Journal of Quality Engineering and Management

Abstract  
The length of waiting time is considered as a key determinant of patient satisfaction level; besides, it has a significant impact on the effectiveness and quality of services which are provided to patients. In addition, Simulation study is also considered as an effective tool for improving patient flow, reducing waiting time and increasing patient satisfaction. Hence, in the present study we made use of simulation to improve the quality of health care in a urology unit which is a part of a kidney center in Tehran. Put differently, the aim of the current study was increasing the efficiency of Extracorporeal Shock Wave Lithotripsy (ESWL) method besides reducing the waiting times and the patient cancellation rates. Therefore, a Discrete-Event Simulation model was developed using iGrafx software, which was later used in conjunction with MATLAB software to form and evaluate the urology unit improvement scenarios in three categories: changing technicians’ presence schedules during different shifts, changing the schedule of patient visits, and examining the factors that reduce the patient cancellation rate. The greatest differences were resulted from the employment of scheduling scenarios of patient visits on waiting time and duration of stay with averages of 23.33 and 22.81 minutes, respectively. In addition, the results of examining the factors that reduce patient cancellation rate demonstrated that the number of cancellations were decreased with an average of 1.44 cases per day. In each category of proposed scenarios, applying changes based on the selected scenario of that category led to significant improvements in performance criteria of the urology unit. It is also worth mentioning that the impact of scheduling scenarios of patient visits was much higher than other scenarios. 

Abstract In this paper, a mathematical model for predicting expected costs to the manufacturer and buyer in pro-rata warranty policy under inflationary conditions is developed. In this model, the cost of individual claims to the manufacturer is limited to fixed cost 𝐶𝐼 whereby the manufacturer carries out all rectification action with a portion of the cost to the customer if the cost of rectification is below a limit 𝐶𝐼. If the cost of rectification exceeds 𝐶𝐼 then the customer pays the difference between the costs of rectification and 𝐶𝐼.based on this model we analyze respectively the expected warranty costs from the perspectives of the manufacturer and the consumer. A real data are executed to demonstrate the applicability of the model. It is proved that, inflation rate, warranty period length and failure parameters impacts the manufacturer and the consumer expected costs. 
 
 

Abstract
 Supervising the service quality and aligning performance objectives in cloud service centers facilitates the effective delivery of the requirements and related operational goals. Nowadays, with huge level of resource sharing and dynamic workloads in data centers, there is a need for comprehensive approaches for service quality supervisory in clouds. Previous research has considered this issue from different aspects and at present, there is a lack of multi-objective and methodologic supervisory approach to connect major previous approaches. Therefore, compared with related work, the purpose of this research is to take steps to compensate the mentioned deficiencies and the most important achievement is a novel service contact aware and quality supervisory methodology in cloud data centers. Proposed methodology, unlike existing solutions, is independent from the management strategy and environmental operators and supervises the goal quality metrics in cloud ecosystem. The empirical results indicate the usefulness and superiority of the proposed methodology in identifying quality bottlenecks and supervising the service quality targets. 

Abstract  Wind Turbines (WT) are one of the fastest growing sources of power production in the world today and there is a constant need to reduce the costs of operating and maintaining them. Condition monitoring (CM) is a tool commonly employed for the early detection of faults/failures so as to minimise downtime and maximize productivity. In this paper, we examine the statuses of wind turbine condition monitoring, maintenance strategy, signal processing methods, SCADA system and criterion design of condition monitoring system failure. The purpose of this paper is to develop a method of interpreting information collected from wind turbines via SCADA using state-of-the-art science that was not used in the past due to the lack of appropriate analytical tools. Indeed, by integrating condition monitoring, SCADA data analysis and signal processing, a new approach is introduced that reduces maintenance costs by monitoring all mechanical and electrical components. 
 

Abstract Quality management is a paradigm shift in management philosophy to improve effectiveness and a source of competitive advantage, innovation and change. The aim of this study was to identify the key factors for the success of TQM and rank them. The present study is applied and descriptive. To collect data, questionnaires based on multi-criteria decision-making methods used in this study were used, which were completed by experts of Akhshan Shiraz Company. Data were analyzed using a combination of fuzzy dimtel approach and fuzzy network analysis. According to the results in the production environment, the factors of commitment of senior management and leadership, human resource management and finally training and learning have the most and the factors of supplier management and modeling have the least effect on success. In general, soft management factors have a greater impact on the success of TQM than soft communication and hard factors.  
 

Abstract One of the most important factors of effect on designing effective supply chain is reduction of economic costs. On the other hand, greenhouse gas emission and pollutants increase has driven managers of organizations and researchers to look for designing and setting up networks that are focused on optimization of environmental factors and reduction of pollutants in all sectors in addition to economic optimization. In addition to these two factors, product delivery time is one of factors of effect on supply chain. In this research an integrated direct and reverse logistic model is studied by considering three objective functions: minimizing environmental effects, economic costs and delivery time. In this research model, uncertainty is considered through solution robustness. Possible scenarios are defined and evaluated by common risk management tools. Then scenario-oriented robust model of the problem is presented by considering uncertain parameters. The model is completed by replacing occurrence rate of each scenario by outputs of risk assessment (normalized RPN) in the model. Then the model is turned in to a single-objective model by applying LP metric method, and solved with the GAMS software. Finally it can be said that an effective supply chain can be designed by combining risk assessment and robust optimization.     
 
 

Abstract Privatization is considered as an approach to help organizations and corporates to achieve high efficiency in developing countries. Therefore, the way it affects the corporate performance is of great importance, because privatization and organizational excellence are so complicated due to feedback relationships, time delays, and dominant non-linear relationships. Moreover, the dynamics of the privatization’s effects on organizational excellence over time should be analyzed using a system dynamics approach in order to examine the effectiveness of privatization so that it can help corporates to make appropriate decisions and invest on the privatizations aspects with highest impact on organizational excellence. Privatization has not analyzed using the system dynamics approach in previous studies. Thus, this study aims to analyze and model the privatization’s effect on corporate performance using the system dynamics approach and EFQM excellence model in five stages in the National Iranian Drilling Company. After the statement of the problem, the key variables are identified and the time horizon is determined. Then, the dynamic hypothesis is presented and the structure of a causal loop diagram is depicted. The surface and flow maps are also explained and the model is simulated. Afterwards, the validity of the proposed model is examined based on validation tests. Finally, by analyzing the sensitivity of the model variables, designing and evaluating policies at the National Iranian Drilling Company are dealt with. Analyzing the impact of privatization on organizational excellence creates a set of advantages for the company using the dynamism systems, the most important ones are: simulating the effects of affective factors on the results; implementing "what if" to analyze the scenarios and potential. future threats; The ability to visually performance the relationship between privatization and organizational excellence; reduce the risk of executive programs by simulating and examining the consequences of different scenarios before implementing the dynamic model; and considering the time reliance between cause and effect. The effects of any changes can be simulated before practical application by using the proposed model in a virtual environment, and decision can be made by complete knowledge of all aspects. 
 
 
 

Abstract Nowadays, many products are the outputs of multi-stage processes. In such processes, the stages are often interdependent, meaning that the quality of the product in a particular stage depends not only on the quality in the current stage but also on the quality of the product in the previous stages. This phenomenon is referred to as the cascading property of multi-stage processes. The existence of this property and the lack of attention to it can lead to errors in interpreting the control charts used in the stages. Therefore, in the literature on multi-stage process monitoring, methods have been proposed to reduce or eliminate this problem. On the other hand, in some cases, the quality of a product is described by the relationship between a response variable and one or more independent variables, known as a profile, which can be the result and output of a multi-stage process. Since fewer studies have been conducted on monitoring profiles resulting from multi-stage processes, this paper investigates the effect of the cascading property on the monitoring of simple linear profiles in a two-stage process, based on the average run length criterion using simulation, and also examines the effect of inter-stage dependency on the estimation of the profile parameters of the second stage.

Abstract Reliability is an essential characteristic in any application area and is defined as the probability of performing a specified task under given conditions within a predetermined period of time. In this study, the strength (capacity) of a product and the stress or load applied to it are used to model and calculate reliability.
In most cases, the imposed load cannot be controlled; however, various approaches can be employed to improve the capacity of the manufactured product. This paper investigates the effect of reducing variability in each quality characteristic of a product under specific distributions.
Analytical and numerical results indicate that decreasing the variability of the product’s quality characteristics increases its capacity and consequently enhances its reliability.

Abstract Dispersion is the enemy of quality and an inherent and integral part of manufactured products. Therefore, identifying critical components and their contribution to total variation is an important engineering task, and measuring it in general without the most restrictive assumptions is very complex. The present article presents a systematic approach that can identify the contribution of each component to the total variability in a complex system, in proportion to the type of mechanism of action of the components. The index introduced in this study determines the contribution of the components and can be used as a measure of the criticality of the components of a system. The application of the proposed method in this article does not require any assumptions regarding the linearity of the functional function of the components or the normality of the statistical distribution of the quality characteristics, and includes the analysis of all systems with uncorrelated components. The proposed solution can be used as a powerful tool in the analysis phase of Six Sigma and Lean Six Sigma, and with its help, it is possible to prioritize and policy the use of resources in order to reduce process dispersion. To understand the proposed method in more detail, two well-known examples in industrial engineering are described.

Abstract  
 If the identification of small changes in the production process is intended, the Moving Averaging Control Chart (EWMA) is a good alternative to the X control chart. In situations where a large sample of the population cannot be extracted due to economic constraints, the Simple Random Sampling Scheme (SRS) may not be accurate enough, in which case the RSS can be used. Appeared. In this paper, for the first time, the economic and statistical-economic design of the EWMA control chart under the RSS design is reviewed. By presenting numerical results, the advantages of statistical-economic design over economic design are shown. The results show that costs in statistical-economic design have increased slightly compared to economic design, but due to the low false alarm rate are in line with statistical quality control objectives and at the same time reduce costs. , Controls the quality of the product at the desired level of error and high power. Keywords: Statistical-economic design, ranking set sampling, shock model. 

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 We introduce a method for the statistical design of a depth-based control chart, using the percentile-based approach. The proposed control chart is affine invariant and is asymptotically distribution-free. Generally, the performance of a control chart is evaluated with the average run length metric. The average run length metric has a geometric distribution skewed to the right with a large standard deviation and may not be a proper measure for evaluating the control chart. Therefore, we use the statistical design method of control charts with the PL approach, which is an improvement and development on classical statistical design. By employing constraints on average run length, the length of in-control and out-of-control performances are guaranteed with predetermined probabilities and we can ensure that the in-control run length exceeds the desired value and the out-of-control run length is less than the desired value. Simulation studies show that the proposed control chart is more efficient than the average run length approach.

Abstract Today, degradation analysis is one of the most important approaches in evaluating the reliability of multi-component systems. As it is clear, improving the performance of real systems requires the use of efficient and predictable approaches to analyze degradation with considering the interaction of system degradation processes on each other. The literature review shows that degradation analysis of multi-component systems has been investigated in various researches, but the approach in which there is a profile relationship between the degradation processes of the system components has not been considered so far. When there is a functional relationship between the degradation processes of one or more components, it is called a profile in the statistical process control literature. The aim of this study is to provide an efficient approach to predict and evaluate the variability of degradation processes in the presence of multivariate profiles under the conditions of stochastic dependence. In fact, the proposed approach offers the possibility of predicting and evaluating the variability of degradation processes at the component and system level. In this paper, in order to evaluate the proposed approach, the data set of a multi-component system with a structure of 2 out of 3 has been used. The results show the effectiveness of the proposed approach.

Abstract Purpose: Given the increasing complexity and uncertainty in today's organizational environments, making informed, timely, and flexible decisions is of paramount importance for ensuring the sustainability and long-term growth of organizations. Accordingly, the present study aims to identify and prioritize individual, group, organizational, technological, and environmental factors that influence the quality of managerial decision-making, thereby providing a practical framework for enhancing decision-making capacity and increasing organizational resilience.
Methodology: This study is applied in nature and adopts a descriptive-analytical approach. Initially, a comprehensive set of factors was extracted through a systematic literature review. Then, a five-member expert panel was formed to identify the evaluation criteria, and the required data were collected using intuitionistic fuzzy numbers. The relative weights of the criteria were calculated using the FUCOM method, and the alternatives were finally prioritized using an aggregated decision matrix based on the multi-criteria WASPAS method.
Findings: Specialized knowledge is the most significant factor influencing the quality of managerial decision-making. It was followed by participative decision-making and data accuracy and reliability, which ranked second and third, respectively. In contrast, factors such as organizational structure and the legal, cultural, and social environment had the least impact. Overall, these results highlight the importance of focusing on internal and controllable factors to enhance organizational sustainability in dynamic environments.
Originality/Value: This research is among the first to provide a comprehensive prioritization of individual-to-environmental factors affecting the quality of managers' decisions by integrating a systematic literature review, intuitionistic fuzzy numbers, the FUCOM method, and the WASPAS method. The resulting framework not only enriches theoretical understanding but also serves as a practical guide for policymakers and managers in optimal resource allocation.

Abstract Purpose: The study aims to develop a new Bayesian estimation approach, termed the E2-Bayesian method, for estimating the traffic intensity parameter in the multi-server M/M/c/∞ queueing system. Given the crucial role of accurate efficiency estimation in optimizing service systems, this research addresses the need for more reliable inference under uncertainty.
Methodology: The M/M/c/∞ queueing model, characterized by servers, exponential interarrival times with rate parameter λ, and exponential service times with rate parameter μ, is considered. The traffic intensity parameter is estimated using Bayesian, E-Bayesian, and the newly proposed E2-Bayesian methods under the general entropy loss function. The performance of the proposed estimator is assessed through Monte Carlo simulation and validated using a real dataset.
Findings: Simulation results and empirical analysis demonstrate that the proposed E2-Bayesian estimator outperforms the traditional Bayesian and E-Bayesian estimators in terms of efficiency and accuracy. The estimator that minimizes the mean waiting time of customers in the queue is identified as the optimal choice.
Originality/Value: This research introduces a novel E2-Bayesian estimation approach that enhances the precision of parameter estimation in queueing models under uncertainty. The integration of the general entropy loss function provides a flexible and robust framework, contributing to the advancement of Bayesian inference in stochastic systems.

Abstract Decision variables aimed at improving system performance are considered one of the key issues in most industries. To this end, numerous multi-objective optimization methods have been developed in recent years to address such problems. However, most of these approaches overlook the potential correlations among objectives. In this study, an optimization approach based on a utility function framework is proposed to solve the perindopril separation problem (perindopril is a widely used and effective pharmaceutical agent for treating cardiovascular diseases and hypertension). The proposed approach not only places all objectives at a minimally acceptable level of utility from the decision maker’s (DM) perspective but also accounts for the potential correlations among the objectives and their relative importance. A comparison of the results obtained from a numerical example using the proposed method with those of existing approaches in the literature demonstrates its favorable performance.

Abstract The growing need for systems with high availability/reliability has led to numerous studies in recent years on reliability optimization (availability, if the system is repairable). The use of different redundancy policies and adding extra components are generally considered effective ways to increase system availability. When the system is multi-state, due to the computational complexity involved, the methods used to calculate system availability play a crucial role in providing an acceptable solution. This paper aims to minimize costs for multi-state systems under the constraint that system availability must exceed an acceptable threshold. The redundancy allocation problem is modeled as heterogeneous, meaning that components in such a system can differ from one another. Both components and the system can have multiple states. To compute system availability, the Universal Generating Function (UGF) algorithm is employed, and to optimize the system structure, the Genetic Algorithm (GA) is used.

Abstract Since the beginning of human existence, humans have sought ways to reduce risks and threats in their living and working environments and to make them safer. Accidents such as airplane crashes, factory explosions, and similar events, which have resulted in extensive human and financial losses, have further emphasized the importance of accuracy and focus on methods for reducing the occurrence of such incidents. Moreover, with the rapid advancement of technology and the increasing complexity of systems, the severity of damages and consequences arising from accidents has increased. Therefore, enhancing the reliability level of equipment and systems, from simple to complex and even highly complex networks, is of great importance. The concept of reliability was first introduced during World War II in the context of improving the dependability of military equipment. The metal powder production industry is exposed to the risk of dust explosions due to dust generation. Therefore, analyzing the production system, identifying critical equipment, and evaluating the reliability of the system are among the most important ways to reduce the probability of explosions. In this paper, the fuzzy reliability of an aluminum powder production system is investigated based on statistical data, and it is demonstrated that fuzzy reliability is significantly more accurate than classical reliability.

Abstract In the statistical control of multivariate processes, two or more quality characteristics must be controlled simultaneously. In controlling such processes, two main goals must be achieved. The first goal is to detect out-of-control conditions and the second goal is to identify the quality characteristics that cause the deviation when an out-of-control condition occurs. In this research, ways to achieve the first goal are investigated and methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented. The main goal of phase 2 is to quickly detect shifts. In this paper, two methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented and the shift in one of the quality characteristics of case 1 (average trail length) and the detection of ARL are investigated. Simulation results show that the proposed methods reduce the out-of-control condition more quickly.

Abstract Control charts are powerful tools used for monitoring process variations. In statistical process control, there are many situations in which qualitative (attribute) characteristics of a product or process are monitored simultaneously. Although an out-of-control signal in control charts indicates the presence of a process change, the exact time at which the change occurs is often unknown. Identifying the precise change-point helps process engineers determine the causes of variation and improve the process.
In this study, statistical process control techniques are examined for a process in which qualitative attribute characteristics of the conforming/nonconforming type are measured in a bivariate form. Using the maximum likelihood method, we propose an estimator for determining the change-point in such processes. It is assumed that the two qualitative attribute characteristics in a product are correlated. Simulation results show that the proposed method performs well in detecting the change-point in the process mean vector.





 




 

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 two parameter exponential distribution is particularly important among statistical distributions due to its constant failure rate and has applications in the fields of medicine, biology, clinical trials, public health, engineering, economics, demographics, and life span data, and reliability. Due to the importance of life span data, two parameter exponential distribution with censored data has recently attracted the attention of many researchers, but so far the inference about the location parameter with random censored data in the presence of outlier data has not been discussed. In this article, the location and scale parameters of two parameter exponential distribution under random censoring with the presence of k outliers are estimated by Bayesian and classical methods. Due to the importance of the spatial parameter, when censoring the two parameter exponential distribution with the presence of outlier data, the spatial parameter is considered the same but the scale parameter is different. In the Bayesian estimation of parameters, the is checked using Gibbs sampling under the error squared loss function. We recommend used the Bayesian estimation.
The generalized variance is given according to the dimensions of the parameters using the maximum likelihood method.

Abstract Purpose: The objective of the present study is to determine an optimal configuration in terms of the type and number of components in order to maximize system availability and reduce costs, using an active redundancy allocation strategy, while considering load-sharing capability and the use of maintenance personnel under maintenance and leave policies, in the electrical power distribution system of a marine vessel. In the active redundancy strategy, all additional components and subsystems are operated simultaneously from the start of system operation, and the system fails only when all components have failed.
Methodology: In this study, a bi-objective model is developed for an electrical power distribution system with active redundancy in a marine vessel, where the first objective is minimization of total cost and the second objective is maximization of system availability. System behavior is simulated using a Markov chain and a phase-type distribution, and the model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Failure of one component affects the failure rates of other components within the same subsystem, leading to an increase in their failure rates. In other words, the problem is analyzed under a load-sharing condition. A single repairman is considered for equipment repair. The maintenance and leave policy is defined such that if a component fails during the repairman’s leave period, the leave is terminated and repair of the failed component begins immediately. If another component fails while a component is under repair, it is placed in a repair queue, and the repairman starts repairing the next failed component immediately after completing the repair of the previous one. When the repairman is on leave and no component failure occurs, the repairman may resume the leave period.
Findings: The results of the study identify the optimal combination of the type and number of electrical power distribution panels in each subsystem of the vessel’s electrical power distribution system, aimed at increasing system availability and reducing costs through the use of active redundancy. In addition, the results provide the probability of the repairman being busy, which can support managerial decision-making regarding maintenance and leave policies.
Originality/Value: Considering the innovative aspects of the study, the results can be effectively used for engineering analyses, particularly in evaluating system availability, as well as for managerial analyses, including cost estimation and the allocation of maintenance personnel.

Abstract Nowadays due to diversity of customer demand and short time for product evolution cycle in market, manufacturing strategy is tended to short run processes characterized by high diversity and low volume. Hence, statistical process control for such processes because of inspection restrictions in a short period is a special and significant practice. In such circumstances, control charts in Phase I cannot be performed and also correct estimations are not available for appraising process parameters. Therefore, it is essential to design new control charts and to utilize them instead of traditional control charts for monitoring such processes. On the other hand, sometimes quality characteristics are described by a relationship between a response variable and one or more explanatory variables, referred to as profile in the literature. In this paper for monitoring quality characteristics delineated by simple linear profiles in short run processes, three control charts are designed to monitor profile parameters (intercept, slope and standard deviation).These control charts have a capability to update the parameter estimations along with new observations and concurrent checks of the out-of-control conditions. The Performance of the proposed method has been compared with competitor control chart by using simulation studies and average run length criterion. The results show that proposed method in some parameters has better performance compared to the competitor control chart in detecting moderate and large shifts.