Journal of Quality Engineering and Management

Abstract Control charts are one of the most effective tools used in quality control to monitor various quality characteristics in a process with the aim to improve quality of the product. Usually, in Shewhart control charts, the normality assumption met for the data, but sometimes there is lack of information regarding the statistical distribution of the observations. For this reason, non-parametric control charts are used in this situation. In this research, non-parametric sign charts are introduced to deal with the lack of information regarding observations’ statistical distribution. Nonparametric Generalized Weighted Moving Average Sign Control Chart (NS GWMA) designed using statistical design and average run length (ARL) and its statistical performance was studied. But statistical design is not enough to ensure the performance of a control chart, so in the next steps, economic design (ED) and economic-statistical design (ESD) were applied using cost model of Lorenzen and Vance, in order to optimize both statistical and economical characteristics of the control chart.

Abstract This research focuses on improving productivity in the home appliance industry through ‎the Kaizen approach. In order to respond to competitive challenges, various ‎organizations are pursuing the implementation of lean production today. The use of lean ‎techniques in manufacturing and even service industries can be seen in writings and ‎articles. From a functional and operational point of view, lean production includes the ‎implementation of a set of tools and techniques that try to reduce waste in the company ‎and the value chain.‎
The main issue includes identifying and reducing waste and mods related to processes in ‎the production system. The main questions in this research focus on the definition and ‎corrective measures to improve productivity and achieve the goals of kaizen in ‎eliminating or reducing waste such as excess production, transportation, etc. Various ‎methods such as case studies and field analyze are used as well as value flow mapping ‎to identify trends and implement corrective measures. ‎

Abstract Allocation is one of the important activities in the reliability process, if it is not done correctly, it will not be possible to achieve the reliability goals for the whole system. To allocate the reliability, methods have been presented that are not free of defects. In this research, five factors of complexity, new technology, operation time, environment and resilience of subsystems are considered and the goal is to consider a new approach in allocating reliability to the goal feasibility method so that different parts and subsystems can be examined and measured based on the desired reliability and resilience. The obtained results are investigated and measured by Foo method with the mentioned five factors and finally validated.
The following items are suggested to researchers in the field of reliability and complex systems:
• Consider the development of each of the four factors in Fu's method in their research.
• Considering this research as a basis, the presented method should be developed in terms of management as well as providing other combined solutions.
• After the development of other factors of Foo's method, software can be developed to assign reliability.
• Designing a mathematical model for assigning reliability so that it includes more factors.
• Finding a quantitative relationship between the cost of a subsystem and its reliability so that the system cost is minimized under the reliability constraint or the system reliability is maximized under the cost constraint.

Abstract Suppose that the lifetime distribution of a random sample of n experimental units is exponential with hazard rate θ and due to time constraints and cost reduction, interval censoring data schemes is used by the experimenter. In this research, some of the existing parameter estimators of the exponential lifetimes under interval censoring are considered. In addition, three new weighted estimators are introduced. we compare the performance of our three methods with some of the existing parameter estimators through simulation studies. For a given sample size n and k inspection times, t1, t2, . . . , tk from the exponential distribution and for each scenario, bias and mean of square error (MSE) are calculated. In addition, the relative efficiency of the proposed estimators are taken into consideration. Moreover, based on the simulation study, their performances are compared to the existing methods. Finally, the asymptotic distribution of one investigated method, which has the best performance, is obtained.

Abstract Despite the important issue of high-reliability organizations, it has not been adequately addressed in our country. Scientific designs that can accurately assess organizations and various assessments and evaluations in the situations of organizations and the possibility of decision-making of organizations and improvement programs are decisive. This aims to provide a comprehensive research need for evaluating high-reliability blocks. In this research, the meta-synthesis method has been used, during which, based on the seventh-stage method of Sandelowski and Barroso, related sources and models have been examined and analyzed, and finally, a five-level model with a comprehensive view has been designed, in which at each level, the characteristics of determining the maturity level have been presented. This model can be used to evaluate and analyze the maturity level of high-reliability organizations, as well as to design an improvement and development program for these organizations.

Abstract In this study, the usual kriging method as a linear statistical estimator and two intelligent methods of artificial neural network ANN and adaptive neural fuzzy inference system ANFIS were evaluated in predicting the amount of electric conductivity and nitrate in groundwater. In order to conduct studies, nitrate concentration in 40 wells in Lanjanat plain of Isfahan was measured by spectrophotometer and electrical conductivity. The input data of the artificial neural model, including the length and width of the geographies, the nitrate concentration, and the electrical conductivity value were determined as the output of the model. In order to investigate the performance and efficiency of artificial intelligence models in predicting qualitative information, qualitative information of 50% of the wells was used for calibration and 50% of the wells were used for validating the models. Finally, the output of the models was compared with the value measured in the observation wells based on the mutual error evaluation criteria. The results showed that the ANFIS model performed better than the other two interpolation models in predicting the value of electrical conductivity and nitrate, respectively, with the root mean square error (RMSE) and (mg/l) of 5.362, with the mean bias error (MBE) 2.365 with a correlation coefficient (R) of 0.767. Also, the ANN model had far better results than the usual kriging method. Based on this, ANFIS model is proposed for spatial prediction of electrical conductivity and nitrate in the study area.