Evaluating Statistical Methods Practiced in Two Important Areas of Quality Improvement

Author(s):  
Subir Ghosh ◽  
Luis A. Lopez
2016 ◽  
Vol 19 (3) ◽  
pp. 77-83 ◽  
Author(s):  
Miroslav Prístavka ◽  
Martina Kotorová ◽  
Radovan Savov

AbstractThe tools for quality management are used for quality improvement throughout the whole Europe and developed countries. Simple statistics are considered one of the most basic methods. The goal was to apply the simple statistical methods to practice and to solve problems by using them. Selected methods are used for processing the list of internal discrepancies within the organization, and for identification of the root cause of the problem and its appropriate solution. Seven basic quality tools are simple graphical tools, but very effective in solving problems related to quality. They are called essential because they are suitable for people with at least basic knowledge in statistics; therefore, they can be used to solve the vast majority of problems.


2008 ◽  
pp. 146-168 ◽  
Author(s):  
Jose D. Montero

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.


Author(s):  
Jose D. Montero

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.


Technometrics ◽  
1998 ◽  
Vol 40 (4) ◽  
pp. 358
Author(s):  
Eric R. Ziegel ◽  
Bovas Abraham

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