M.Sc Student | Shourky Keren |
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Subject | Production Process Control by Multivariate Statistical Process Control (MSPC) |

Department | Department of Quality Assurance and Reliability |

Supervisor | Dr. Pnina Sason |

Statistical Process Control (SPC) is one of the most important and common methods in which a product or a service process control is conducted. The industry field is characterized with multivariable processes where the variables are usually correlated among themselves. In these cases using SPC is not sufficient enough.

This paper
introduces a statistical tool which answers the problem described above: **Multivariate
Statistical Process Control (MSPC). **The method chosen to monitor the
process is controlling Hotelling's T^{2} statistic. The basic idea of this
method is to transform the number of the quality variables, which are being
checked, into one single variable T^{2}. This variable stands for the
statistical distance between the observation vector in each checkpoint and the
mean vector of those quality variables.

A quality control process is performed on the production of mixed nuts roasting, by measuring three quality variables of the product during the production process: percentage of moisture, percentage of salt, and a qualitative variable - product organoleptic (looks, taste and smell).

The
implementation of MSPC for this production process includes the formulation of
a control chart and limits for the Hotteling’s T^{2}, based on a set of
historical data (HDS), applying new observations to the chart and examination
of the outliers. Analysis of the distribution of the process variables, as well
as that of T^{2} statistic, and the assumptions which have been taken,
are explained in the paper.