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Mapping Requirements and Roadmap Definition for Introducing I 4.0 in SME Environment

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Advances in Manufacturing Engineering and Materials

Abstract

Industry 4.0 as a new manufacturing paradigm brings in a new wave of networked manufacturers and smart factories, which will determine future competitiveness of manufacturing companies. The aim for researchers should thus be to generate and optimize innovative solutions for different types of producers including SMEs in order to support them in meeting the challenges of Industry 4.0. The paper presents the readiness self-assessment method and roadmap model as a tools to secure a consistent implementation of technologies and devices supporting smart logistics and smart production. Proposed method has been applied by selected SMEs and it was proved that the model is easy to use in real production environment.

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References

  1. Kagermann, H., et al.: Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0: Deutschlands Zukunft als Produktionsstandort sichern; Abschlussbericht des Arbeitskreises Industrie 4.0. Forschungsunion (2013)

    Google Scholar 

  2. Modrak, V., Marton, D., Bednar, S.: Modeling and determining product variety for mass-customized manufacturing. Procedia CIRP 23, 258–263 (2014)

    Article  Google Scholar 

  3. Modrak, V., Soltysova, Z.: Novel complexity indicator of manufacturing process chains and its relations to indirect complexity indicators. Complexity 2017, 15 (2017)

    Article  MathSciNet  Google Scholar 

  4. Modrak, V., Soltysova, Z., Modrak, J., Behunova, A.: Reducing impact of negative complexity on sustainability of mass customization. Sustainability 9(11), 2014 (2017)

    Article  Google Scholar 

  5. Dima, I.C., Gabrara, J., Modrak, V., Piotr, P., Popescu, C.: Using the expert systems in the operational management of production. In: 11th WSEAS International Conference on Mathematics and Computers in Business and Economics, pp. 307–312 (2010)

    Google Scholar 

  6. UNITY Consulting and Innovation – via The Network Effect. https://www.slideshare.net/MarketingUNITY/supply-chain-management-71050225

  7. Leyh, C., Bley, K., Schäffer, T., Forstenhäusler, S.: SIMMI 4.0 - a maturity model for classifying the enterprise-wide it and software landscape focusing on Industry 4.0. In: Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, pp. 1297–1302 (2016)

    Google Scholar 

  8. Anderl, R.: Industrie 4.0–Digital transformation in product engineering and production. In: 21st International Seminar on High Technology-Smart Products and Smart Production. At Piracicaba (SP), Brazil (2016)

    Google Scholar 

  9. Agca, O., Gibson, J., Godsell, J., Ignatius, J., Wyn Davies, C., Xu, O.: An Industry 4 readiness assessment tool. WMG International Institute for Product and Service Innovation University of Warwick (2017). https://warwick.ac.uk/fac/sci/wmg/research/scip/industry4report/final_version_of_i4_report_for_use_on_websites.pdf

  10. Ibarra, D., Ganzarain, J., Igartua, J.I.: Business model innovation through Industry 4.0: a review. Procedia Manuf. 22, 4–10 (2018)

    Article  Google Scholar 

  11. Kans, M., Ingwald, A.: Business model development towards service management 4.0. Procedia CIRP 47, 489–494 (2016)

    Article  Google Scholar 

  12. Arias-Perez, J.E., Durango-Yepes, C.M.: Exploring knowledge management maturity from funcionalist and interpretivist perspectives. Entramado 11(1), 94–104 (2015)

    Article  Google Scholar 

  13. Geissbauer, R.; Vedso, J.; Schrauf, S.; Industry 4.0: Building the digital enterprise. Retrieved from PwC Website (2016). https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016.pdf

  14. Stock, T., Seliger, G.: Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP 40, 536–541 (2016)

    Article  Google Scholar 

  15. Ganzarain, J., Errasti, N.: Three stage maturity model in SME’s toward industry 4.0. J. Industr. Eng. Manag. 9(5), 1119 (2016)

    Article  Google Scholar 

  16. Ivanov, D., Sokolov, B., Ivanova, M.: Schedule coordination in cyber-physical supply networks Industry 4.0. IFAC-Papers Online 49(12), 839–844 (2016)

    Article  Google Scholar 

  17. Chromjakova, F.: Flexible man-man motivation performance management system for Industry 4.0. Int. J. Manag. Excell. 7(2), 829–840 (2016)

    Article  Google Scholar 

  18. Zhong, R.Y., et al.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5), 616–630 (2017)

    Article  Google Scholar 

  19. Singh, P.M., Van Sinderen, M.J., Wieringa, R.J.: Smart logistics: an enterprise architecture perspective. In: 29th CAiSE Conference CAiSE Forum, pp. 9–16 (2017)

    Google Scholar 

  20. Chavarría-Barrientos, D., et al.: A methodology to create a sensing, smart and sustainable manufacturing enterprise. Int. J. Prod. Res. 56(1-2), 584–603 (2018)

    Article  Google Scholar 

  21. Making Industry 4.0 Real – using the acatech I4.0 Maturity Index. https://www.infosys.com/engineering-services/white-papers/Documents/industry-4.0-real.pdf

  22. Hofmann, E., Rüsch, M.: Industry 4.0 and the current status as well as future prospects on logistics. Comput. Ind. 89, 23–34 (2017)

    Article  Google Scholar 

  23. Rauch, E., Dallasega, P., Matt, D.: Critical factors for introducing lean product development to small and medium sized enterprises in Italy. Procedia CIRP 60, 362–367 (2017)

    Article  Google Scholar 

  24. Cortina, J.M.: What is coefficient alpha? an examination of theory and applications. J. Appl. Psychol. 78(1), 98 (1993)

    Article  Google Scholar 

  25. Machin, D., Campbell, M.J., Walters, S.J.: Reliability and method comparison studies. In: Medical Statistics, 4th edn., 209 p. Wiley, England (2007)

    Google Scholar 

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Acknowledgement

This paper has been supported by the project with acronym SME 4.0 and titled as “SME 4.0 - Smart Manufacturing and Logistics for SMEs in an X-to-order and Mass Customization Environment” with funding received from the European Union’s Horizon 2020 research and innovation program under the H2020-EU.1.3.3, Project ID: 734713 and by VEGA project Nr. 1/0419/16 granted by the ME of the Slovak Republic.

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Correspondence to Vladimir Modrak .

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Modrak, V., Soltysova, Z., Poklemba, R. (2019). Mapping Requirements and Roadmap Definition for Introducing I 4.0 in SME Environment. In: Hloch, S., Klichová, D., Krolczyk, G., Chattopadhyaya, S., Ruppenthalová, L. (eds) Advances in Manufacturing Engineering and Materials. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-99353-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-99353-9_20

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