Community of Practice for Product Innovation Towards the Establishment of Industry 4.0

  • Mohammad Maqbool WarisEmail author
  • Cesar Sanin
  • Edward Szczerbicki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10752)


The aim of this paper is to present the necessity of formulating the Community of Practice for Product Innovation process based on Cyber-Physical Production Systems towards the establishment of Industry 4.0. At this developing phase of Industry 4.0, there is a need to define a clear and more realistic approach for implementation process of Cyber-Physical Production Systems in manufacturing industries. Today Knowledge Management is considered as the next arena of global competition. One of the most promising areas where Knowledge Management is studied and applied is product innovation. This paper explains the efficient and systematic methodology for Knowledge Management through Community of Practice for product innovation, thus connecting manufacturing units at global level.


Smart Innovation Engineering Product innovation Cyber-Physical Production Systems Set of experience  Community of Practice Industry 4.0 


  1. 1.
    Corso, M., Martini, A., Paolucci, E., Pellegrini, L.: Knowledge management in product innovation: an interpretative review. Int. J. Manag. Rev. 3(4), 341–352 (2001)CrossRefGoogle Scholar
  2. 2.
    Verhagen, W.J.C., Bermell-Garcia, P., van Dijk, R.E.C., Curran, R.: A critical review of knowledge-based engineering: an identification of research challenges. Adv. Eng. Inf. 26(1), 5–15 (2012)CrossRefGoogle Scholar
  3. 3.
    Waris, M.M., Sanin, C., Szczerbicki, E.: Enhancing product innovation through smart innovation engineering system. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10191, pp. 325–334. Springer, Cham (2017). CrossRefGoogle Scholar
  4. 4.
    Waris, M.M., Sanín, C., Szczerbicki, E., Shafiq, S.I.: A semiautomatic experience-based tool for solving product innovation problem. Cybern. Syst. 48(3), 231–248 (2017)CrossRefGoogle Scholar
  5. 5.
    Waris, M.M., Sanin, C., Szczerbicki, E.: Toward smart innovation engineering: decisional DNA-based conceptual approach. Cybern. Syst. 47(1–2), 149–159 (2016)CrossRefGoogle Scholar
  6. 6.
    Jayaram, J., Okeb, A., Prajogo, D.: The antecedents and consequences of product and process innovation strategy implementation in Australian manufacturing firms. Int. J. Prod. Res. 52(15), 4424–4439 (2014)CrossRefGoogle Scholar
  7. 7.
    Feigenbaum, E.A., McCorduck, P.: The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World. Addison-Wesley, Boston (1983)Google Scholar
  8. 8.
    Scarso, E., Bolisani, E.: Communities of practice as structures for managing knowledge in networked corporations. J. Manuf. Technol. Manag. 19(3), 374–390 (2008)CrossRefGoogle Scholar
  9. 9.
    Zack, M.H.: Developing a knowledge strategy. Calif. Manag. Rev. 41(3), 125–145 (1999)CrossRefGoogle Scholar
  10. 10.
    Akhavan, P., Jafari, M., Fathian, M.: Critical success factors of knowledge management systems: a multi-case analysis. Eur. Bus. Rev. 18(2), 97–113 (2006)CrossRefGoogle Scholar
  11. 11.
    Baheti, R., Gill, H.: Cyber-physical systems, pp. 161–166 (2011)Google Scholar
  12. 12.
    Lee, J., Lapira, E., Bagheri, B., Kao, H.-A.: Recent advances and trends in predictive manufacturing systems in big data environment. Manuf. Lett. 1(1), 38–41 (2013)CrossRefGoogle Scholar
  13. 13.
    Lee, J., Lapira, E., Yang, S., Kao, A.: Predictive manufacturing system - trends of next-generation production systems. In: IFAC Proceedings Volumes, vol. 46, no. 7, pp. 150–156 (2013)Google Scholar
  14. 14.
    Thiede, S., Juraschek, M., Herrmann, C.: Implementing cyber-physical production systems in learning factories. Procedia CIRP 54(Supplement C), 7–12 (2016)CrossRefGoogle Scholar
  15. 15.
    Kagermann, H., Helbig, J., Hellinger, A., Wahlster, W.: 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. Forschungsunion (2013)Google Scholar
  16. 16.
    Roblek, V., Meško, M., Krapež, A.: A complex view of industry 4.0. SAGE Open 6(2), 1–11 (2016)CrossRefGoogle Scholar
  17. 17.
    Sanders, A., Elangeswaran, C., Wulfsberg, J.: Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing. J. Ind. Eng. Manag. 9(3), 811–833 (2016)Google Scholar
  18. 18.
    Santos, K., Loures, E., Piechnicki, F., Canciglieri, O.: Opportunities assessment of product development process in industry 4.0. Procedia Manuf. 11, 1358–1365 (2017)CrossRefGoogle Scholar
  19. 19.
    Wenger, E., McDermott, R.A., Snyder, W.: Cultivating Communities of Practice: A Guide to Managing Knowledge. Harvard Business Press, Boston (2002)Google Scholar
  20. 20.
    Smith, H.A., McKeen, J.D.: Creating and facilitating communities of practice. In: Holsapple, C.W. (ed.) Handbook on Knowledge Management 1. INFOSYS, vol. 1, pp. 393–407. Springer, Heidelberg (2004). CrossRefGoogle Scholar
  21. 21.
    Pattinson, S., Preece, D.: Communities of practice, knowledge acquisition and innovation: a case study of science-based SMEs. J. Knowl. Manag. 18(1), 107–120 (2014)CrossRefGoogle Scholar
  22. 22.
    Rongo, D.: Managing virtual communities of practice to drive product innovation. Int. J. Web Based Commun. 9(1), 105–110 (2013)CrossRefGoogle Scholar
  23. 23.
    Chu, M.-T., Khosla, R., Nishida, T.: Communities of practice model driven knowledge management in multinational knowledge based enterprises. J. Intell. Manuf. 23(5), 1707–1720 (2012)CrossRefGoogle Scholar
  24. 24.
    Sanin, C., Szczerbicki, E.: Towards the construction of decisional DNA: a set of experience knowledge structure Java class within an ontology system. Cybern. Syst. 38(8), 859–878 (2007)CrossRefzbMATHGoogle Scholar
  25. 25.
    Sanin, C., Szczerbicki, E.: Genetic algorithms for decisional DNA: solving sets of experience knowledge structure. Cybern. Syst. 38(5–6), 475–494 (2007)CrossRefzbMATHGoogle Scholar
  26. 26.
    Ji, H., Sui, Y.-T., Suo, L.-L.: Understanding innovation mechanism through the lens of communities of practice (COP). Technol. Forecast. Soc. Change 118(Supplement C), 205–212 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mohammad Maqbool Waris
    • 1
    Email author
  • Cesar Sanin
    • 1
  • Edward Szczerbicki
    • 2
  1. 1.The University of NewcastleCallaghanAustralia
  2. 2.Gdansk University of TechnologyGdanskPoland

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