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

  • Mariusz RodzenEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)


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.


Process improvement Industry 4.0 Data mining Assembly process Manufacturing data Wheel alignment 


  1. 1.
    ACATECH: Industrie 4.0. international benchmark, options for the future and recommendations for manufacturing research (2016).
  2. 2.
    Brynjolfsson, E., McAfee, A.: Big data: the management revolution. Harv. Bus. Rev. 90, 60–66, 68, 128 (2012)Google Scholar
  3. 3.
    Brynjolfsson, E., Mitchell, T.: Track how technology is transforming work. Nature 544, 290–292 (2017)CrossRefGoogle Scholar
  4. 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). Scholar
  5. 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 2017Google Scholar
  6. 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). Scholar
  7. 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). Scholar
  8. 8.
    ISO/FDIS: 9000:2015 Quality Management Systems: Fundamentals and Vocabulary (2015).
  9. 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).
  10. 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).
  11. 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)CrossRefGoogle Scholar
  12. 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). Scholar
  13. 13.
    Kusiak, A.: Decomposition in data mining: an industrial case study. IEEE Trans. Electron. Packag. Manuf. 23(4), 345–353 (2000)CrossRefGoogle Scholar
  14. 14.
    Plura, J.: Continual improvement within the quality management systems. Kvalita Inovacia Prosperita IV/1, 13–22 (2000)Google Scholar
  15. 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. 16.
    Rojek, I.: Miejsce baz danych i baz wiedzy w systemie wspomagania decyzj. Stud. Inform. 30(2B), 35–47 (2009)Google Scholar
  17. 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 2017Google Scholar
  18. 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. 19.
    Tape, T.: The area under an ROC curve. University of Nebraska Medical Center. Accessed 02 June 2018
  20. 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)CrossRefGoogle Scholar
  21. 21.
    Wang, K.: Applying data mining to manufacturing: the nature and implications. J. Intell. Manuf. 18(4), 487–495 (2007). Scholar
  22. 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).
  23. 23.
    Wang, X.Z., McGreavy, C.: Automatic classification for mining process operational data. Ind. Eng. Chem. Res. 37(6), 2215–2222 (1998). Scholar
  24. 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). Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Manufacturing Engineering Central DepartmentOpel Manufacturing PolandGliwicePoland

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