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In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the... 

A new approach is developed in this paper to detect general mean shifts of multivariate quality control systems and to determine the quality characteristic(s) responsible for the shift. This approach takes advantage of both a decomposition method and an EWMA-based control statistics that are employed for multivariate normal distributions. In order to evaluate the performance of the proposed methodology, simulation studies are provided to estimate the in- and out-of-control average run lengths under different mean and variance shift scenarios. Simulation experiments are also given to compare the performances of the proposed procedure with the ones of the well-known MEWMA and MCUSUM methods.... 

In this research, an iterative approach is employed to analyze and classify the states of uni-variate quality control systems. To do this, a measure (called the belief that process is in-control) is first defined and then an equation is developed to update the belief recursively by taking new observations on the quality characteristic under consideration. Finally, the upper and the lower control limits on the belief are derived such that when the updated belief falls outside the control limits an out-of-control alarm is received. In order to understand the proposed methodology and to evaluate its performance, some numerical examples are provided by means of simulation. In these examples, the... 

In this paper, in order to find an adequate method of monitoring the mean vector and covariance matrix of a production process simultaneously, first, some available univariate control methods were reviewed and evaluated. Then, the maximum exponentially weighted moving average method with a better potential application and good performances in terms of average time to signal (ATS) criterion was selected to be extended to the bivariate case. In the extended procedure, by proper transformation of the control parameters, the primary control space is transformed such that all control elements have the same probability distributions. In this case, only the maximum absolute value of the transformed... 

A new methodology is proposed in this paper to both monitor an overall mean shift and classify the states of a multivariate quality control system. Based on the Bayesian rule (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005), the belief that each quality characteristic is in an out-of-control state is first updated in an iterative approach and the proof of its convergence is given. Next, the decision-making process of the detection and classification the process mean shift is modeled. Numerical examples by simulation are provided in order to understand the proposed methodology and to evaluate its performance. Moreover, the in-control and... 

The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models. The Burr III distribution has four parameters known as location, scale, and two shape parameters. The estimation process of these parameters is controversial. Although the maximum likelihood estimation (MLE) is understood as a straightforward method in parameters estimation, using MLE to estimate the Burr III parameters leads to maximize a complicated function with four unknown variables, where using a conventional optimization such as... 

In some quality control applications, processes consist of multiple components, stations or stages to finish the final products or the services. Some quality characteristics in each stage of these processes (called multistage processes) can be represented by a relationship between a response and one or more explanatory variables which is named as profile. In this paper, a general model is proposed for monitoring multivariate profiles in multistage processes. To this aim, the multivariate form of the U transformation approach is first used to remove the effect of the cascade property between the stages. Then, three control schemes are employed to monitor the parameters of multivariate simple... 

In some quality control applications, processes consist of multiple components, stations or stages to finish the final products or the services. Some quality characteristics in each stage of these processes (called multistage processes) can be represented by a relationship between a response and one or more explanatory variables which is named as profile. In this paper, a general model is proposed for monitoring multivariate profiles in multistage processes. To this aim, the multivariate form of the U transformation approach is first used to remove the effect of the cascade property between the stages. Then, three control schemes are employed to monitor the parameters of multivariate simple... 

In some quality control applications, processes consist of multiple components, stations or stages to finish the final products or the services. Some quality characteristics in each stage of these processes (called multistage processes) can be represented by a relationship between a response and one or more explanatory variables which is named as profile. In this paper, a general model is proposed for monitoring multivariate profiles in multistage processes. To this aim, the multivariate form of the U transformation approach is first used to remove the effect of the cascade property between the stages. Then, three control schemes are employed to monitor the parameters of multivariate simple... 

The earned value is a leading technique in monitoring and analyzing project performance and project progress. Although, it allows exact measurement of project progress, and can uncover any time and cost deviations from the plan, its capability in reporting accepted level of deviation is not well studied. This study presented an approach to overcome this limitation by applying statistical quality control charts to monitor earned value indices. For this purpose, project time and cost performance indices of a real construction project were monitored regularly on individual control charts. The results were quite promising, and not only competed well against traditional approaches, but also...