ESTONIAN ACADEMY
PUBLISHERS
eesti teaduste
akadeemia kirjastus
PUBLISHED
SINCE 1952
 
Proceeding cover
proceedings
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2022): 0.9
Industry 4.0 readiness for the manufacturing sector in the Baltic Sea Region; pp. 453–460
PDF | 10.3176/proc.2021.4.12

Authors
Sara M. Bazaz, Sakari Penttilä, Mikael Ollikainen, Juho Ratava, Juha Varis
Abstract

This study provides data and analysis on the state of digitalization of the manufacturing industries in the Baltic Sea Region. The compiled matrix contains entities on seven fields of operation and lists ten service and technology areas mapped from Denmark, Estonia, Finland, Latvia, Lithuania and Poland. A comparison of the collected data estimates the level of digitalization as well as identifies potential strengths and weaknesses by region. The available support structure is analysed to find potentially suitable partners to improve the level of digitalization in the enterprises of a region. Furthermore, the strength areas of each country are compared to weak areas to develop a roadmap to improve the readiness for implementation and use of Industry 4.0 functionality and tools. Examples are provided on the generation of a Business­to­Business (B2B) focused platform to improve the level of technologies and services in use, an example is also presented to identify and implement a use case based on an identified need.

References

1. Mahmood, K., Karaulova, T., Otto, T. and Shevtshenko, E. Development of cyber-physical production systems based on modelling technologies. Proc. Est. Acad. Sci., 2019, 68(4), 348–355.
https://doi.org/10.3176/proc.2019.4.02

2. Kangru, T., Riives, J., Mahmood, K. and Otto, T. Suitability analysis of using industrial robots in manufacturing. Proc. Est. Acad. Sci., 2019, 68(4), 383–388.
https://doi.org/10.3176/proc.2019.4.06

3. Qi, Q. and Tao, F. Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access, 2018, 6, 3585–3593.
https://doi.org/10.1109/ACCESS.2018.2793265

4. Lõun, K., Riives, J. and Otto, T. Evaluation of the operation expedience of technological resources in a manufacturing network. Est. J. Eng., 2011, 17(1), 51–65.
https://doi.org/10.3176/eng.2011.1.06

5. Riives, J., Karjust, K., Küttner, R., Lemmik, R., Koov, K. and Lavin, J. Software development platform for integrated manufacturing engineering system. In Proceedings of the 8th International DAAAM Baltic Conference “Industrial Engineering”, Tallinn, Estonia, April 19–21, 2012. Tallinn University of Technology, 2012, 555–560. 

6. Kaganski, S., Majak, J., Karjust, K. and Toompalu, S. Implementation of key performance indicators selection model as part of the Enterprise Analysis Model. Procedia CIRP, 2017, 63, 283–288. 
https://doi.org/10.1016/j.procir.2017.03.143

7. Kolberg, D. and Zühlke, D. Lean automation enabled by Industry 4.0 technologies. IFAC-PapersOnLine, 2015, 48(3), 1870–1875.
https://doi.org/10.1016/j.ifacol.2015.06.359

8. De Carolis, A., Macchi, M., Negri, E. and Terzi, S. Guiding manufacturing companies towards digitalization a methodology for supporting manufacturing companies in defining their digitalization roadmap. In Proceedings of the 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), Madeira, Portugal, June 27–29, 2017. IEEE, 487–495. 
https://doi.org/10.1109/ICE.2017.8279925

9. Bazaz, S. M., Penttilä, S., Ratava, J., Ollikainen, M. and Varis, J. SMEs’ support functionality analysis based on statistical analysis. Procedia Manuf., 2020, 51, 960–966.
https://doi.org/10.1016/j.promfg.2020.10.135

10. Roblek, V., Meško, M. and Krapež, A. A complex view of Industry 4.0. SAGE Open, 2016, 6(2). 
https://doi.org/10.1177/2158244016653987

11. Carré, H. Statistical classification of economic activities in the European Community. Publications Office of the European Union, Luxembourg, 2008.

Back to Issue