MATEC Web Conf.
Volume 277, 20192018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|Number of page(s)||9|
|Section||Data and Signal Processing|
|Published online||02 April 2019|
Analysis on the endogenous mechanism of big data and tower management framework in intelligent manufacturing enterprises
Public Teach. and Education Dep., Changchun Automobile Industry Institute, China
2 Big Data Research Centre, School of Management, Jilin University, China
3 School of M-Science and Info. Engineering, Jilin University of Finance and Economics, China
4 Aviation University of Airforce, Flight Training Base, Changchun 130000, China
5 Computer Teaching and Research Center Jilin University, China
* Corresponding author: Wf64@163.com
This paper makes an in-depth comparative analysis of years of experience in intelligent manufacturing projects and literature research related to Big Data. The 4.0 value chain model and concept are put forward to carry out the logical analysis of the endogenous relationship drive and endogenous management mechanism of intelligent manufacturing. The intelligent manufacturing business management process under the 4.0 value chain is established, and the tower Big Data management framework of intelligent manufacturing enterprises is innovatively proposed. This paper discusses the connotation, elements and drive relationship of enterprise Big Data from three dimensions of business operation, information drive and management policies. The hierarchical structure and related connotation of Big Data are revealed, and the basic characteristics of intelligent manufacturing enterprises Big Data are analyzed. The purpose of this paper is to clarify the difference of the concept between enterprise Big Data and mass data and open the Big Data fundamental research driven by digitization management. It provides basic innovative ideas and scientific research methods for the new generation of digital virtual simulation, digital factory construction and industrial chain management.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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