A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: An empirical case study of a die casting factory

  • Ju Yeon LeeEmail author
  • Joo Seong Yoon
  • Bo-Hyun Kim
Regular Paper


This paper proposes an architecture and system modules for a big data analytics platform to implement smart factories in small and medium-sized enterprises. The big data analytics platform enables small and medium-sized enterprises 1) to achieve the integrated system environment between the legacy system and the platform; 2) to address quality issues by applying analytical models to their factories; and 3) to reduce their financial burdens of infrastructure and experts for the platform through cloud computing. In terms of evaluation, the proposed platform was applied to the factory of a die casting company in South Korea. Using the big data analytics platform that was developed, this paper also introduced the application scenario to identify defects in the die casting process. From this empirical research, we have clarified the difficulties and challenges in applying big data analytics to small and medium-sized manufacturing enterprises. For future works, this paper suggests a manufacturing data analytics library to provide consolidated information, including a data-mining model, its datasets, and preprocessing methods for specific manufacturing problems.


Big data analytics platform Smart factory Small and medium-sized manufacturing enterprises Die casting process Defective casting 


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Copyright information

© Korean Society for Precision Engineering and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.IT Converged Process R&D GroupKorea Institute of Industrial TechnologyGyeonggi-doSouth Korea
  2. 2.Smart Manufacturing Technology GroupKorea Institute of Industrial TechnologyChungcheongnam-doSouth Korea

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