Skip to main content
Springer Nature Link
Log in

Exploring Potential Barriers for the Adoption of Cognitive Technologies in Industrial Manufacturing SMEs – Preliminary Results of a Qualitative Study

  • Conference paper
  • First Online:
Subject-Oriented Business Process Management. Models for Designing Digital Transformations (S-BPM ONE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1867))

Abstract

While small and medium-sized enterprises (SMEs) make up 99% of registered companies in Germany, only a fraction of them is engaged in Internet of Things (IoT) and Artificial Intelligence (AI) as part of their Industry 4.0 initiatives. Despite the potential of IoT and AI, the prerequisites to use these technologies may not be met by SMEs, or the benefits expected may not be aligned with their needs. This research paper identifies typical characteristics of SMEs in the manufacturing sector through a literature review. In addition, we conducted a brainwriting workshop and discussed the findings among interdisciplinary researchers. Our qualitative research approach revealed 19 distinct barriers classified into three key dimensions. Our findings can assist technology managers and production departments in evaluating their organizations and addressing the identified adoption barriers. Additionally, the results can be used in further research to set up practice-oriented guidelines that support the holistic adoption of IoT and AI in manufacturing SMEs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zeba, G., et al.: Technology mining: Artificial intelligence in manufacturing. Technological Forecasting and Social Change, vol. 171 (2021). https://doi.org/10.1016/j.techfore.2021.120971

  2. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014). https://doi.org/10.1007/s12599-014-0334-4

    Article  Google Scholar 

  3. Elia, G., Margherita, A.: A conceptual framework for the cognitive enterprise: pillars, maturity, value drivers. Technology Analysis & Strategic Management, vol. 34 (2022). https://doi.org/10.1080/09537325.2021.1901874

  4. Hollowell, C., Kollar, B., Vrbka, J., Kovalova, E.: Cognitive Decision-Making Algorithms for Sustainable Manufacturing Processes in Industry 4.0: Networked, Smart, and Responsive Devices. Econ. Manage., Financial Markets, 14(4), 9–15 (2019). https://doi.org/10.22381/EMFM14420191

  5. Ghosh, A., Chakraborty, D., Law, A.: Artificial Intelligence in Internet of Things. CAAI Trans. Intell. Technol., 3(4), (2018). https://doi.org/10.1049/trit.2018.1008

  6. European Commission: 2022 SME Country Fact Sheet Germany (2022). https://ec.europa.eu/docsroom/documents/50688

  7. Bauer, W., et al.: Künstliche Intelligenz in der Unternehmenspraxis. Studie zu Auswirkungen auf Dienstleistung und Produktion. Fraunhofer Verlag, Stuttgart (2019)

    Google Scholar 

  8. Bischoff, J., et al.: Erschließen der Potenziale der Anwendung von Industrie 4.0 im Mittelstand. agiplan, Mülheim an der Ruhr (2015)

    Google Scholar 

  9. Türkeș, M., et al.: Drivers and Barriers in Using Industry 4.0: A Perspective of SMEs in Romania. Processes, 7(3), 153 (2019). https://doi.org/10.3390/pr7030153

  10. 1Ulrich, P., Frank, V.: Relevance and Adoption of AI technologies in German SMEs – Results from Survey-Based Research. Procedia Computer Science, vol. 192, 2152–2159 (2021). https://doi.org/10.1016/j.procs.2021.08.228

  11. Kuldeep, B., Anuj, K., Arya, K., Purvi, P.: A Study of Barriers and benefits of Artificial Intelligence Adoption. In: Small and Medium Enterprise. Academy of Marketing Studies Journal, vol. 26 (2022)

    Google Scholar 

  12. Ghobakhloo, M., et al.: Drivers and barriers of Industry 4.0 technology adoption among manufacturing SMEs: a systematic review and transformation roadmap. JMTM, vol. 33(4) (2022). https://doi.org/10.1108/JMTM-12-2021-0505

  13. Mayring, P.: Qualitative Content Analysis: Demarcation, Varieties, Developments. Qualitative Content Analysis, vol. 20 (2019). https://doi.org/10.17169/FQS-20.3.3343

  14. Wilson, C.: Brainstorming and Beyond. A User-Centered Design Method. Elsevier Science, Amsterdam (2013)

    Google Scholar 

  15. Guenzi, P., Storbacka, K.: The organizational implications of implementing key account management: A case-based examination. Indust. Market. Manage. 45, 84–97 (2015). https://doi.org/10.1016/j.indmarman.2015.02.020

  16. Ihlau, S., Duscha, H.: Besonderheiten bei der Bewertung von KMU. Planungsplausibilisierung, Steuern, Kapitalisierung. Springer, Wiesbaden (2019). https://doi.org/10.1007/978-3-658-18675-3

  17. Wiesner, S., Gaiardelli, P., Gritti, N., Oberti, G.: Maturity Models for Digitalization in Manufacturing - Applicability for SMEs. In: Moon, I., Lee, G.M., Park, J., Kiritsis, D., von Cieminski, G. (eds.) APMS 2018. IAICT, vol. 536, pp. 81–88. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99707-0_11

    Chapter  Google Scholar 

  18. Peschke, F., Eckardt, C.: Flexible Produktion durch Digitalisierung. Entwicklung von UseCases. Carl Hanser Verlag, München (2019)

    Book  Google Scholar 

  19. Ludwig, T., et al.: HMD Praxis der Wirtschaftsinformatik 53(1), 71–86 (2015). https://doi.org/10.1365/s40702-015-0200-y

    Article  Google Scholar 

  20. Steinmüller, K.: Methoden der Zukunftsforschung – Langfristorientierung als Ausgangspunkt für das Technologie-Roadmapping. In: Möhrle, M.G., Isenmann, R. (eds.) Technologie-Roadmapping. V, pp. 29–46. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-52709-2_3

    Chapter  Google Scholar 

  21. Sames, G., Diener, A.: Stand der Digitalisierung von Geschäftsprozessen zu Industrie 4.0 im Mittelstand. Technische Hochschule Mittelhessen, Gießen (2018)

    Google Scholar 

  22. Mittal, S., Khan, M.A., Romero, D., Wuest, T.: A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). J. Manufact. Syst. 49, 194–214 (2018). https://doi.org/10.1016/j.jmsy.2018.10.005

  23. Dickmann, P.: Schlanker Materialfluss mit Lean. Kanban und Innovationen. Springer, Berlin (2015)

    Book  Google Scholar 

  24. Schebek L.: Ressourceneffizienz durch Industrie 4.0. VDI Zentrum Ressourceneffizienz, Berlin (2017)

    Google Scholar 

  25. Teichmann, M., Ullrich, A., Wenz, J., Gronau, N.: HMD Praxis der Wirtschaftsinformatik 57(3), 512–527 (2020). https://doi.org/10.1365/s40702-020-00614-x

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technical University of Applied Sciences Amberg-Weiden, Hetzenrichter Weg 15, 92637, Weiden, Germany

    Thomas Auer, Stefan Rösl & Christian Schieder

Authors
  1. Thomas Auer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Rösl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Schieder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Auer .

Editor information

Editors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Matthes Elstermann

  2. University of Rostock, Rostock, Germany

    Anke Dittmar

  3. Technical University of Applied Sciences, Weiden, Germany

    Matthias Lederer

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Auer, T., Rösl, S., Schieder, C. (2023). Exploring Potential Barriers for the Adoption of Cognitive Technologies in Industrial Manufacturing SMEs – Preliminary Results of a Qualitative Study. In: Elstermann, M., Dittmar, A., Lederer, M. (eds) Subject-Oriented Business Process Management. Models for Designing Digital Transformations. S-BPM ONE 2023. Communications in Computer and Information Science, vol 1867. Springer, Cham. https://doi.org/10.1007/978-3-031-40213-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-40213-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40212-8

  • Online ISBN: 978-3-031-40213-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics