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Knowledge-Based Multi-agent System for Smart Factory of Small-Sized Manufacturing Enterprises in Korea

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

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Abstract

This paper focuses on the development of practical application prototype of information and communication model that can be applied in the field in order to solve the problems of SME manufacturing and manufacturing companies in each manufacturing industry. We are trying to establish a process to implement a smart factory by adding a specialist network through problem-solving data management based on the cases of bad people in the manufacturing process including field workers. It also has the implication of supporting field work by providing Manufacturing Problem Solving (MPS) processes to shop floor workers.

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References

  1. Bhuiyan, N., Baghel, A.: An overview of continuous improvement: from the past to the present. Manag. Decis. 43(5), 761–771 (2015)

    Article  Google Scholar 

  2. Rother, M.: Toyota Kata: Managing People for Improvement, Adaptiveness, and Superior Results. McGraw-Hill Professional, New York (2010)

    Google Scholar 

  3. Yang, Y.B.: MES technology analysis and optimization for electronic manufacturing industry. In: International Conference on Robots Intelligent System (ICRIS), pp. 202–205 (2017)

    Google Scholar 

  4. Xiong, J.: Fault diagnosis method based on improved evidence reasoning. Math. Probl. Eng. 3(4), 1–9 (2019)

    Google Scholar 

  5. Camarillo, A., Rios, J., Althoff, K.: CBR and PLM applied to diagnosis and technical support during problem solving in the Continuous Improvement Process of manufacturing plants. Procedia Manuf. 13, 987–994 (2017)

    Article  Google Scholar 

  6. Chigurupati, A., Lassar, N.: Root cause analysis using artificial intelligence. In: Reliability and Maintainability Symposium (RAMS), pp. 1–5 (2017)

    Google Scholar 

  7. De Mast, J., Lokkerbol, J.: An analysis of the Six Sigma DMAIC method from the perspective of problem solving. Int. J. Prod. Econ. 139, 604–614 (2012)

    Article  Google Scholar 

  8. Shainin, R.D.: Statistical engineering six decades of improved process and systems performance. Qual. Eng. 24(2), 171–183 (2012)

    Article  Google Scholar 

  9. Prashar, A.: Adoption of Six Sigma DMAIC to reduce cost of poor quality. J. Prod. Perform. Manag. 63(1), 103–126 (2014)

    Article  Google Scholar 

  10. Miltenburg, J.: Production planning problem where products have alternative routings and bills-of-material. Int. J. Prod. Res. 39, 1755–1775 (2001)

    Article  Google Scholar 

  11. Kepner, C.H., Tregoe, B.B.: The New Rational Manager - An Updated Edition for a New World. Princeton Research Press, Princeton (2008)

    Google Scholar 

  12. Ham, W., Park, S.: A framework for the continuous performance improvement of manned assembly lines. Int. J. Prod. Res. 52(18), 5432–5450 (2014)

    Article  Google Scholar 

  13. Riesenberger, C.A., Sousa, S.D.: The 8D methodology: an effective way to reduce recurrence of customer complaints. World Congr. Eng. 3, 2225–2230 (2010)

    Google Scholar 

  14. Marjanca, K.: With 8D method to excellent quality. Revija za Univerzalno Odličnost 1(3), 118–129 (2012)

    Google Scholar 

  15. Liu, D.R., Ke, C.K.: Knowledge support for problem-solving in a production process: a hybrid of knowledge discovery and case-based reasoning. Expert Syst. Appl. 33, 147–61 (2007)

    Article  Google Scholar 

  16. Richter, M.M., Weber, R.O.: Case-Based Reasoning: A Textbook. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40167-1

    Book  Google Scholar 

  17. Camarillo, A., Rios, J., Althoff, K.: Product lifecycle management as data repository for manufacturing problem solving. MDPI Mater. 11(8), 1469–1487 (2018)

    Google Scholar 

  18. Putri, N., Mustafa, K., Norlida, B.: Knowledge management system in industries. In: 4th International Conference on Electronics and System Engineering (ICEESE), pp. 107–111 (2018)

    Google Scholar 

  19. Bach, K.: Knowledge acquisition for case-based reasoning systems. Ph.D. thesis. University of Hildesheim (2012)

    Google Scholar 

  20. Walker, A.J.: An artificial neural network driven decision-making system for manufacturing disturbance mitigation in reconfigurable systems. In: 13th IEEE International Conference on Control & Automation (ICCA), pp. 695–700 (2017)

    Google Scholar 

  21. Camarillo, A., Ríos, J., Althoff, K.: Knowledge-based multi-agent system for manufacturing problem solving process in production plants. J. Manuf. Syst. 47, 115–127 (2018)

    Article  Google Scholar 

  22. Glushchenko, F., Fedotova, A.: Developing automotive production with using product lifecycle management system. In: 2nd School on DCNAR, pp. 38–40 (2018)

    Google Scholar 

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Acknowledge

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1A6A3A11035613).

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Correspondence to Jongpil Jeong .

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Park, B., Jeong, J. (2019). Knowledge-Based Multi-agent System for Smart Factory of Small-Sized Manufacturing Enterprises in Korea. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11624. Springer, Cham. https://doi.org/10.1007/978-3-030-24311-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-24311-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24310-4

  • Online ISBN: 978-3-030-24311-1

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