Journal of Quality Engineering and Management

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 purpose of the current research is to design a structure so that it can be used to investigate the drivers of the appropriate implementation of quality 4.0 in the country's steel industry. In order to carry out this research, ten stimuli were initially identified using research literature. Then, using the interpretative structural modeling technique, these stimuli were structured using the opinions of 13 experts. , in order to fit the obtained structure, the structural equation modeling of the tools related to it, including measurement model fitting, structural and general model fitting, were used. For this purpose, a questionnaire containing 33 questions with a five-point Likert scale was designed and in order to complete it, opinions were sought from 214 managers and employees of the country's steel industry. The findings of the research on the high effectiveness of reward stimuli and control of big data in proper implementation have quality 4.0.

Abstract As is well known, data obtained from real-world processes are typically autocorrelated, and monitoring such processes requires accounting for this autocorrelation. The control charts developed so far for monitoring the defective proportion (p) are generally based on the assumption that binary observations are independent, which ignores the inherent correlation in the data.
In this paper, a cumulative sum (CUSUM) control chart is proposed that incorporates the autocorrelation between binary observations using a first-order two-state Markov chain model. Furthermore, using the Average Number of Observations to Signal (ANOS) index, it is shown that under 100% inspection conditions, the proposed chart performs better than the Bernoulli CUSUM chart—which assumes independent observations—and, in other words, detects increases in p more quickly.

Abstract Guiding the engineering process in systems has created an interdisciplinary approach called system engineering. The concept of guidance in the first step means choosing the best path among the existing paths and then guiding, directing and managing the process in the selected path. In this context, requirements management is one of the first stages of the product development process, which can engineer the requirements with a comprehensive and accurate view and produce a product that meets the needs of users.
The purpose of preparing this research, which was carried out in the period of 1402-1401, is to implement all requirements management activities in a marine system. For this purpose, at first, the basic requirements related to this product were extracted from different sources, categorized and then the metadata of the requirements was prepared, and one or more confirmation methods were provided for each of them. Finally, the relationship between the requirements has been determined and after resolving the conflicts between them, the metadata of the requirements has been updated. According to the results of this process, 106 requirements have been identified. These requirements will be considered as the basis for starting the product development and design process.

Abstract The purpose of this paper is to investigate the reliability parameter R = P (X <Y) based on samples with type 2 incremental censorship in which X and Y are independent random variables with generalized inverse exponential distribution with different shape parameters and the same scale parameter. The maximum likelihood estimator (MLE) and the nonlinear estimator with uniform uniform variance (UMVUE) of the parameter R are obtained and different confidence intervals are provided. Also, Bayesian R estimator and HPD confidence interval using Gibbs sampling method are proposed. Monte Carlo simulations have been performed to compare the performance of different methods.

Abstract Reliability studies are an essential part of every management program for equipment maintenance. As systems become more complex, maintenance strategies become critical for making sustainable management decisions. Unexpected failures in a system can be the primary reason for the poor performance of industrial machinery and equipment. Various fault resistance mechanisms are utilized to make critical decisions for the system. In the present paper, a two-parameter Weibull distribution approach is considered to evaluate the heat exchanger datasets in the petrochemical industry using Isograph Hazop + v7.0 software. As the effective execution of a system depends on its reliability and planning under suitable conditions, this paper presents a strategy for finding the reliability to calculate the preventive maintenance intervals in actual systems. This approach leads to fewer number of inspections and fewer repair activities, higher safety and reliability of industrial units and also, higher economic benefit.
 

Abstract Purpose: Effective management of organizational processes and facing challenges is crucial for organizations such as the Cooperative Investment Guarantee Fund to achieve their goals in today's competitive world. This issue is doubly important for this fund, given its wide range of stakeholders and its key role in supporting the cooperative sector. Therefore, the present study aimed to present challenges and solutions for improvement in the processes of issuing cooperative development guarantee credit insurance policies.
Methodology: This research is based on the principles of quality management and a process approach to ensure the scientific and practical validity and reliability of the results. In-depth analysis of the challenges and their prioritization was carried out using the Shannon entropy method, TOPSIS technique, and Nominal Group Approach (NGT).
Findings: The research findings showed that most of the fund's problems are concentrated in the process and strategy sections; therefore, in accordance with the extracted priorities, optimization solutions were presented and Key Performance Indicators (KPIs) were developed for continuous monitoring.
Originality/Value: In addition to creating process transparency, the final results of this research, by providing an operational and scientific roadmap, provided a basis for focusing resources on key points of success, which is a pivotal step towards reducing time and cost, improving effectiveness, and achieving strategic goals in the cooperative sector.

Abstract Business excellence initiatives help organizations develop and increase management capabilities in order to achieve high performance and greater competitiveness, and management commitment to implement excellence models and self-assessment based on them is an important initial stage of the organizational excellence process. The present applied research seeks to identify and classify all its components in the implementation of business excellence models by examining the concept of management commitment. In this regard, by reviewing the research literature and conducting semi-structured interviews with experts, 92 components that form the concept of management commitment were identified and counted, and validated using a questionnaire and their importance was assessed. Based on the implementation of exploratory factor analysis, the components of management commitment were categorized into 4 main factors: ownership of organizational excellence, improvement programs, membership in self-assessment teams and creating integration, and creating and developing a culture of excellence. By identifying and categorizing the components of management commitment, this research lays the groundwork for creating a broader understanding and awareness for managers of their roles and responsibilities in implementing excellence models and increasing the effectiveness of the organizational excellence process.

Abstract In today’s competitive world, organizations are constantly confronted with internal and external environmental changes. On the other hand, with the rapid advancement of information technology, effective and efficient use of this technology to improve the quality of leading manufacturing or service industries has become inevitable. Providing electronic services within organizations plays a key role in enhancing service quality, customer satisfaction, and achieving competitive advantage. Employees’ information literacy is a critical factor in enabling organizations to leverage this tool effectively. Given the importance of this issue, the present study examines the impact of information literacy on organizational agility. Technological innovations and diverse information sources alone, without information literacy, do not lead to organizational learning, responsiveness to change, flexibility, or ultimately, high-quality services. Therefore, a new form of organization that views environmental changes as opportunities is necessary. Such organizations require employees who can collect, organize, evaluate, and analyze large volumes of information to advance the organization. Clearly, the absence of these skills leads to organizational failure in the current competitive landscape. Information literacy involves lifelong learning skills. It enables individuals to develop their thinking to identify information needs, search for and optimally utilize information sources and systems, and evaluate work processes, thereby transforming them into information-savvy employees. The aim of this study is to investigate the factors through which employees’ information literacy affects organizational agility. The statistical population of this research includes employees of the Cultural and Artistic Organization of Tehran Municipality. According to the research findings, the impact of various dimensions of information literacy on organizational agility was confirmed through statistical analyses.

Abstract The measurement process is usually accompanied by error. Measurement error creates a discrepancy between the true value and the observed value of a quality characteristic, affecting the performance of control charts and reducing their ability to detect process changes. In some processes, a quality characteristic is expressed as a function, which is referred to as a profile. Over the past decade, profiles and methods for monitoring them have attracted considerable attention from statistical quality control researchers. Although the impact of measurement error on various control charts and their parameters has been investigated, there is limited information regarding its effect on profiles and their monitoring methods. In this study, the effect of measurement error on the response variable of linear profiles in Phase II is examined. Simulation results indicate that measurement error also affects the performance of profile monitoring methods, reducing the power of techniques such as EWMA-R and EWMA-3 in detecting process changes. Furthermore, this study proposes a new approach for selecting an appropriate measuring device based on profile monitoring methods in the presence of measurement error.

Abstract Purpose: Given that determining the aggregate coefficient is a key constraint in Aggregate Production Planning (APP), this study seeks to identify the factors influencing this coefficient and to analyze them using Fuzzy Interpretive Structural Modeling (FISM) to explore their interrelationships.
Methodology: After identifying the key factors influencing the aggregate coefficient, an Interpretive Structural Modeling (ISM) questionnaire was distributed among experts, and the responses were aggregated. Subsequently, the FISM steps were carried out. Finally, an interaction network was constructed, and an analysis was performed to evaluate the degree of dependence and driving power among the identified factors.
Findings: The developed interpretive structural model comprised 11 hierarchical levels. The fuzzy analysis of dependence and driving power indicated that none of the factors were categorized as autonomous, reflecting a strong degree of interconnection among the variables within the model.
Originality/Value: Production planning for multiple products utilizing shared resources is a complex challenge. Thus, analyzing the variables that influence the determination of the aggregate coefficient in production planning provides valuable insights, facilitating informed decision-making, particularly in optimizing resource allocation to achieve an optimal production level.

Abstract In this research, a hybrid approach based on fuzzy logic and artificial neural network is presented to predict the failure of machines in order to increase productivity. The subject of this research is one of the factories of the automobile industry named Diaco Ide Aria, which operates in the field of automobile parts production. Preventive maintenance requires correct prediction of breakdowns and accidents, equipment and machines so that productivity can be increased by timely and correct maintenance of machines as well as fixing defects and breakdowns. To model the multi-layer perceptron fuzzy-neural network (MLP), first, 100 failures and stops were collected in a period of 15 months and then entered into MATLAB software. The obtained results show that the implementation of fuzzy-neural network and the prediction of machine failure time has reduced the duration and cost of repairs. Therefore, the working time and accessibility of the machines increased and ultimately increased the productivity by 57%, also, the accuracy of the developed neural-fuzzy model was estimated at 94%.

Abstract Nowadays, competition in various industries focuses on reducing production waste and improving the quality of manufactured products. In this article, the improvement of surface roughness quality of AISI 4340 workpieces in the turning process using TiN-coated carbide tools is investigated. For this purpose, the parameters affecting the surface quality, namely cutting speed, feed rate, and depth of cut, were selected as the input factors for the experiments. Then, the degrees of freedom of the system and the required number of levels were determined, and the corresponding orthogonal arrays were calculated. Accordingly, twenty-seven experiments were designed to measure the surface roughness values, and for each experiment, three measurements of the surface roughness parameter were recorded. Next, by calculating the grey ratios, grey coefficients, and grey grades using the relevant formulas, the final grey relational graphs were plotted for all three levels of the experiments. Based on the grey relational diagram, the percentage influence of each input parameter on achieving the desired surface roughness was determined, and the optimal numerical values for these parameters were identified. A comparison of the grey analysis results with the actual experimental data confirms the accuracy and capability of this method in predicting surface roughness in the turning process.

Abstract Cost, quality and design time of macro integrated products are important and necessary factors in design management. The aim of the current research is to provide an integration model for technological readiness, requirements statement document and also description of product features in the management of macro system design in domestic air base products. In particular, the research intends to reduce the time required for the final delivery of the product by identifying and prioritizing the real and prospective needs of customers/users, while applying gradual changes and evolutionary measures. The research method is survey-descriptive in terms of execution. In fact, the approach of data survey and analysis is carried out quantitatively using exploratory and confirmatory factor analysis. The results obtained from the significant factor loading coefficient and path coefficients as well as the test results of the research variables show the following. The significant coefficient of six paths - among the visible and hidden variables in addition to the components determined in the research conceptual model - is higher than 1.96 at the 95% reliability level. These numerical results confirm the research hypothesis and validity of the model fit.

Abstract In this research, using Markov model, general formula for calculating reliability and MTTF, which are the main engineering factors in quality in systems with redundancy of 1 of n, from two methods of solving differential equations and shortcut method (direct calculation of MTTF without the need for function Reliability is obtained by using transition matrices in the Markov model, which are sometimes briefly mentioned in the article. One of the remarkable results of this research is that the possibility of estimating parameters for any desired number of n has been facilitated. Also, using the obtained functions, the effect of increasing add-on items on improving reliability in these systems (with ready-to-serve and active modes and repairable and non-repairable components), along with factors such as repair rate and reliability of systems. Switching has been evaluated. Mathematica software has been used to perform calculations and data analysis. 

  

Abstract Process analysis is closely related to process optimization and in the optimization, the discussion of resources plays a major role. Since the optimal set of resources, especially in the event of unexpected events will have variable effects on risk, process performance and efficiency can be similar to the optimal stock portfolio selection model. The purpose of this paper is to provide a mathematical model for allocating human resources using the Markowitz model to the days of the week based on skills and costs, which the objective function minimizes the risk and maximizes the efficiency of resources. The proposed model includes a combination of risk and return assessments to find the best resource portfolio in critical situations. The study also used the Epsilon constraint method. The innovations of this research is the application of Markowitz model to optimize resource allocation and risk-based allocation of resources in the process. The proposed model has been used in a case study of a urology subspecialty kidney center. Numerical results show that using the designed model, resources can be allocated in such a way that it has optimal returns and risk, in order to produce the maximum amount of output in critical situations.
 

Abstract The subject of time-dependent quality indices—whose values vary over time—has attracted significant attention from quality researchers in recent years. Various methods have been developed for this purpose, among which one of the most recent approaches involves analyzing performance profiles over time. Moreover, as competition among manufactured products intensifies, product design has become a primary focus within the product life cycle for manufacturing organizations. One of the most widely used approaches in this context is robust design. The application of response surface methodology and its related optimization tools has also gained great importance in design studies. In this article, a utility function–based method is presented for the robust design optimization of time-dependent quality indices. Based on the results obtained from a pharmaceutical case study, the proposed method performs significantly better than the two existing methods.

Abstract Purpose: The goal of this research is to establish a design framework based on reliability for submarine pressure hulls, with the aim of attaining an ideal equilibrium between structural integrity and reliability.
Methodology: Initially, a mechanical Finite Element Model (FEM) was created and verified by comparing it to experimental data. Following that, various alternative models created through weight optimization algorithms were formulated. Uncertainties were represented using random variables, and reliability assessments were performed for each design.
Findings: The findings suggest that optimized models, even with reduced weights, can provide satisfactory failure probabilities. The prioritization derived from the reliability analysis offers a clear view of the final design.
Originality/Value: This study's uniqueness stems from its combined application of finite element analysis, uncertainty modeling, and optimization methods in reliability-based pressure hull design, a strategy seldom utilized in marine structural design.

Abstract Purpose: In light of the growing challenges in today's supply chains, including market fluctuations, increasing environmental and social pressures, and the need to enhance resilience against foreseeable crises such as the COVID-19 pandemic and economic disruptions, the strategic importance of selecting suppliers that simultaneously meet sustainability and resilience criteria has become more prominent. Accordingly, the main objective of this study is to present a comprehensive, data-driven, and forward-looking decision-making model for evaluating and selecting suppliers within the supply chain, accounting for multiple dimensions of sustainability and resilience simultaneously.
Methodology: In the proposed model, the weights of the defined criteria and sub-criteria were initially determined using the Stochastic Best-Worst Method (SBWM). Supplier performance was then evaluated using the Stochastic VIKOR Multi-Criteria Decision-Making (MCDM) method. In the final stage, the Random Forest regression algorithm was applied to predict future supplier performance. The model was tested through a case study conducted at SAIPA Kashan Automotive Company using expert input collected via structured questionnaires.
Findings: Sustainability and resilience criteria play a central role in supplier selection in the automotive industry. Among the sub-criteria, "greenhouse gas emissions" and "energy consumption reduction" were most influential due to environmental regulations. At the same time, "cost" and "safety stock level" had the greatest impact due to their direct effect on economic performance and operational continuity. Furthermore, the Random Forest algorithm achieved high predictive accuracy (RMSE = 0.0976), confirming the model's ability to generate reliable, data-driven forecasts.
Originality/Value: Although each of the methods used in this research (Random Best-Worst Method, Random VIKOR, and Random Forest algorithm) has been employed individually in previous studies, the main innovation of this study lies in presenting an integrated framework that combines all three approaches. In fact, this research is the first to merge MCDM methods with a machine learning algorithm, offering a comprehensive, data-driven decision-making model. This model not only assesses the current performance of suppliers but also enables prediction of their future performance. Such a combination has not previously been introduced in the supplier selection literature with a simultaneous focus on supply chain sustainability and resilience in the automotive industry, marking a clear methodological innovation.

Abstract Micro-coatings have wide applications in modern industrial production. The splashing of micro droplets during impact on the surface, reduce the quality of the surface coating. Spray pressure is one of the most important factors in micro-droplet splashing. In this research, we study the effect of pressure on the diameter of the micro-droplet splashing using the reliability model based on the censored data. The inverted exponentiated Rayleigh as an adequate distribution is used to calculate the reliability model and the maximum likelihood estimator of the parameters of model are obtained. Also based on the Metropolis-Hastings algorithm, the unknown parameters are estimated. The results indicate that the proposed reliability model performs well in estimating the probability of micro-droplet splashing at different spraying pressures. Based on the proposed model, as the nozzle pressure increases, the micro-droplet splashing diameter decreases.   
  

Abstract The technique of control charts to monitor process behavior is one of the basic tools of statistical process control. Process changes can be divided into two main categories: common (random) cause, which is a fundamental feature of any process, and cause (definable) deviation, the occurrence of which is an unusual disorder that must be eliminated in order for the process to reverse. The main purpose of using management control charts is to separate these two different sources.Control charts are widely used in the analysis and control of production processes to produce satisfactory, sufficient, reliable and economical quality. Optimizing chart parameters is an important issue for quality engineers to improve processes. In this paper, the economic statistical design of the X ̅ control chart under the Pareto shock model based on the double objective design is presented. Actually by applying restrictions on the first type of error, the cost as the economic objective and the second type of error as the statistical objective is considered and then the optimal solutions are selected based on the Pareto front. Finally, through a practical example, the advantages of the proposed approach are shown by preparing a list of optimal solutions and graphical representations.

Abstract In image monitoring, the information contained in an image is evaluated using control charts. A spatial control chart is a type of control chart in which the horizontal axis represents the position within the image. These charts are used to detect abnormal points in an image. Thermovision is a branch of machine vision that deals with the analysis of infrared images. Although infrared cameras have long been used in preventive maintenance to identify faulty equipment, overloads, and loose connections, the images captured by these cameras are usually analyzed only through empirical methods, and the few quantitative studies conducted in this area have not utilized control charts. In identifying defects in power distribution panels, several challenges must be considered, including the variety of equipment used in electrical panels, the lack of sufficient data to train pattern recognition models, autocorrelation, and the complex behavior of heat transfer by radiation, convection, and conduction. The insufficient data for training pattern recognition models such as neural networks makes spatial control charts relatively more advantageous than these methods. In this study, a combination of spatial control charts and robust regression is employed to detect defects in power distribution panels, and the detection capabilities of various control charts for identifying these defects are compared.

Abstract There are many factors and variables that affect the reliability of organizations equipment that neglecting them may cause irreparable damage to organizations. Despite the importance of high reliability of equipment on the profitability of organizations, so far no research has considered the factors affecting it simultaneously and together. Because the relationship between these factors has a lot of dynamics and feedback, system dynamics is a good tool for analyzing the equipment reliability. The purpose of this study is to create and develop a new way to evaluate and improve the reliability of equipment of one of the most important industries in the world using the system dynamics approach in a 5-year horizon. In this regard, first, the key variables affecting the improvement of reliability, identification and their relationships in the form of accumulation and flow diagrams are completed and simulated in vensim software. Then, after creating a flow-accumulation diagram, suitable scenarios for improving performance are discussed. The validity of the model was also assessed by three tests of reference behavior reconstruction, limit behavior and sensitivity. Simulation results indicates that by implementing policies to improve staff training, allocation of resources to preventive maintenance, etc., the reliability of equipment is significantly increased and this will increase the sales and profits of the organization and managers should pay more attention to these variables. 

Abstract Organizations have a unique role as drivers of the economic growth of countries. On the other hand, due to the rapid changes in technologies and business environment, the architecture of the organization based on the principles of the high reliability organization is very important. According to the mission of organizations, dynamic environmental conditions on one hand and the complexity of technologies that are constantly changing on the other hand, create the possibility of any kind of unexpected challenges in organizations. Therefore, organizations always need to use the principles of the organization with high reliability. The purpose of this research is to identify and quantify these principles. This research is applied and developmental in terms of purpose and its research method is combined (mixed method). In this research, qualitative data and then quantitative data have been collected. The statistical population of this research includes 50 experts and academics who are familiar with the issues of organization administration, management and reliability. The combination of qualitative and quantitative data is also exploratory, and the tool used is a questionnaire, so that the first stage uses qualitative approaches to achieve the principles of the organization with high reliability, and in the second stage, the discovered principles are completed and validated with a quantitative approach. The obtained results include six principles, resilience, acceptance of expertise, perceived risk, sensitivity of main executives, sensitivity to failures and mistakes, and organizational culture, which are the principles of the organization with high reliability

Abstract Many researchers have shown that adaptive control charts perform more efficiently than fixed-parameter charts in detecting process shifts. The adaptive charts proposed in previous studies are generally based on the assumption that the observations follow a normal distribution. However, in many real-world processes, the distribution of observations is non-normal or, in most cases, unknown. The control chart proposed in this article is a sign control chart with variable sample size for monitoring the process median. It has two key advantages: first, it does not require the assumption of normality for the observations, and second, it is adaptive, which allows it to detect shifts in the process median more quickly than fixed-parameter charts. The performance of this chart is evaluated based on the Average Sample Number (ASN) until an alarm is triggered, which is obtained using the properties of a Markov chain.