Clustering Entrepreneurial and Innovative Universities in Turkey According to Their Relevance to Industry 4.0

Yıl 2020, Cilt: 24 Sayı: 6, 1171 - 1184, 01.12.2020

Türkay Dereli , Özge Var , Alptekin Durmuşoğlu

https://doi.org/10.16984/saufenbilder.682459

Cited By: 1

Öz

Industry 4.0 differentiates production and business models through connecting embedded system, production technologies, and smart production processes. Preparing the young generation to this change is a challenge for higher education. In this study, the adaptation of the entrepreneurial and innovative universities in Turkey to Industry 4.0 is linked with their relevance level to Industry 4.0. To represent the relevance level of universities, a cluster analysis is put forward. Three criteria, named as the number of academic publications related to Industry 4.0, the number of physical structures that facilitate the adaptation to Industry 4.0, and the number of events organized by the universities within the Industry 4.0 concept, were selected for the clustering analysis. To access the number of academic publications of universities, ISI Web of Science database was used. “Industry 4.0” and its components were used as keywords. Both the websites of the each university and Google search were used to access the values of remaining two criteria. The obtained data were used for K-means clustering analysis. The optimal number of cluster was determined as five with the elbow method. It is thought that the results of the study could be used as an indicator for universities to determine their Industry 4.0 road maps.

Anahtar Kelimeler

Industry 4.0, Elbow Method, K-Means Algorithm

Kaynakça

• T. Dereli, Ö. Var and A. Durmuşoğlu, “Clustering universities in Turkey according to their relevance to Industry 4.0,” in 10th International Symposium on Intelligent Manufacturing and Service Systems, pp.1147-1153, 2019.
• A. Yıldız, “Endüstri 4.0 ve akıllı fabrikalar,” Sakarya University Journal of Science, vol. 22, 2, pp.546-556, 2018.
• I. Janis and M. Alias, “A systematic literature review: Human roles, competencies and skills in industry 4.0,” in Asia International Multidisciplinary Conference (AIMC), pp. 1052-1072, 2017.
• J. Dostal and X. J. Wang, “Digital identity- significant topic to be included in curriculum content in primary schools,” in 10th International Conference of Education, Research and Innovation, pp. 3808-3813, 2017.
• M. Baygin, H. Yetis, M. Karakose, and E. Akin, “An effect analysis of industry 4.0 to higher education,” in 15th International Conference on Information Technology Based Higher Education and Training (ITHET), 2016.
• N. L. Carutasu and G. Carutasu, “Replicating enterprise environment using Office 365 to enhance graduates' employability,” in 8th International Conference on Manufacturing Science and Education– Trends in New Industrial Revolution, pp. 1-8., 2017.
• K. Lensing and J. Friedhoff, “Designing a curriculum for the Internet-of-Things-Laboratory to foster creativity and a maker mindset within varying target groups,” in 8th CIRP Sponsored Conference on Learning Factories -Advanced Engineering Education & Training for Manufacturing Innovation, pp. 231-236, 2018.
• S. Coşkun, Y. Kayıkcı, and E. Gençay, “Adapting engineering education to industry 4.0 vision,” Technologies. vol. 7, 2019.
• TUBITAK (2019, Aug 15). Entrepreneurial and innovative universities index 2018 [Online]. Available:https://www.tubitak.gov.tr/sites/default/files/289/gyue_ilk50.pdf.
• M. R. Cesur, O. Torkul, İ. H. Cedimoğlu, and S. Uçar, “Intelligent campus implementation for smart cities,” Sakarya University Journal of Science, vol. 23, 6, pp.1218-1224, 2019.
• K. Kaur, D. S. Dhaliwal, and R. K. Vohra, “Statistically refining the initial points for K-Means clustering algorithm,” International Journal of Advanced Research in Computer Engineering & Technology, vol. 2, pp. 29972-2977, 2013.
• P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 1st ed. Boston: Pearson- Addison Wesley, 2006.
• S. B. Kiriş and F. Tüysüz, “Performance comparison of different clustering methods for manufacturing cell formation,” Sakarya University Journal of Science, vol. 21, 5, pp. 1031-1044, 2017.
• S. Solak and U. Altunışık, “Görüntü işleme teknikleri ve kümeleme yöntemleri kullanılarak fındık meyvesinin tespit ve sınıflandırılması,” Sakarya University Journal of Science, vol. 22, 1, pp. 56-65, 2018.
• U. Altınışık and M. Yıldırım, “Veri madenciliği kümeleme algoritmaları kullanarak arıza dayanımlı denetim sistemi tasarımı,” Sakarya University Journal of Science, vol. 17, 1, pp. 9-16, 2013.
• J. Zeng, J. Wang, L. Guo, G. Fan, K. Zhang, and G. Gui, “Cell scene division and visualization based on auto encoder and K-means algorithm,” IEEE Access, vol. 7, pp. 165217-165225, 2019.