Monitoring the variability of beltline moulding process using Wilks’s statistic
DOI:
https://doi.org/10.11113/mjfas.v6n2.193Keywords:
Multivariate statistical process control, Process variability, Covariance matrix, Wilks’s statistic,Abstract
Due to the increase of the complexity of customer demand on products and services, monitoring process quality is becoming multivariate in nature. In this setting there are two important parameters to be monitored, i.e., the mean vector and the covariance structure which determines the variability of the process. This paper deals with process variability monitoring of beltline moulding process at an automotive industry where the process is in multivariate setting and monitoring process is based on individual observations. Our approach is based on Wilks’s statistic. A real application will be presented and the strength of that statistic, as well as its limitations, will be discussed.
References
W.H. Woodall, Controversies and contradictions in statistical process control, J. Qual. Technol. 32 (2000) pp. 341-350.
F.B. Alt, Multivariate quality control, The Encyclopaedia of Statistical Sci., John Wiley & Sons, New York, NY, (1985) pp. 110-122.
M.B. Khoo and S.H. Quah, Multivariate control chart for process dispersion based on individual observations, Qual. Engineering, 15 (2003) pp.
-642.
N.D. Tracy and J.C. Young, Multivariate control charts for individual observations, J. Qual. Technol. 24 (1992) pp. 88-95.
J.H. Sullivan and W.H. Woodall, A comparison of multivariate control chart for individual observation, J. Qual. Technol. 28 (1996) pp. 398-408.
R.L. Mason, Y.M. Chou and J.C. Young, Monitoring variation in a multivariate process when the dimension is large relative to sample size,
Commun. Statist. Theor. and Meth. 38 (2009) pp. 939-951.
S.S. Wilks, Mathematical statistics, John Wiley & Sons, New York, NY, (1962).
S.S. Wilks, Multivariate statistical outliers, Sankhaya A, 25 (1963) pp. 407-426.
A.C. Rencher, Methods of multivariate analysis, 2nd ed. John Wiley & Sons, New York, NY, (2002).
R.L. Mason and J.C. Young, Multivariate statistical process control with industrial applications, ASA-SIAM, Philapedia, (2002).
G.A.F. Seber, Multivariate observations, John Wiley & Sons, New York, NY, (1984).
R.L. Mason, Y.M. Chou and J.C. Young, Identifying variables contributing to outliers in phase I, Commun. Statist. Theor. and Meth. 37 (2008)
pp. 1103-1118.
A.T. Bon, Process Quality Improvement on Beltline Moulding Manufacturing, PhD Thesis (2008).
