Journal of Quality Engineering and Management

Abstract Today hidden costs and quality opportunities cost are two parameters not seriously considered in most organizations.in today's competitive world, loss of customers imposes important and significant costs on the organizations. therefor  in this study, in order to reduce the cost of total quality we present a method to calculate the cost of damage to customers ' goodwill and the cost of damage to the organization brand .In working out the cost of damage to the goodwill of customers, first the quality requirements as well as the weight assigned to each requirement is calculated. Having figured out the costs related to the discontented customers, we have embarked on finding out the loss rate damage to the customers' good faith and eventually the cost of damage to the customers' good will. In calculating the damage costs to the organization's brand, from among available models, the Acker model has been utilized which serves our purpose. Following the procedures given in the latter model, the parameters generating costs are identified and then the damage costs to the organization's brand is worked out. finally, the findings indicate that dissatisfied customers inflict the most damage on the organization. Finally, the methods and procedures suggested in the current research are implemented in a selected industry. The obtained results in the industry referred to were endorsed both by the industry's elite experts and academic authorities.
 

Abstract  
The accelerated life test model is one of the optimal models to obtain information about the reliability of the industrial products in the shortest possible time. In this article, the problem of the Bayesian prediction intervals from the Kumaraswamy distribution based on censored data in constant-stress partially accelerated life test model is studied. Since the Bayesian predictive function can not be computed in closed-form, the Markov chain Monte Carlo algorithm is used to construct the prediction intervals. Simulation and real data analyses are performed to compare different Bayesian prediction intervals. The results show that the prediction intervals perform well and contain the actual values of the data. The obtained results can be used to increase the quality and reduce the time and cost of product quality control tests.
 

Abstract The growth of activity and investment of startups in the field of financial technology has provided a good opportunity to increase the quality management system. The application and implementation of quality management system requires a codified and indigenous model, so the present study was conducted with the aim of identifying the effective components and providing a model of quality management system in Fintech startups. This study is a fundamental research in terms of purpose and has been done with a cross-sectional survey approach. Also, because both quantitative and qualitative methods have been used, a research is mixed. The statistical population includes theoretical experts (university professors) and experimental experts (managers of Fintech startups). Purposeful method was used for sampling and continued until theoretical saturation was achieved. Finally, the views of 17 experts were used. The content quality analysis method and MaxQDA software were used to identify the basic categories of quality management system. Structural-interpretive method and MicMac software were used to present the model. Findings show that leadership and management and customer orientation affect the decision-making method, systematic approach to management and employee participation. These factors also affect the continuous improvement of the process and process management. Finally, they lead to improved quality. Level four elements, namely leadership and management and customer-orientation, have the greatest impact on the continuous improvement of the process of public institutions.
 
 

Abstract In this research, a Multi-Objective model for reliability-centered preventive maintenance planning for a production system with parallel series components is developed. In this model, maintenance costs and cost of failures; including the cost of lost production, due to system shutdown are considered. In this way, this model plans preventive maintenance operations with the aim of increasing the system reliability level, with the lowest total cost. A binary nonlinear program is developed and a numerical example is solved for it and the results are discussed. To solve the proposed model, the Augmented Epsilon Constraint Method (AUGMECON) by GAMS software is used and the results are discussed. The results show the effect of preventive service planning on system failure rate and reliability. The proposed approach can be used to plan maintenance of industrial systems by considering the reliability related costs
 
 
 

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 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.