Journal of Quality Engineering and Management

Abstract Purpose: This paper examines how blockchain technology can improve reliability and operational performance in hospital pharmaceutical supply chains with a focus on inventory variability and responsiveness to demand.
Methodology: A system dynamics model of a three-echelon chain (manufacturer–distributor–hospital) is developed. Two information-sharing scenarios are compared: a traditional setting with centralized, delayed information and a blockchain setting with real-time, decentralized data sharing.
Findings: Results indicate that blockchain adoption enhances behavioral stability, reduces the persistence of hospital backlog, and shortens mean delivery lead time. Specifically, mean lead time decreases by ~15.1% and mean hospital backlog decreases by ~15.8% (both statistically significant). However, the difference in mean hospital inventory is not significant; stability improves, with inventory SD decreasing by ~21.5% and lead-time SD decreasing by ~10%. Taken together, these effects strengthen service reliability and overall supply-chain performance.
Originality/Value: By integrating blockchain-based decentralized data sharing with system dynamics modeling in the hospital pharmaceutical context, this study provides quantitative evidence of how transparency supports quality-oriented supply chain management.

Abstract Purpose: Reliability is one of the most critical quality characteristics of components, products, and systems. Unlike other attributes, it cannot be directly measured and is usually evaluated only after significant operational time under real conditions. However, waiting for long-term field data may reduce market competitiveness in commercial industries and pose serious safety risks in sensitive systems such as military equipment. Therefore, reliability prediction plays a vital role in key decision-making areas such as product release timing, warranty policies, and maintenance planning. This study aims to present an integrated model based on accelerated degradation testing and accelerated life testing to predict the lifetime of a turbine engine nozzle under operational conditions.
Methodology: Initially, the ADT was designed and conducted to monitor the degradation trend of the nozzle's critical feature at various temperature and time levels. Using the power-law and Arrhenius acceleration models, acceleration parameters and the activation energy were estimated. Subsequently, the ALT was performed under high-stress thermal conditions using the extracted parameters, and the corresponding failure times were recorded. Finally, by integrating the results of both tests and applying statistical methods such as maximum likelihood estimation and degradation path modeling, the system's lifetime distribution was modeled.
Findings: The implementation of the proposed model on a turbine engine nozzle demonstrated its ability to predict lifetime accurately and to reduce testing time and cost significantly.
Originality/Value: This model introduces a novel analytical framework that systematically combines two testing methods (ADT and ALT), with the output of one serving as input to the other. The proposed approach can be generalized and applied to other critical industrial and defense-related products.

Abstract Purpose: This study aims to improve diesel engine reliability estimation by using a risk-based model that incorporates key environmental factors, especially wear particles in engine oil, for more accurate analysis than traditional time-based methods.
Methodology: The Proportional Hazards Model (PHM) was used to assess engine reliability based on wear particles in oil. The Harrell and Lee test checked model assumptions, and the Wald test validated coefficients. Reliability was then compared across two engine groups under different conditions.
Findings: The study's results showed that incorporating risk factors, such as the level of wear particles in engine oil, increases the accuracy of reliability estimation for diesel engines. Specifically, it was found that engine age, maintenance status, and operational conditions significantly impact reliability, such that worn-out engines reach lower levels of reliability more quickly. The proposed model, by providing a more precise analysis, can serve as an effective tool for optimizing maintenance scheduling and preventing unexpected failures in industrial systems.
Originality/Value: This research's primary distinction lies in the integration of qualitative data related to the internal condition of the engine (wear particles in oil) with advanced statistical models of PHM, which has been less addressed in previous studies. This approach, by creating a link between condition-based data analysis and reliability analysis, opens new horizons for condition-based maintenance planning.

Abstract Purpose: This paper presents an innovative model for the simultaneous optimization of redundancy allocation and condition-based maintenance in series-parallel load-sharing systems. The primary objective of the model is to determine the optimal level of redundancy so that costs are minimized while system reliability constraints are met.
Methodology: In this research, stochastic dependencies between system components are considered using the proportional hazards model and tempered failure rates to assess reliability accurately. Additionally, transition probability matrices are used to determine the optimal maintenance limits for each subsystem, and periodic inspections are performed. The proposed model is solved using MATLAB, and its performance is evaluated under four different scenarios: 1) a baseline model without redundancy or stochastic dependencies, 2) redundancy allocation without stochastic dependencies, 3) stochastic dependencies without redundancy, and 4) the proposed model.
Findings: The results show that the proposed model achieves an optimal balance between cost and reliability, reducing both failure and maintenance costs. Compared to the various scenarios, the proposed model demonstrates superior performance in optimizing costs and enhancing reliability. The findings also emphasize the importance of simultaneously considering stochastic dependencies and redundancy allocation to improve system performance.
Originality/Value: This research introduces a novel approach by simultaneously considering stochastic dependencies and redundancy allocation in series-parallel load-sharing systems. The proposed model significantly improves system performance and reduces failure and maintenance costs. It underscores the importance of integrating these two factors in optimizing complex engineering systems.

Abstract Today, the increasing progress in technology, the expansion and development of human needs for sustainable technologies have caused attention to electric energy to be in the center of attention more than in the past. Therefore, increasing the reliability of electrical systems in the power industry is very important. The purpose of this research is to provide a mathematical model to calculate and increase reliability in the power distribution network. This research is practical in terms of its purpose and results, and in terms of the method and nature of implementation, it is based on operational research that was conducted using mathematical modeling and using Python software based on data from 1398 to 1402. The findings show that parameters such as generators, high pressure and low pressure busbars, 20 kV to 400 V power transformers, communication cables, capacitors, generators and UPS are more important in calculating the reliability of this network. Therefore, according to the purpose and the corresponding limitations, a suitable mathematical model has been presented for each parameter. The results show that after 50 repetitions and simulations, the ultra-emergency line has a higher importance and rank in reliability among the four output feeders. Also, based on the presented model, it has been observed that the entire 20 kV line under investigation has 0.67 degrees of reliability. The results of this research can be considered as a suitable basis for the implementation of research and operational projects in wide radial networks in the electricity industry.

Abstract The Permasin engine is a critical mission-oriented system, the failure of which will cause the failure of the main mission. Comprehensive studies on its reliability can be a step towards improving the performance of this system. The aim of the article is to analyze the reliability of the Permasin engine in the specified scenarios. In this article, first of all, the structure of Permasin engine as the studied system is described and the breakdown structure of the product is drawn for this system, and according to the mission-oriented nature of this system, there are 2 time scenarios, the basis of floating performance, definition and failure rate for sensitive parts from the standard. MilHDB-217 and NPRD-95 will be calculated in two optimistic and pessimistic modes. By using the breakdown structure, the connection of specific system components and the reliability block diagram are drawn in the Reliability Workbench software. Finally, the reliability of the subsystems and the studied system is calculated separately for both working scenarios. The results show that drive and motor subsystem has the lowest level of reliability compared to other subsystems in rated power.

Abstract Dependency in systems is one of the problems that reliability faces. Dependence increases the probability of failure and can affect the performance of a system; Therefore, it is very important to study and understand the consequences when designing, operating and maintaining the system. Regardless of the dependency, reliability is optimistically estimated and the system fails sooner than expected. In the rail industry, subsystems show a high level of dependence due to their high dynamics and complexity. Failure of any of the subsystems can affect the performance of the entire network and sometimes have irreparable consequences. Identifying dependencies and dependent failures based on the block diagram of reliability and the structure of the rail system can affect the more accurate prediction of reliability and reduce the subsequent consequences. This paper presents a new mathematical model for predicting reliability in the rail industry by considering common cause failure. Various methods have been introduced to estimate the common cause failure coefficient. The results show an increase in accuracy in predicting reliability.

Abstract Reliability is one of the most important issues in the engineering design process. When we use a system, we often want to determine the reliability of this system. Clearly, higher reliability systems are more valuable. On the other hand, the reliability of each system depends on the structure and reliability of its components. Therefore, to increase the reliability of the system, the reliability of its components can be improved. In addition, to increase the reliability of the components of a system, it is necessary to consider its costs. This study, by considering a series-parallel system, determines the amount of increase required for the reliability of system components so that the reliability of the whole system is maximized and the cost of this increase does not exceed a predetermined value. The following is a numerical example for reviewing the results. Finally, a summary of the results of the article is given.

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 Precision machining of complex industrial parts requires special machines, one of the best of which is the CNC milling machine. Malfunction of machines can cause the production line to stop, while repairs of these machines when they break down without prior possibility and preparation of spare parts with Paying attention to the need to import them is time consuming and costly. In this article, the failure of the device on an annual basis is extracted from the failures recorded in the repair and control sheets of the device, which have been prepared and maintained by the personnel. Then, using Markov chain, to estimate the reliability, maintenance and repair and availability of four-axis CNC milling machine in 2011 was used. The results of implementation in 2011-2018, were compared with the indicators of 2011. The evaluation results show that the availability of the device has increased from 96.3% in 2011 to 99.28% in 2018.

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 This research provides a solution for modeling the availability of Feed Water System (FWS) of Heat Recovery Steam Generator (HRSG) in MAPNA Boiler and Equipment Engineering and Manufacturing Company with the aim of determining the system equipment suppliers in such a way that the total cost in the construction and operation of the system is minimized. The Feed Water System is one of the most sensitive parts of boilers; because its failure causes the boiler to stop and incurs irreparable costs to the system. The system may be in three capacities: full capacity, half load, and stop, each of which may impose costs on the system. For modeling, possible states are first written for the system and then the system configuration is plotted using the reliability block diagram and P&ID. Then the results of modeling with Markov chain method are placed in a mathematical optimization model. After solving the model, the optimal strategy in selecting equipment suppliers will be determined. 

Abstract The use of Heat Recovery Steam Generators (HRSGs) in combined cycle power plants dramatically increases the efficiency of power generation. One of the most important parts of HRSG is Feed Water System (FWS). This paper describes the process of selecting the optimal configuration for the FWS of Heat Recovery Steam Generator, which has been done in MAPNA Boiler and Equipment Engineering and Manufacturing Company. First, the important equipment of the FWS is identified and is demonstrated by the reliability block diagram. Then the failure rate of each equipment is estimated using the documents of the International Atomic Energy Agency, the OREDA handbook and the IEEE 500 standard, and the system reliability is calculated in the Excel software environment. Finally, the results were analyzed and based on that, the optimal configuration of the FWS is determined. The proposed approach can also be used to design the reliability-based design of other industrial systems. 
 
 

Abstract The purpose of this article is to design a Non-Linear Mixed Integer Multilevel TwoObjective Mathematical Model to minimize the costs and maximize the reliability of blood supply chain. In this research, the reliability is measured according to the conditions and safety of transportation, temperature fluctuations, packaging standards, laboratory equipment and the demand. To test the model, the problem is modeled and solved by different dimensions using real data. In addition, the sensitivity analysis of the outputs is carried out to parameters changes. To solve the proposed mathematical model, Baron Solver of GAMS 24.9 is used. This model determines the product sent from blood center to hospital, the amount of production in blood center, the amount of blood donated from donors, the number of collection centers, the amount of product inventory in each center and hospital to minimize the costs and maximize the reliability. Given the fact that the first objective function is the maximization and the second one the minimization, there is a conflict between these two functions. That is, the costs will be minimized by maximizing the reliability. The model developed in this study determines the variables of decision so that by maximizing the reliability of supply chain, the costs will be well controlled and the waste and lack of blood minimized.  

Abstract The use of a Functional Block Diagram is usually one of the most commonly used methods to estimate the reliability of products. This method is not responsive in many missionoriented complex systems. Because at each stage of each mission, different sections and subsystems work and then stop at different times. This is precisely the problem of this research, which is a mission-centered submarine for rescue.  For this purpose, in this paper, a method for calculating the reliability of this submarine, which has four 9-step subsystems, has been designed and presented. At first, the functions of each stage are extracted from the subsystems (electricity, secondary, radio-electronics and navigation) during the meetings with experts, then a Functional Block Diagram is drawn for each step. In the following, the potential failure states for each function are determined in the form of a Failure Mode and Effect Analysis tool. Also, for risk analysis, the severity-probability matrix and the average RPN number have been used and good suggestions for improving the design presented. Further, calculating the failure rate for each step by Kim formula, we calculated the reliability of each stage of each subsystem. Then, the reliability of each subsystem is computed. Finally, in order to calculate the reliability of the entire submarine, first, the reliability of each step is obtained by multiplying the reliability values of each of the four subsystems in each step, eventually multiplied by the successive steps. According to the calculations, the total submarine reliability in the design stage is approximately 0.6, which is considered by the experts to be reasonable. 
       

Abstract The reliability assessment of multi-state systems is often investigated by assuming the independence of the components. It is very useful to consider the interactions between components to evaluate the reliability of such systems. The traditional block diagrams do not evaluate the reliability of a repairable multi-state system. The use of simple stochastic process methods is very difficult due to the high dimension of the problem (the very large number of system states) for engineering applications. To the best of our knowledge, the universal generating function has not been used for systems with dependent components. In this paper, using stochastic processes and a universal generating function method, multi-system systems are analyzed in a situation in which the performance distributions of some components depend on the state of others.

Abstract Guarantee costs in production companies are very important and can have a great impact on the profit of the company. Putting effort into reducing these costs may result in profit increase. In this regard, detecting reliability related defects before their occurrence can be useful in reducing guarantee costs and also customer dissatisfaction. Reliability problems may be due to manufacturing defects. Removing defective items during production process can prevent high guarantee and customer dissatisfaction costs. In this paper a model is developed for early detection of reliability defects by means of guarantee data and qualitative parameters of the production line. A case study on TU5 engines manufactured by Irankhodro Company is also included.

Abstract The reliability of manufactured products can vary according to changes in production quality. Field failure data provide useful information for assessing whether changes in reliability are significant or identifying the cause of changes. In order to identify these errors, we need to model the effect of these errors on product reliability. In this research, we intend to predict product reliability behavior based on the percentage of different quality errors with which products may be produced. In this regard, two types of quality errors, namely non-compliant items and assembly error are examined separately. In order to model, it is assumed that the percentage of qualitative errors follow the beta distribution and the failure times follow the Weibull distribution. Reliability, risk rate and probability chart of products are studied under these two types of qualitative errors. Based on the results of this research, it is possible to guess the type and percentage of quality errors with which products are produced.

Abstract Today, supply chain networks in a competitive business environment are faced with the occurrence of potential disruptions, uncertain nature of business parameters and constant changes in market demand that affect the efficiency and performance of the network. This research has developed a new stable-feasibility possibility combination for designing a multi-product closed-loop supply chain network under uncertainty conditions to develop a new approach to planning. Mathematics of credit limitation has been used. The above network has been designed with the objectives of maximizing accountability, reliability and cost minimization. . Reliable models based on credit constraint planning and new robust-credit constraint combination were presented and evaluated using real data from a national industrial project. The results show the proposed robust new combination with average cost-effectiveness and minimum standard deviation , Has improved the stability of the model and its effectiveness.

Abstract Redundancy allocation is one of the important approaches to increase reliability used by system designers. In this approach, to improve the reliability of the system, components or subsets (components) are considered in the system, which in case of failure of sensitive components of the system are quickly replaced and intermittently stop the operation of the system.
It is prevented. In this research, the problem of redundancy allocation for a system with one component with defined constraints and considering the cost of lost production is presented. In this article, the goal is to minimize the total cost of the system, which considers two strategies: cold plug-in and inventory. Cold plug-in refers to a situation where the surplus component is not normally under load and the probability of failure before replacing the damaged component is independent of system performance time. The decision variable in this study is the values ​​of the number of redundancy components and the stock of spare components in stock. The difference between the two is that the plug-in component quickly and without delay replaces the defective component in the system, and its presence does not stop during the maintenance of the main component. The mathematical planning model has been developed to achieve the objectives of the problem as a nonlinear complex problem. An example for the system is also given and solved by GAMS optimization software. Finally, the results of solving the model are discussed.

Abstract Equipment is usually damaged by a random pattern based on a gradual deterioration process. In these cases, the level of deterioration gradually increases, and when its value exceeds the predefined decline threshold, it is considered disabled. In addition, environmental disturbance factors such as temperature, humidity, pressure, etc. may experience uncontrollable changes and alter or accelerate failure patterns. Since estimating the remaining equipment life is very important in the effectiveness of forecast maintenance planning and this estimate should be based on identifying accelerated failure patterns, the present study is the first to estimate the remaining equipment life in the presence of effects. The accelerator is focused on two correlated disturbance factors and in it to monitor the environmental factors affecting the life of the equipment from control charts and how to meet the average residual life of the equipment in different conditions under control, out of control due to the first disturbance factor, Being out of control due to the second factor and being out of control due to both factors are presented. A numerical example is also provided to illustrate the details of the calculations.

Abstract Today, the defense of sensitive areas and resources is a fundamental issue that encompasses all key infrastructures, and to achieve the goal of reducing the damage and injuries caused by the attacker, the use of informed and useful strategies is necessary. In this research, modeling is considered to optimize the protection of investment systems that have a series-parallel reliability structure and have a functional correlation with each other. In general, in this study, first considering the functional independence of subsystems, the probabilities of a successful attack, the system reliability structure and the game theory approach to finding the equilibrium point, a nonlinear programming model is proposed to determine the investment defense of systems. Then, by determining the reliability relationships despite the functional correlation between the subsystems, a linear programming model is introduced to determine the correlation coefficient of the subsystems and to redistribute investment to defend the systems. Finally, the proposed research model is used for a numerical example and the results are analyzed.

Abstract Reliability and safety of any system is the most important quality characteristic of a system. This qualitative characteristic is of special importance in systems whose operation is under various stresses such as: high temperature, high speed, high pressure, etc. A noteworthy point that is often not taken into account in calculating the reliability and safety of systems is the existence of interdependencies between subsystems with each other, which causes different failures in the system. One of the most important of these failures is common cause failure. In this type of failure, several subsystems or all subsystems fail simultaneously or in a short period of time due to a common cause. Failure to consider common cause failures in calculating the reliability of systems leads to an optimistic estimate of the reliability rate of the system and consequently leads to overconfidence in the system. In this paper, first using the product structure separation techniques (PBS), performance flow block diagram (FFBD) to identify and then using the reliability block diagram (RBD) to calculate and allocate the output reliability of a dynamic positioning system that includes hydraulic thrusters And electric for the movements of roll, suge, sui, yaw and hyo, will be discussed.In calculating the reliability of the system with the help of cascade failure probability rules and with the help of beta factor model and PDS method, common cause failures of different subsystems were considered.

Abstract The current trend in various industries acknowledges that establishing a system with the ability to quickly refer to the level of product failures or estimate its reliability is a necessity for every industry. Reliability is doubly important in military industries. One of the products of the military industries is anti-aircraft shells that are used against enemy threats, and the failure or failure to act on time of this product can cause irreparable damage, which increases the importance of this product. In this case, it is in the design stage and there is no previous or experimental data available, the lack of data is considered the main problem, and to solve this problem, we use Bayesian networks, and due to the lack of knowledge of the reliability of components in the design stage and the lack of sufficient and accurate knowledge of experts about the reliability of components, a basis is considered for the reliability of each member, and fuzzy theory is used to obtain reliability. To obtain reliability using fuzzy Bayesian networks, we first draw the product fault tree and by converting the fault tree into Bayesian networks, the product reliability is estimated.