Theme-Logo
  • Login
  • Home
  • Course
  • Publication
  • Theses
  • Reports
  • Published books
  • Workshops / Conferences
  • Supervised PhD
  • Supervised MSc
  • Supervised projects
  • Education
  • Language skills
  • Positions
  • Memberships and awards
  • Committees
  • Experience
  • Scientific activites
  • In links
  • Outgoinglinks
  • News
  • Gallery
publication name Generalized Variance Chart for Multivariate Quality Control Process Procedure with Application
Authors Mohamed Sewalim Hamed
year 2014
keywords Quality Control, Multivariate Process, Generalized Variance
journal Applied Mathematical Sciences
volume 8
issue 163
pages 8137 - 8151
publisher Applied Mathematical Sciences
Local/International International
Paper Link Not Available
Full paper download
Supplementary materials Not Available
Abstract

Generalized variance|S|quality control chart is very powerful way to detect small shifts in the mean vector. The main purpose of this paper, presents an improved the generalized variance |S|quality control chart for multivariate process. Generalized variance chart allow us to simultaneously monitor whether joint variability of two or more related variables is in control. In addition, a control chart commonly requires samples with fixed size be taken at fixed intervals. It is assumed that in both univariate and multivariate control charts, each sample is independent of the previous samples. Industry fertilizers is important one of the chemical industries in Egypt, so that this work concerns the fertilizers industries quality control, especially urea fertilizer with application on Delta fertilizer and chemical industries which is considered one of the leading companies the field of fertilizer production in Middle east with application of multivariate quality control procedures to achieve best one procedure for multivariate quality control. This application shows that the company should use the multivariate quality control chart to determine whether not the process is in-control because the production have several correlated variables, and the used of separate control charts is misleading because the variables jointly affect the process. The used of separate univariate control charts in multivariate situation lead to a type I error and the probability of a point correctly plotting in-controlare not equal to their expected values.

Benha University © 2023 Designed and developed by portal team - Benha University