The name of this site is "Data Science for Environment and Quality". Environment and Quality are put on the same level. Many messages of this site are for manufacturing.
Many contents in this site are collected for "How we prevent environmental problems." I studied Quality and Environment in the same time. I found that the environment of manufacturing changed but education of qualtiy still be old.
Quality Control, Quality Engineering, Reliability Engineering, Industrial Engineering and Operations Reserch were recommended in manufacturing companies as useful tools. It was for mass-production ( mass-marketing ) .
Statistics is important in these studies. It needs to deal with large amount of data. By the development of computer technology, those studies could be practical.
By the way, when statistics could be a practical, other methods developed rapidly. These methods also use computers. These methods are useful in quality studies. But these are not educated in manufacturing companies enough.
Other methods need computers as a basic tool. These methods are introduced in many educational books for Marketing .
Quality Studies were develped for the shortage of production. We had to make many productions efficiently.
In this time, Average and Standard Deviation are important indexes. Statistics is useful as mathematical way. In managemental way, Normalization and KAIZEN are useful.
If the manufacturing is for the shortage of production, these methods are useful. Actually, some countries use these methods and are developping rapidly, now. This is the reason that Japanese TQM is useful in these country in these days.
For the area where productions are oversupply, we must make various productions rapidly. Marketing has changed from mass-marketing to segmentation-marketing already.
We need a large amount of information. So computer-depended-methods are useful.
To prevent environmental problems , Environmental Economics studies the way of manufacturing. GDP is not so important for sustainable society .
Both shortage and oversupply of productions, manufacturing must change. And studies for quality also change.
Now IT has developped. The methods we can use are increasing.
Strength and Weakness of Big Data
Statistical Way of Making Hypothesis
Selection of Methods
Tools of Data Science (G7, W7, M7)
TQC, TQM and Others
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