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Due to continuously changing conditions (Covid-19 pandemic, energy crisis) the companies search for ways to change their business model and structure in order to remain competitive by utilizing lean manufacturing philosophy and industry 4.0 technologies. The digitalization process is inextricably linked with the introduction of industry 4.0 technologies. In the industry 4.0 era, the systems collect information and data from multiple sensors and adjust the function of the production process without human intervention offering flexibility and automation. Digitalization entails the development of digital organization units with smart devices enabling human-to-machine communication emerging as a key factor. The aim of this paper is to present the framework and the successful strategy for the digital transition of industries combining lean manufacturing principles. To achieve this target, a survey was applied to identify the trends of businesses and investigate the relationship with lean production techniques. Additionally, a logistic regression model is developed based on the survey data. The present paper presents the crucial factors, the most significant obstacles to digital transformation and the most widespread industry 4.0 technology.

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References

  1. Ghobakhloo, M., & Fathi, M. (2019). Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management, 31(1), 1–30. https://doi.org/10.1108/jmtm-11-2018-0417.
     Google Scholar
  2. Hoellthaler, G., Braunreuther, S., & Reinhart, G, (2018). Digital lean production approach to identify potentials for the migration to a digitalized production system in SMEs from a lean perspective. 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering.
     Google Scholar
  3. Kasunic, M. (2005). Designing an Effective Survey. Handbook. Software Engineering Institute. Report No: CMU/SEI-2005-HB-004. DOI: 10.1184/R1/6573062.v1.
     Google Scholar
  4. Kalbande, V. N., Handa, C. C., & Bankar, A. W. (2018). Binary Logistics Regression Analysis to Assess Employability of Engineering Graduates in IT Sector. In Smart Technologies for Energy, Environment and Sustainable Development. Springer Singapore.
     Google Scholar
  5. Mayr, A., Weigelt, M., Kühl, A., Grimm, S., Erll, A., Potzel, M., & Franke, J. (2018). Lean 4.0 - A conceptual conjunction of lean management and Industry 4.0. Procedia CIRP, 72, 622–628. https://doi.org/10.1016/j.procir.2018.03.292.
     Google Scholar
  6. Ponis, S. T. (2020). Research Design & Approaches. School of Mechanical Engineering, National Technical University of Athens.
     Google Scholar
  7. Suthar, V., Tarmizi, R. A., Midi, H., & Adam, M. B. (2010). Students’ Beliefs on Mathematics and Achievement of University Students: Logistics Regression Analysis. Procedia - Social and Behavioral Sciences, 8, 525–531. https://doi.org/10.1016/j.sbspro.2010.12.072.
     Google Scholar
  8. Schumacher, S., Bildstein, A., & Bauernhansl, T. (2020). The Impact of the Digital Transformation on Lean Production Systems. Procedia CIRP, 93, 783–788. https://doi.org/10.1016/j.procir.2020.03.066.
     Google Scholar
  9. Tortorella, G. L., & Fettermann, D. (2017). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975–2987. https://doi.org/10.1080/00207543.2017.1391420.
     Google Scholar