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Showing 5 results for Saghaei

Hosein Saghaei, Hosein Didehkhani ,
Volume 20, Issue 4 (IJIEPR 2010)

This research aims at presenting a fuzzy model to evaluate and select Six-Sigma projects.  For this purpose, a model of fuzzy analytic network process (ANP) was designed to consider the relation and mutual impact among the factors. In order to evaluate the projects, nine sub-criteria were considered which were classified into three categories of business, finance and procedural ones. Also to consider the ambiguity related to the pairwise comparisons being used in the research, the fuzzy logic was employed. The fuzzy algorithm being used is in the method of Mikhailov which has various advantages such as the presentation of consistency index and weight vector in a crisp form. At the end, in order to show the applicability, the proposed methodology was applied in an automobile part manufacturing firm.
Rassoul Noorossana, Abbas Saghaei , Mehdi Dorri,
Volume 21, Issue 4 (IJIEPR 2010)

  In an increasing number of practical situations, the quality of a process or product can be effectively characterized and summarized by a profile. A profile is usually a functional relationship between a response variable and one or more explanatory variables which can be modeled frequently using linear or nonlinear regression models. In this paper, we study the effect of non-normality on profile monitoring in Phase II when within or between autocorrelation is present. Different levels of autocorrelation and skewed and heavy-tailed symmetric non-normal distributions are used in our study to evaluate the performance of three existing monitoring schemes numerically. Simulation results indicate that the non-normality and autocorrelation can have a significant effect on the in-control performances of the considered schemes. Results also indicate that the out-of-control performances of the schemes are not very sensitive to low and moderate levels of autocorrelation in moderate and large shifts .

Abbas Saghaei, Hoorieh Najafi,
Volume 22, Issue 2 (IJIEPR 2011)


  Six sigma,

  Rolled throughput yield, Organizational performance

Six Sigma is a well- established approach to improve the capability of business processes in order to gain satisfaction of customers. The performance assessment of a given process is essential to some phases of six sigma methodology. So far, different indicators are used to demonstrate the performance of a process, while many organizations tend to report their organizational performance level. Unfortunately there have been few methods on calculating overall performance. This paper introduces a quantitative model that is formulated by focusing on process features. In addition, a number of numerical examples illustrate the performance of our proposed method in comparison to other methods .

Abbas Saghaei, Maryam Rezazadeh-Saghaei, Rasoul Noorossana, Mehdi Doori,
Volume 23, Issue 4 (IJIEPR 2012)

In many industrial and non-industrial applications the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variables. This relationship is referred to as profile. In the past decade, profile monitoring has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the non-normal response variable. In this paper, the focus is especially on the binary response followed by the bernoulli distribution due to its application in many fields of science and engineering. Some methods have been suggested to monitor such profiles in phase I, the modeling phase however, no method has been proposed for monitoring them in phase II, the detecting phase. In this paper, two methods are proposed for phase II logistic profile monitoring. The first method is a combination of two exponentially weighted moving average (EWMA) control charts for mean and variance monitoring of the residuals defined in logistic regression models and the second method is a multivariate T2 chart to monitor model parameters. The simulation study is done to investigate the performance of the methods.
Rassoul Noorossana, Abbas Saghaei, Hamidreza Izadbakhsh, Omid Aghababaei,
Volume 24, Issue 2 (IJIEPR 2013)

In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary,multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpose, multinomial logit regression (MLR) is considered as the basis.Then, the MLR is converted to Poisson GLM with log link. Two methods including Multivariate exponentially weighted moving average (MEWMA) statistics, and Likelihood ratio test (LRT) statistics are proposed to monitor MLR profiles in phase II. Performances of these three methods are evaluated by average run length criterion (ARL). A case study from alloy fasteners manufacturing process is used to illustrate the implementation of the proposed approach. Results indicate satisfactory performance for the proposed method.

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