Науковий вісник НГУ

Clustering as a tool for managing industrial enterprise

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Contens №3 2020

Last Updated on 13 September 2020

Published on 02 July 2020

Hits: 3596

Authors:

M. I. Ivanova, Dr. Sc. (Econ.), Assoc. Prof., Professor of the Department of Management, orcid.org/0000-0002-1130-0186, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.O.Faizova, Cand. Sc. (Econ.), Assoc. Prof., Senior Lecturer of the Department of Administration, Management and Entrepreneurship, orcid.org/0000-0002-7243-0726, National Metallurgical Academy of Ukraine, Dnipro, Ukraine

M. V. Boichenko, Dr. Sc. (Econ.), Assoc. Prof., Professor Department of Management, orcid.org/0000-0002-9874-3085, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O. K. Balalaiev, Cand. Sc. (Biol.), Senior Research Fellow, orcid.org/0000-0002-9389-4562, M. S. Polyakov Institute of Geotechnical Mechanics of NASU, Dnipro, Ukraine

V. L. Smiesova, Cand. Sc. (Econ.), Assoc. Prof., Senior Lecturer of the Department of Entrepreneurship, Organization of Production, Theoretical and Applied Economics, orcid.org/0000-0002-0444-4659, State Higher Educational Institution “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2020, (3): 96-102

https://doi.org/10.33271/nvngu/2020-3/096

повний текст / full article

Abstract:

Purpose. Substantiation of methodological foundations for forming a cluster of industrial enterprises and establishing a system of relationships between their cluster groups.

Methodology. Specific analogue modelling techniques were used to identify the relations between manufacturing enterprises; economic and mathematical modelling was used to search for multilayered network communities.

Findings. A fundamentally new methodological basis has been proposed for identifying a cluster of industrial enterprises. This has been done through a comparison of the results of the three approaches to clustering. It has been revealed that hierarchical cluster analysis does not allow identifying similar groups of enterprises or relationships between them, since this approach lacks a single strict criterion for an optimal split of the dendrogram into clusters. The competitive approach of the geometric distance of neurons to objects, which is based on the technique of self-learning neural network and Kohonen’s self-organizing maps, also identified a non-uniform cluster structure. The study proposes to form a cluster of industrial enterprises through the method of searching for communities in multilayered network graphs. This method was a breakthrough in building a cluster as a merger of extractive and processing industrial enterprises, together with academic and research institutions.

Originality. A new methodological approach to the formation of industrial enterprise cluster has been proposed, whose mathematical basis was developed by T. Kamada. This approach uses multiple object proximity matrices, which take into account supplier-consumer relationships, geographical distances, and patterns of ownership. It has been proved that this method for clustering is more advantageous, since it allows identifying the communities of enterprises, which are network analogues to a cluster; it also takes into account the relationships of the analysed metallurgical enterprises of the mining and processing industry with educational and research institutions of the enterprises. The development of these relationships creates the basis for the productive development, efficient operation and additional competitive advantages for industrial enterprises.

Practical value. Under conditions of a crisis in the metallurgical industry, it is recommended to create a cluster, which will significantly increase the competitiveness of each enterprise included in the cluster and fully use the potential of the metallurgical complex.

References.

1. Smiesova, V. L., & M’iachyn, V. H. (2018). Neural network approach to clustering countries by iIndicators characterizing the processes of formation and implementation of economic interests. Naukovyi Visnyk Uzhhorodskoho Natsionalnoho Universytetu. Seriia: Mizhnarodni ekonomichni vidnosyny ta svitove hospodarstvo, 19(3), 49-57.

2. Glinskiy, V., Serga, L., Chemezova, E., & Zaykov, K. (2016). Clusterization Economy as a Way to Build Sustainable Development of the Region. Procedia CIRP, 40, 324-328. https://doi.org/10.1016/j.procir.2016.01.050.

3. Babkin, A., Kudryavtseva, T., & Utkina, S. (2013). Formation of Industrial Clusters Using Method of Virtual Enterprises. Procedia Economics and Finance, 5, 68-72. https://doi.org/10.1016/S2212-5671(13)00011-7.

4. Jucevicius, G., & Grumadaite, K. (2015). Patterns for Cluster Emergence in Latecomer Economies. Procedia-Social and Behavioral Sciences, 213, 198-203. https://doi.org/10.1016/j.sbspro.2015.11.426.

5. Luyi, C., Yuan, Z., Dillon, Z., & Lan, X. (2017). Clustering enterprises into eco-industrial parks: Can interfirm alliances help small and medium-sized enterprises? Journal of Cleaner Production, 168, 1070-1079. https://doi.org/10.1016/j.jclepro.2017.09.104.

6. Baldassarre, B., Schepers, M., Bocken, N., Cuppen, E., & Calabretta, G. (2019). Industrial Symbiosis: towards a design process for eco-industrial clusters by integrating Circular Economy and Industrial Ecology perspectives. Journal of Cleaner Production, 216, 446-460. https://doi.org/10.1016/j.jclepro.2019.01.091.

7. Kayvanfar, V., Moattar Husseini, S.M., Sajadieh Moh­sen, S., & Karimi, B. (2018). A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters. Computers & Industrial Engineering, 8(115), 69-79. https://doi.org/10.1016/j.cie.2017.11.003.

8. Ivanova, M. I. (2017). Formalization of performance indicators in industrial logistics clusters. Visnyk Natsionalnoho Universytetu “Lvivska politekhnika”. Seriia: Menedzhment ta pidpryiemnytstvo v Ukraini: etapy stanovlennia i problemy rozvytku. (Pidpryiemnytstvo), 875, 147-152.

9. Pjatak, I. V. (2015). Enterprise clustering as a factor of innovative and integration processes of regional economy development. Visnyk Berdianskoho Universytetu Menedzhmentu i Biznesu, 1, 119-122.

10. Kohonen, T. (2013). Essentials of the self-organizing map. Neural Networks, 37, 52-65. https://doi.org/10.1016/j.neunet.2012.09.018.

11. Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1-44. https://doi.org/0.1016/j.physrep.2016.09.002.

12. Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203-271. https://doi.org/10.1093/comnet/cnu016.

13. Stock market infrastructure development agency of Ukraine (n.d). Retrieved from: http://www.smida.gov.ua/.

14. Manukyan, N., Eppstein, M. J., & Rizzo, D. M. (2012). Data-driven cluster reinforcement and visualization in sparsely-matched self-organizing maps. Neural Networks and learning Systems, IEEE Transactions on, 23(5), 846-852. https://doi.org/10.1109/TNNLS.2012.2190768.

• < Prev
• Next >