TQM Model of Elements-Deployment Table Developed from Quality ...
TQM Model of Elements-Deployment Table Developed from Quality ...
TQM Model of Elements-Deployment Table Developed from Quality ...
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on the plus side, “9.2 Management Review is implemented”, “9.5<br />
Businesses Results is improved”, and on the minus side, “4.5<br />
Customer Satisfaction Degree is improved”.<br />
Factor 2: It is named "Product and service process factor", because the <strong>TQM</strong><br />
element existed that on the plus side, “3.3 Customer-relationships are<br />
constructed”, “2.6 Long term pr<strong>of</strong>it is ensured”, and on the minus side,<br />
“5.5 the GWQM process is established”, “6.1 Qualities, Environmental<br />
regulations, and standards are focused”.<br />
Factor 3: It was named "Relationship factor" because the <strong>TQM</strong> element exist that<br />
on the plus side, “3.3 Customer-relationships is constructed”, and on<br />
the minus side, the elements such as the customer, employee, and<br />
stockholder are concentrated, as “6.1 Qualities and Environmental<br />
regulations are focused”, “6.4 Processes Management and<br />
Improvement are proceeded”, “8.1 Basis <strong>of</strong> Human Resources<br />
Developments is established”, “9.4 Stockholders-relationship is<br />
improved”<br />
.<br />
(4) Accumurateive Contributiion Ratio is 0.976, caluculated by Factor 1, Factor 2,<br />
among <strong>of</strong> 3 factors that Factor model could be explained by these 2<br />
fctors.<br />
(B) Application <strong>of</strong> Principal Component Analysis<br />
(1) Selection <strong>of</strong> analysis method<br />
The matrix <strong>of</strong> <strong>Table</strong> side ☓ <strong>Table</strong> top <strong>of</strong> 48☓233 and 49☓49 for the time<br />
series comparison by the Principal Factor Analysis Method is prepared and<br />
analized, but the Correlation Coefficient Matrix fall the rank falling (omission).<br />
The reason is in the cause that there are a lot <strong>of</strong> 0 and no quite effective data in<br />
the element <strong>of</strong> the matrix. Moreover, because the number <strong>of</strong> rows was able to be<br />
used up to 256 rows, the Principal Component Analysis Method was adopted, and<br />
the matrix <strong>of</strong> 48☓49 was prepared and used.<br />
(2) Extraction and interpretation <strong>of</strong> Principal Component<br />
The Cumulative Contribution Ratio becomes 7.53% by 5 Principal<br />
Components as the <strong>Table</strong> 6.3 after the results <strong>of</strong> studies <strong>of</strong> the number <strong>of</strong><br />
Principal Component by the Eigenvalue.<br />
The Principal Component name was interpreted <strong>from</strong> the Principal Component<br />
Scorebook and the Principal Component Score Scatter Diagram with <strong>TQM</strong> <strong>Elements</strong><br />
<strong>Deployment</strong> <strong>Table</strong> and 5 Principal Components as follows.<br />
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