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Improved Data Analysis, a Step Towards Factory 4.0 - A Preliminary Study in a Car Assembly Plant

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Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety (BDAS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 928))

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Abstract

Effective data analysis is one of the key characteristics of the Smart Factory, a term that comes from the concept of Industry 4.0 currently being discussed worldwide. This paper presents an attempt to introduce data mining methods for improved data analysis in a car assembly plant. The presented pilot study, on an example of wheel alignment adjustment process, aims to find correlations between earlier production data and the results at the end of the assembly line for process improvement and problem-solving support. Preliminary findings, along with expected results and benefits are provided. Finally, directions and issues for the further research are presented.

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References

  1. ACATECH: Industrie 4.0. international benchmark, options for the future and recommendations for manufacturing research (2016). http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Sonderpublikationen/INBENZHAP_E_web.pdf

  2. Brynjolfsson, E., McAfee, A.: Big data: the management revolution. Harv. Bus. Rev. 90, 60–66, 68, 128 (2012)

    Google Scholar 

  3. Brynjolfsson, E., Mitchell, T.: Track how technology is transforming work. Nature 544, 290–292 (2017)

    Article  Google Scholar 

  4. Choudhary, A.K., Harding, J.A., Tiwari, M.K.: Data mining in manufacturing: a review based on the kind of knowledge. J. Intell. Manuf. 20(5), 501–521 (2008). https://doi.org/10.1007/s10845-008-0145-x

    Article  Google Scholar 

  5. Ciolacu, M., Tehrani, A.F., Beer, R., Popp, H.: Education 4.0 - fostering student’s performance with machine learning methods. In: 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging, SIITME, pp. 438–443, October 2017

    Google Scholar 

  6. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996). https://doi.org/10.1145/240455.240464

    Article  Google Scholar 

  7. Godfrey, P., Gryz, J., Lasek, P., Razavi, N.: Interactive visualization of big data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015–2016. CCIS, vol. 613, pp. 3–22. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34099-9_1

    Chapter  Google Scholar 

  8. ISO/FDIS: 9000:2015 Quality Management Systems: Fundamentals and Vocabulary (2015). https://www.iso.org/obp/ui/#iso:std:iso:9000:ed-4:v1:en

  9. Kagermann, H., Wahlster, W., Helbig, J.: Recommendations for implementing the strategic initiative industrie 4.0 - securing the future of german manufacturing industry. Final report of the industrie 4.0 working group. acatech, National Academy of Science and Engineering, Munchen (2013). http://forschungsunion.de/pdf/industrie_4_0_final_report.pdf

  10. Kazemitabar, J., Amini, A., Bloniarz, A., Talwalkar, A.: Variable importance using decision trees. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 426–435. Curran Associates, Inc. (2017). http://papers.nips.cc/paper/6646-variable-importance-using-decision-trees.pdf

  11. Koga, T., Aoyama, T.: Discrete state change model of manufacturing quality to aid assembly process design. J. Adv. Mech. Des. Syst. Manuf. 3(4), 378–389 (2009)

    Article  Google Scholar 

  12. Koksal, G., Batmaz, I., Testik, M.C.: Review: a review of data mining applications for quality improvement in manufacturing industry. Expert Syst. Appl. 38(10), 13448–13467 (2011). https://doi.org/10.1016/j.eswa.2011.04.063

    Article  Google Scholar 

  13. Kusiak, A.: Decomposition in data mining: an industrial case study. IEEE Trans. Electron. Packag. Manuf. 23(4), 345–353 (2000)

    Article  Google Scholar 

  14. Plura, J.: Continual improvement within the quality management systems. Kvalita Inovacia Prosperita IV/1, 13–22 (2000)

    Google Scholar 

  15. Rajkumar, R., Lee, I., Sha, L., Stankovic, J.: Cyber-physical systems: the next computing revolution. In: 2010 47th ACM/IEEE Design Automation Conference, DAC, pp. 731–736. IEEE (2010)

    Google Scholar 

  16. Rojek, I.: Miejsce baz danych i baz wiedzy w systemie wspomagania decyzj. Stud. Inform. 30(2B), 35–47 (2009)

    Google Scholar 

  17. Samaranayake, P., Ramanathan, K., Laosirihongthong, T.: Implementing industry 4.0 - a technological readiness perspective. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM, pp. 529–533, December 2017

    Google Scholar 

  18. Sikora, M., Sikora, B.: Indukcja regul jako technika data mining w eksploracji przemyslowych baz danych. In: Kozielski, S., Malysiak, B., Kasprowski, P., Mrozek, D.E. (eds.) Bazy Danych: Modele, Technologie, Narzedzia, Analiza danych i wybrane zastosowania, vol. 2, pp. 95–102. Wydawnictwo Komunikacji i Lacznosci WKL (2005)

    Google Scholar 

  19. Tape, T.: The area under an ROC curve. University of Nebraska Medical Center. http://gim.unmc.edu/dxtests/ROC3.htm. Accessed 02 June 2018

  20. Wallis, R., Erohin, O., Klinkenberg, R., Deuse, J., Strombefger, F.: Data mining-supported generation of assembly process plans. Proc. CIRP 23, 178–183 (2014)

    Article  Google Scholar 

  21. Wang, K.: Applying data mining to manufacturing: the nature and implications. J. Intell. Manuf. 18(4), 487–495 (2007). https://doi.org/10.1007/s10845-007-0053-5

    Article  Google Scholar 

  22. Wang, S., Wan, J., Li, D., Zhang, C.: Implementing smart factory of industrie 4.0: an outlook. Int. J. Distrib. Sens. Netw. 12(1) (2016). https://doi.org/10.1155/2016/3159805

  23. Wang, X.Z., McGreavy, C.: Automatic classification for mining process operational data. Ind. Eng. Chem. Res. 37(6), 2215–2222 (1998). https://doi.org/10.1021/ie970620h

    Article  Google Scholar 

  24. Wang, Y., Zhang, Y., Yu, Y., Zhang, C.: Data mining based approach for jobshop scheduling. In: Qi, E., Shen, J., Dou, R. (eds.) IEMI2013, pp. 761–771. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-40060-5_73

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Manufacturing Engineering Central Department, Opel Manufacturing Poland, A. Opla 1, 44-121, Gliwice, Poland

    Mariusz Rodzen

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  1. Mariusz Rodzen
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Correspondence to Mariusz Rodzen .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Rodzen, M. (2018). Improved Data Analysis, a Step Towards Factory 4.0 - A Preliminary Study in a Car Assembly Plant. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety. BDAS 2018. Communications in Computer and Information Science, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-319-99987-6_37

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  • DOI: https://doi.org/10.1007/978-3-319-99987-6_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99986-9

  • Online ISBN: 978-3-319-99987-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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