SPC "Quality" has various meaning. Each quality is studied in different ways. "Quality Study" of this site collects studies common ideas among various areas.
In Japanese TQM , "quality" means both product-quality and management-quality. But quality study only deals with product-quality.
Management-quality is discussed in Management .
I classify "Quality" into Equality, Totality, Reproducibility, Reliability, Productivity and Harmoniousness. There are study-fields for each meaning.
Reliability Engineering is named because it studies "Reliability". In this way, Quality Engineering (in Japan) should be named as "Reproducibility Engineering" because it studies "Reproducibility". "Quality Engineering (in Japan)" uses the word, "Quality" as "Reproducibility".
Relationship between the targets of quality study and each field is represented as the table below. Y = Yes, N = No, P = Partialy Yes. It does not mean "TQM is the all-round tool because it has three Y." The set of different view points is used.
QE and RE are developed to remove the trouble of users. Because the functions of facilities, processes and manufacturing systems product, QE and RE are used for design of these functions.
| Name | Target | SPC | TQM | QE | RE | IE | fE |
| Process Control | Condition of Process |
Y | Y | P | N | Y | N |
| Process Design | facilities, processes and manufacturing systems |
N | Y | Y | Y | Y | Y |
| Product Design | Function of Products |
N | Y | Y | Y | N | Y |
In quality study, Statistics is often used because statistics is a strong tool of the data in mass-production. In mass-production, evenness is important. Evenness is evaluated by Average and Standard Deviation .
But now, mass-production is not only a part of production.
And even if we analyze the data of mass-production, statistics is not always useful. Statistics is good at the data of static phenomena, for example, the data of Design of Experiments . If we use statistics for the data of dynamic phenomena, we may lose important points.
The basis of Data Science is watching the data from various view points before using mathematical models. Data Literacy is more important than statistics in quality study.
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