strategic.tools

Data Analysis for Manufacturing

Data analysis is a completely understandable process inherent in mankind since time immemorial. Why has it become so widespread only in the last 10-20 years? All this is connected with the development of computer technology. Data analysis (including big data) is used in industries where technology is needed. For example banking, bioinformatics, image analysis, medical informatics, etc.

The tasks of data analysis are to develop rules for the formation and improvement of a theoretical understanding of the object.

To perform data analysis, this data must be collected. The data must be organized in a specific format. For example, the data table should contain an object and its attribute. Attribute may be as quantitative as not.

Data analysis methods are applied to the collected data. Next, the patterns are defined. Results should be based on identified patterns.

Classification of data analysis methods:

Principal component analysis, Independent Component Analysis used to form quantitative concepts.

Сluster analysis is used to form quantitative concepts.

Regression, Classification and Support Vector Machine Methods are used to form patterns and predict based on them.

Modern manufacturing requires data analysis to establish business processes, production, and more. For example, demand forecasting allows you to rationally purchase components. In result, The entire value chain is optimized.

Science and engineering is another area in modern high-tech manufacturing where data analysis techniques can be used. For example, the selection of chemical formulas for future medicinal preparations. Undoubtedly, this requires a large database of accumulated data. Hence a large number of experiments must be carried out. But do not think that only large commercial giants can afford to work with data analysis. For example, scientists and engineers involved in the synthesis of materials have created an open database that anyone can use. The biggest drawback is the inability to check the relevance of the data. However, good results have been achieved in this area.

by Dimerion Steelson