Journal of Quality Engineering and Management

Abstract This paper aims to develop a comprehensive model for the synergy of quality, sustainability, and agility in drug production systems. This model seeks to use data collected by automated inspection systems to improve product quality, plan preventive maintenance, and optimize production planning. Reviewing the literature on quality management, sustainability, and agility in production, an integrated model of quality, maintenance, and production (IQMP) was designed and developed using Bayesian approaches. The results show that the model can effectively improve product quality, and increase production stability and system agility against environmental changes and fluctuations. Using online inspection data in this model significantly increases its accuracy and efficiency in decisions related to quality, maintenance, and production planning. In addition to helping to improve the efficiency of production systems, this model can be used as a strategic tool for production and maintenance managers. By implementing this model in real conditions, companies can take advantage of the data collected by automatic inspection systems and make more detailed plans for maintenance and quality control.

Abstract In many control charts, the assumption of normality is a fundamental premise. However, in real-world applications, this assumption is often violated, leading to reduced efficiency in parametric control charts. When process data follow an unknown or non-normal distribution, parametric methods may yield unreliable results, making nonparametric control charts a more effective alternative. Among these, the sign control chart is a widely used technique for monitoring the location parameter of a process without requiring distributional assumptions. This study focuses on the economic design of the sign control chart in the presence of multiple independent assignable causes. A modified cost model, adapted from Duncan’s economic model, is developed to optimize the chart’s parameters. The analysis balances inspection costs and process performance to determine the most cost-effective monitoring strategy. Results indicate that the proposed model significantly enhances economic efficiency and detection performance under non-normal data conditions, making it a valuable tool for practical applications.

Abstract Digital transformation, large amount of data, and high speed of production processes are among the most important characteristics of the ecosystem of the fourth industrial revolution. These characteristics have influenced the decision-making processes of business managers. In the fourth industrial revolution, all elements of business and production systems, including decision support systems, will be affected by digitization and the high speed and accuracy of new technologies. One of these important pillars is quality management. Digitized quality management is called the Quality 4. Design of experiments as an active approach in quality management plays an important role in improving the quality of products and even processes. This powerful tool has been influenced by the changes that occurred in the fourth industrial revolution and has tended towards the design of intelligent experiments. This research aims to show with a comprehensive review in the literature how the design of experiments can provide a special role in meeting the requirements of customers in the fourth industrial revolution and the fourth quality. In this research, firstly, the changes made in the methods and design plans of the experiments, then the possible changes in the activities of the basic process of the implementation of the design of the experiments have been redefined according to the effects of new technologies. At the end, suggestions for future research have been proposed.

Abstract Nowadays one of most important organizations’ goals is to access compatibility to increase their market share and higher profitability. Therefore, it can be claimed that compatibility is considered as one of the most general organizations’ and industries’ outputs. The purpose of writing the current paper is to represent a model to measure carpet industry compatibility emphasizing quality and innovation. Statistical society in qualitative part includes 14 top managers and 582 employees in quantitative part from which 232 ones were selected as statistical sample. First of all, by the research model was obtained with three main dimensions resources and capabilities, quality and innovation with 30 indices through semi-structured and unstructured interviews with experts and during the coding process. Meanwhile resources and capabilities includes spiritual capital, human capital and technological capabilities, quality dimension includes raw materials, pre-production phase and production phase and finally innovation dimension includes product innovation, process innovation and sale innovation.

Abstract In this paper, firstly, a comprehensive and detailed model is presented to define the technical design requirements of engineering systems to be designed in design offices. From a theoretical point of view, it has been tried that the proposed model includes the general categories of technical requirements presented in international and national standards, as well as important references in the field of system engineering. From a practical point of view, the presentation of this model is based on the practical experience of defining the technical requirements for the design of a variety and a large number of complex electrical, electronic, computer, telecommunication, mechanical, and electromechanical systems. Also, as an example in this paper, the results of the definition of technical requirements for a type of Global Maritime Distress and Safety System, GMDSS, as one of the essential telecommunication subsystems of a type of surface vessel according to the proposed model, are briefly presented. Therefore, the proposed model can be used to define the technical requirements of a wide range of engineering systems in design offices.

Abstract Couplings are widely used in the industry and this equipment are always subject to defects and failures due to continuous rotation. Vibration analysis is a suitable technique for failure analysis and failure detection of rotating equipment. The purpose of this research is to analyze the failures that occurred in a coupling, whose data was collected in normal state and three failure states with four sensors connected to the coupling. For this purpose, two different types of feature extraction have been used, and seven machine learning algorithms and one deep learning algorithm have been used to classify situations. In this research, the performance of each of the implemented algorithms and the importance of extracted features have been investigated, and the role of sensors and their importance to reduce the number of sensors have been investigated. From the results of this research, we can point out the high importance of the features of the frequency domain in the accuracy of the implemented models, as well as the high efficiency of two sensors for classification.