Industry 4.0 Operationalization Based on an Integrated Framework of Industrial Digitalization and Automation

  • Andreas SchumacherEmail author
  • Christian Schumacher
  • Wilfried Sihn
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The utilization of Digitalization and Automation (DA) is currently one of the determining factors of increasing prosperity, productivity, and efficiency. Although widely promoted and incentivized by policy- and decision makers, industrial enterprises seem reluctant to actively push DA-development in their organizations. From a scientific point of view, we find missing operationalization resulting in a lack of analysis and understanding of basic DA-elements in real production environments as the main barrier towards Industry 4.0- and DA-implementation. Thus, in this paper we introduce a novel approach to transfer abstract Industry 4.0-concepts into industrial environments through the utilization of basic concepts of industrial digitalization and automation. Based on this framework we develop a model to assess the degree of digitalization and automation of value creation factors and organizational factors (DAVO). Knowledge about the company’s DAVO-degree enables their decision makers to derive operational Industry 4.0 target-states, define strategies thus decide targeted investments into organizational and technological developments. We applied the developed and operationalized measurement-metric and model in an empirical study in the Austrian industry assessing the DAVO-degree of 200 industrial companies. Besides insights into their maturity status, we find clear evidence in our study for the need of operationalized Industry 4.0-concepts. Furthermore, the DAVO-approach seems to encourage practitioners to reflect their organization’s DA-status with more accuracy thus enables them to derive more targeted and sustainable strategic steps.


Industry 4.0 Digitalization Automation Value creation processes Empirical studies 



Research and results presented in this paper were fully enabled by funding from the Austria Research Promotion Agency (FFG)/grand no. 872637.


  1. 1.
    Schumacher, A., Nemeth, T., Sihn, W.: Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises. In: Proceedings 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME, pp. 409–414. Gulf of Naples (2018)Google Scholar
  2. 2.
    Schumacher, A., Erol, S., Sihn, W.: A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. In: Proceedings 6th CARV International Conference on Changeable, Agile, Reconfigurable and Virtual Production, pp. 161–166. Bath (2016)Google Scholar
  3. 3.
  4. 4.
    Berghaus, S., Back, A.: Gestaltungsbereiche der digitalen transformation von unternehmen: entwicklung eines reifegrademodells. Die Unternehmung 70, 98–123 (2016)CrossRefGoogle Scholar
  5. 5.
    Åsa, F., Stahre, J., Dencker, K.: Level of automation analysis in manufacturing systems. In: Karwowski, W., Salvendy, G. (eds.) Advances in Human Factors, Ergonomics, and Safety in Manufacturing and Service Industries, pp. 233–242. CRC Press (2016)Google Scholar
  6. 6.
    Kotarba, M.: Measuring digitalization – key metrics. Found. Manag. 9, 123–138 (2015)CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
    Boston Consulting Group (2018) Boston Consulting Group homepage.
  10. 10.
    PricewaterhouseCoopers (2018) Pricewaterhouse Coopers homepage.
  11. 11.
    Accenture website (2016).
  12. 12.
    Fraunhofer ISI (2018) Fraunhofer ISI homepage.
  13. 13.
    Schumacher, A., Erol, S., Sihn, W.: Automation, digitization and digitalization and their implications for manufacturing process. In: Proceedings International Scientific Conference “Innovation and Sustainability”, Bucharest (2016)Google Scholar
  14. 14.
    Ganzarain, J., Nekane, E.: Three stage maturity model in SME’s toward industry 4.0. J. Ind. Eng. Manag. 9, 1119–1128 (2016)Google Scholar
  15. 15.
    Jodlbauer, H., Schagerl, M.: Reifegradmodell industrie 4.0 - ein vorgehensmodell zur identifikation von industrie 4.0 potentialen. In: Proceedings Informatik, pp. 1473–1487. Bonn (2016)Google Scholar
  16. 16.
    Klötzer, C., Pflaum, A.: Toward the development of a maturity model for digitalization within the manufacturing industry‘s supply chain. In: Proceedings 50th HICSS Hawaii International Conference on System Sciences, Waikoloa Village (2017)Google Scholar
  17. 17.
    de Carolis, A., Macchi, M., Negri, E., Terzi, S.: A maturity model for assessing the digital readiness of manufacturing companies. In: Lödding, H., Riedel, R., Thoben, K.D. (eds.) Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing, pp. 13–20. Springer Verlag, Heidelberg (2017). Scholar
  18. 18.
    Rößler, M.R., Haschemi, M.: Smart factory assessment (SFA): eine methodik zur integralen reifegradbewertung von produktion und logistik hinsichtlich lean und industrie 4.0. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 112, 699–703 (2017)CrossRefGoogle Scholar
  19. 19.
    Leyh, et al.: Assessing the IT and software landscapes of industry 4.0-enterprises: the maturity model SIMMI 4.0. Bus. Inf. Process. 277, 103–119 (2017)Google Scholar
  20. 20.
    Schott, P., et al.: A maturity model to organize the multidimensionality of digitalization in smart factories. In: Yilmaz, Ö.F., Tüfekçí, S. (eds.) Applied Optimization Methodologies in Manufacturing Systems, pp. 20–42. IGI Global, Nürnberg (2017)Google Scholar
  21. 21.
    Issa, A., Hatiboglu, B., Bildstein, A., Bauernhansl, T.: Industrie 4.0 roadmap: framework for digital transformation based on the concepts of capability maturity and alignment. In: Proceedings 51th CIRP Conference on Manufacturing Systems, pp. 973–978, Stockholm (2018)Google Scholar
  22. 22.
    Rajnai, Z., Kocsis, I.: Assessing industry 4.0 readiness of enterprises. In: Proceedings 16th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 225–230, Kosice (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Andreas Schumacher
    • 1
    • 2
    Email author
  • Christian Schumacher
    • 3
  • Wilfried Sihn
    • 1
    • 2
  1. 1.University of Technology ViennaViennaAustria
  2. 2.Fraunhofer Austria Research GmbHViennaAustria
  3. 3.University of Economics ViennaViennaAustria

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