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http://dx.doi.org/10.9717/kmms.2016.19.8.1516

Developing a Big Data Analytics Platform Architecture for Smart Factory  

Shin, Seung-Jun (Graduate School of Management of Technology, Pukyong National University)
Woo, Jungyub (Information Technology Laboratory, National Institute of Standards and Technology)
Seo, Wonchul (Division of Systems Management and Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.19, no.8, 2016 , pp. 1516-1529 More about this Journal
Abstract
While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.
Keywords
Smart Factory; Big Data; Data Analytics; Machine Learning; Manufacturing Execution System; Energy Prediction;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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