Wireless Personal Communications

, Volume 98, Issue 4, pp 3147–3162 | Cite as

Model of Knowledge-Based Process Management System Using Big Data in the Wireless Communication Environment

  • Kyoo-Sung NohEmail author


For any small and medium-sized manufacturer, delivery deadline compliance and quality assurance are the most important factors for their survival. And, for the compliance to delivery deadline, the SCM system that integrates the manufacturing process covering from the material purchase to the product release and the demand process covering from the logistics to the sales process is required. To guarantee the manufacturing quality, a system that maintains the optimized rate-of-production by responding in advance to any fault occurrence in the process and/or the facility is required. The big data analysis technology that is required to provide the decision support system optimized for the analysis based manufacturing management for small and medium-sized manufacturers greatly in need for their survival and competition has been introduced. This study aims to develop a model of knowledge-based system for manufacturing facilities optimization specialized for small and medium-sized manufacturers by collecting and analyzing data generated in the supply chain networks and manufacturing facilities of small and medium-sized manufacturers. This study proposes the development of knowledge-based process management system for the survival and the competitiveness improvement for small and medium-sized manufacturers in very weak business conditions. Specifically, the proposal is on the development of system and its service model to support the decision on the manufacturing process management by providing high level knowledge through the data analysis on manufacturing facilities of small and medium-sized manufacturers.


Internet of Things (IoT) Big data Small and medium manufacturer Knowledge-based process managment system 


  1. 1.
    Noh, K-S., & Ju, S-H. (2014). A study on big data applicable policy in the manufacturing field of SMEs. In 2014 SDPM Conference Proceedings.Google Scholar
  2. 2.
    Noh, Y. H., & Hong, S. C. (2011). An empirical analysis on stages of growth in the Korean SME manufacturing industry. Journal of The Korean Official Statistics, 16(2), 82–109.Google Scholar
  3. 3.
    Seethamraju, R. (2014). Enterprise systems and demand chain management: A cross-sectional field study. Information Technology and Management, 15(3), 151–161.CrossRefGoogle Scholar
  4. 4.
    Wang, L., Shi, H., Yu, S., Li, H., Bi, Z., & Fu, L.(2012). An application of enterprise systems in quality management of products. Information Technology and Management, 13, 389–402.CrossRefGoogle Scholar
  5. 5.
  6. 6.
    McKinsey Group, Institute. (2011). Big data: The next frontier for innovation, competition and productivity. New York: McKinsey & Company.Google Scholar
  7. 7.
    Li, L., Zhang, L., & Willamowska, M.-K. (2014). The effects of collaboration on build-to-order supply chains: with a comparison of BTO, MTO, and MTS. Information Technology and Management, 15, 69–79.Google Scholar
  8. 8.
    Kim, S.-H., Kim, K.-A. (2011). An empirical study on the influence of environmental determinants on the mobile cloud computing technology usage and the moderating effects of job relevance. Journal of Information Technology Applications & Management, 18(4), 1–20.Google Scholar
  9. 9.
    Lee, Ho. (2013). Big data utilization plan for strengthening manufacturing competitiveness. Industry Research Institute.Google Scholar
  10. 10.
    Lee, J., Kim, H., Han, J., & Cho, G.(2000). The development and application of SPC software. Journal of the Korean Society for Quality Management, 1(2), 54–61.Google Scholar
  11. 11.
    Wang, L., Zeng, J., & Xu, L. (2011). A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization. Information Technology and Management, 12, 111–119.CrossRefGoogle Scholar
  12. 12.
    Kim, T.-H., Moon, C. B., Kim, B. M., Lee, H. A., & Kim, H. S. (2012). Construction of information management system for user customized manufacturing process. Journal of Korean Industrial Information Systems Society, 17(2), 45–55.CrossRefGoogle Scholar
  13. 13.
    Noh, K.-S., & Park, S. (2014). An exploratory study on application plan of big data to manufacturing execution system. Journal of Digital Convergence, 12(1), 305–311.CrossRefGoogle Scholar
  14. 14.
    Kaul S. D., & Awasthi, A. K. (2017). Privacy model for threshold RFID system based on PUF. Wireless Personal Communications, online version (pp. 1–26).Google Scholar
  15. 15.
    Vallati, C., Mingozzi, E., Tanganelli, G., Buonaccorsi, N., Valdambrini, N., Zonidis, N., et al. (2016). BETaaS—A platform for development and execution of machine-to-machine applications in the internet of things. Wireless Personal Communications, 87(3), 1071–1091.CrossRefGoogle Scholar
  16. 16.
    Han, C., Ha, Y., & Lee. K. (2013). The marketing supporting model for small and medium-sized businesses using big data of telecommunication giants (SKT, etc.), Foundation for SME cooperation Research Reports, 2013.Google Scholar
  17. 17.
    Moon, Y. (2013). Outlook and challenges of big data to change the world and technology, Science and Technology Policy Forum No. 369, 2013.Google Scholar
  18. 18.
    Kim, Y. (2012). Case of productivity and quality improvement through MES big data analysis, Case Study Report SAP, 2012.Google Scholar
  19. 19.
    ORACLE. (2014). Big data architecture for the production data analysis of the manufacturing site, utilization and case, ‘Big Data at Work’ Seminar, 2014.Google Scholar
  20. 20.
    Korea IoT Association. (2013). RFID, M2M Industry Survey, 2014.Google Scholar
  21. 21.
    Lee, S. H. (2014). A study on the competitiveness reenforcement of manufacturing industry utilizing the internet of things. Sejong City: Ministry of Trade, Industry & Energy.Google Scholar
  22. 22.
    Kim, S. (2013). Utilizing big data for improving the quality of the manufacturing process. Industrial Engineering Magazine, 20(2), 42–45.Google Scholar
  23. 23.
    Kim, H., Park, J. H., & Jeong, Y. (2015). Efficient resource management scheme for storage processing in cloud infrastructure with internet of things. Wireless Personal Communications. doi: 10.1007/s11277-015-3093-8.Google Scholar
  24. 24.
    Mohajerzadeh, A. H., Yaghmaee, M. H., & Fakoor, V. (2015). Total data collection algorithm based on estimation model for wireless sensor network. Wireless Personal Communications, 81(2), 745–778.CrossRefGoogle Scholar
  25. 25.
    Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75–86.CrossRefGoogle Scholar
  26. 26.
    Kim, S., & Na, W. (2016). Safe data transmission architecture based on cloud for internet of things. Wireless Personal Communications, 86(1), 287–300.CrossRefGoogle Scholar
  27. 27.
    Sosan, R., & Azim, C. F. (2016). RETRACTED ARTICLE: Mobile cloud computing: The taxonomy and comparison of mobile cloud computing application models. Wireless Personal Communications, 89(4), 1435.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Business AdministrationSunmoon UniversityAsansiKorea

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