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Authors:
Jaehui Park
Su-young Chi
Keywords: Big data; data-driven framework; high availibility; extensibility; predictive manufacturing.
Abstract:
In recent years, Big data prevail in various domains such as marketing, manufacturing and finance. Although many analytical models and practical systems have been studied, it is not a typical task to adopt them to domain problems. There exists a gap between understanding the problems in the fields and adapting the Big data techniques. This work aims a data-driven framework bridging the gap between existing data management issues and problems on the shop floors in manufacturing industries. In this paper, we propose a high available and extensible data management system to integrate manufacturing data with regard to four factors: man, machine, material and method. This data management system supports a large scale data in terms of reliability and efficiency for data collectors and analytical systems. Furthermore, as an ongoing study, we have investigated a set of requirements and practical problems from field-workers rather than executive managers. We try to formulate a comprehensive system structure towards a predictive manufacturing system that can capture, in advance, potential risk factors, such as, machine worn out progress and production time loss tendency.
Pages: 74 to 77
Copyright: Copyright (c) IARIA, 2015
Publication date: July 19, 2015
Published in: conference
ISSN: 2308-4464
ISBN: 978-1-61208-423-7
Location: Nice, France
Dates: from July 19, 2015 to July 24, 2015