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 : Mineral industries are one of the important sectors of industry in Iran, therefore, it is necessary to improve the quality of mineral products. One of these products is foundry silica sand. The aim of this study was to create a complete model using this type of silica sand. A comprehensive analysis was done on ten mines and seven mines were selected to perform the quality improvement stage. A total of 1400 tests were conducted to achieve the main goal of the research, which was to increase the quality of silica sand parameters. It was also found that the seven basic characteristics of silica sand have a significant effect on the quality of the final products. The quality of silica sands is influenced by elements such as calcium, sodium, potassium and magnesium, which are alkaline elements of the soil. A higher percentage of silica in a mineral is usually associated with increased quality, as it ensures the achievement of ideal properties and performance in silica sands. Factors affecting the quality of silica sand were prioritized by experts using the fuzzy Delphi technique and hierarchical analysis. These factors have an effect on the chemical composition, purity, reactivity and performance of silica sands. Also, a data mining model was designed to predict the quality of these sands. The findings of this study show that the presence of calcium, sodium, potassium, magnesium, silica content, ADV (sand alkalinity or acidity) and pH affect the quality of silica sands. It is concluded that this model provides an efficient attitude and prediction to increase product quality.

Abstract Extensive research has been done in the field of statistical process control. Also recently, control charts based on the Bayesian predictive distribution idea have been proposed in statistical process control texts. This idea was first proposed by Menzefricke and its parameters were considered unknown. In this paper, for the first time, statistical-economic design of control charts for monitoring the process mean with known standard deviation based on Bayesian predictive distribution is presented. According to the results presented in the article, regarding the superiority of statistical-economic design over economic design and also the superiority of Bayesian approach over the classical approach in designing control charts, it is suggested to use Bayesian control chart with statistical-economic design in controlling the process mean. 

Abstract  Among the factors responsible and involved in projects are quality control consulting services and resistance of building materials companies which are working to have an effective performance through interior fit with the structure of the projects as well as exterior fit with the environment. They are also working to complete the projects in the shortest possible time and cost and with the right and certain quality. This paper aims to show the position and significance of quality management in construction quality control companies and to prioritize criteria for successful quality management. In this regard, through literature review and interview with experts, a questionnaire with 33 variables and 7 indices was designed to rank quality management measures. Interacting with the managers and senior technical staff in consulting engineer companies finally 15 sub-indices were determined. The research community is composed of 22 companies associated with quality control process and resistance of building materials in Rasht. The sub-indices have been asked through paired comparison questionnaires. Findings were analyzed through fuzzy analytical hierarchy process (AHP) software and the criteria were prioritized. Then, the impact of the criteria on quality management of construction quality control companies was found. Training index and operational management process, among the indices, were identified as the most significant factors in quality management of construction consulting engineers companies.

Abstract The main purpose of this study is to identify the causes of vibration and detectable defects of bearings through sonometry using a multilayer neural network. Neural network is an intelligent method and due to its main properties, ie its high ability to estimate nonlinear functions and adaptive learning, it has been used to troubleshoot mechanical vibrations of machines, ie bearing acoustics and their frequency analysis. To collect the data, a type of healthy ball bearing cone bearing and a similar bearing with defective bullets were used and tested in desktop drills and radial-based drills in 5 different rounds. In this study, according to the network with 10 hidden layers, the signal frequency is considered as the input of the multilayer neural network and finally the bearing defects and its probable cause are determined and corrective measures are proposed.

Abstract Successful organizations continuously evaluate the quality of the company's products and services with other competitors, but self-assessment is more important from a quality perspective. This research, by using the "performance measurement based on network auxiliary variables" model, helps managers to self-assess the quality of products. The proposed model in this research is examined in the form of a case study (Moghan Wire and Cable Company) and the quality performance of the company's products over time is analyzed. The results of this research indicate the effectiveness of the proposed model.

Abstract The importance of software is increasing day by day, and with this growing importance, continuous efforts are being made to develop technologies that lead to the creation of high-quality software. Software metrics are essential tools for project and quality management. In addition to selecting appropriate metrics for monitoring, detecting meaningful behaviors and changes or deviations in the process, analyzing them, and determining whether process shifts are statistically significant are also crucial.
Since statistical quality control is one of the well-established approaches for addressing these issues, this paper aims, for the first time, to identify and categorize all techniques used in the existing literature related to Statistical Process Control (SPC) in software processes. This classification helps researchers recognize practical topics and research directions and supports them in conducting useful and statistically sound studies based on the compiled material.