives.

Abstract This study investigates the feasibility of integrating Hyperloop technology with hydrogen fuel systems. The primary objective was to evaluate how Hyperloop infrastructure can incorporate hydrogen refueling stations to optimize transport efficiency and sustainability. The research utilized simulation models developed with ArchiCAD and FlexSim software to analyze the integration of Hyperloop cargo and passenger transport with hydrogen refueling processes. The study included detailed simulations of Hyperloop operations and refueling logistics, focusing on safety, efficiency, and the potential for reducing carbon emissions. Key parameters such as capsule speed, refueling times, and infrastructure requirements were analyzed. Results indicated that Hyperloop technology can significantly enhance transport efficiency, particularly for high-value and time-sensitive goods, while hydrogen refueling stations offer a sustainable energy solution with considerable potential for reducing environmental impact. The findings demonstrate that integrating hydrogen refueling stations into the Hyperloop system can improve operational efficiency and sustainability. However, challenges remain in optimizing refueling processes and infrastructure development. Future research should address these challenges and explore additional AI-based optimization techniques to enhance the overall system performance. This research is especially useful for urban planners, transportation engineers, and policymakers aiming to develop efficient, sustainable transportation systems that incorporate cutting-edge technology like Hyperloop and hydrogen refueling infrastructure.