SYSTEM OF PREVENTIVE АCTION OF CONSTRUCTION ENTERPRISES ON THE BASIS OF IDENTIFICATION OF ANTICRISIS POTENTIAL

Authors

DOI:

https://doi.org/10.37943/AITU.2020.53.13.002

Keywords:

economic security, anti-crisis potential, digitalization, financial indicators, construction enterprise

Abstract

Peculiarities of formation of anti-crisis potential of construction enterprises are considered. Construction companies are rapidly adapting to the requirements of the digital economy, transforming the management structure, business processes. To improve the system of preventive protection and protection of enterprises from loss of viability and subsequent self-liquidation or bankruptcy, a system of indicators is proposed, which allows to identify existing risks and threats at an early stage. In order to improve the mechanism of control of the stability of the system of anti-crisis potential of construction enterprises in the medium term, a cluster analysis was performed. The study was based on 53 enterprises of the type of activity «construction». This study allowed us to identify the most important, priority, leading indicators of the loss of economic security and to clarify the threshold values of these indicators and the degree of their «blurring» in the unstable conditions of the external economic environment. Indicators of crisis state of construction enterprises are determined by means of fuzzy sets, among which it is possible to allocate: level of capital consumption by owners, level of operating sales on retained earnings, return on working capital on retained earnings, cost of
operating expenses on personnel costs, term of accounts payable. The main direct and indirect signs of deterioration of the anti-crisis potential of the enterprise are revealed. The model of information interaction of divisions of the enterprise is offered. All processes of information exchange with the help of IMS (Information Management System) have the ultimate goal of the maximum possible exclusion from the business practice of paper documents and the transition to direct paperless data exchange (in the practice of construction is an example of creating a BIM-model of objects).

Author Biographies

O. Bielienkova, Kyiv National University of Construction and Architecture

Candidate of Economic Sciences, Associate Professor of the Department of Construction Economics

S. Stetsenko, Kyiv National University of Construction and Architecture

Doctor of Economics, Associate Professor of the Department of Construction Economics

L. Sorokina, Kyiv National University of Construction and Architecture

Doctor of Economics, Professor of the Department of Construction Economics

O. Molodid, Kyiv National University of Construction and Architecture

Candidate of Economic Sciences, Senior researcher

N. Bolila, Kyiv National University of Construction and Architecture

Senior Lecturer of the Department of Construction Economics

References

Zeltser, R., Bielienkova, O., Novak, Ye. and Dubinin, D. (2019). Digital Transformation of Resource Logistics and Organizational and Structural Support of Construction. Nauka i innovatsii, vol. 15(5), 38-51

Tugay, O.A., Zeltser, R.Ya., Kolot, M.A., Panasiuk, I.O. (2019). Organization of Supervision over Construction Works Using Uavs and Special Software. Nauka i innovatsii, vol. 15(4), 23-32

Tugay, O.A., Shebek, M.O., Dubynka, O.V. 2019). Identifying New and Structuring Existing Organizational and Technological Approaches to Managing the Cycle of Engineering Preparation for a Construction and Investment Project. Nauka innov. 15(2), 105-114

Abidali, A.F., & Harris, F. (1995). A methodology for predicting company failure in the construction industry. Construction Management and Economics, vol. 13(3), 189-196. doi:10.1080/01446199500000023

Chan, J. K. W., Tam, C. M., & Cheung, R. K. C. (2005). Construction firms at the crossroads in hong kong: Going insolvency or seeking opportunity. Engineering, Construction and Architectural Management, vol. 12(2), 111-124. doi: 10.1108/09699980510584476

Edum-Fotwe, F., Price, A., & Thorpe, A. (1996). A review of financial ratio tools for predicting contractor insolvency. Construction Management and Economics, vol. 14(3), 189-198. doi:10.1080/014461996373458

Kangari, R., Farid, F., & Elgharib, H.M. (1992). Financial performance analysis for construction industry. Journal of Construction Engineering and Management, vol. 118(2), 349-361. doi: 10.1061/(ASCE)0733-9364(1992)118:2(349)

Mason, R. J., & Harris, F. C. (1979). Predicting company failure in the construction industry. Proceedings Institution of Civil Engineers, vol. 66(2), 301-307.

Tserng, H., Lin, G., Tsai, L., & Chen, P. (2011). An enforced support vector machine model for construction contractor default prediction. Automation in Construction, 20(8), 1242-1249. doi:10.1016/j.autcon.2011.05.007

Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, vol. 10(1), 167-179.

Edison, H.J. (2003). Do indicators of financial crises work? an evaluation of an early warning system. International Journal of Finance and Economics, vol. 8(1), 11-53. doi: 10.1002/ijfe.197

Karas, M., & Režňáková, M. (2017). The stability of bankruptcy predictors in the construction and manufacturing industries at various times before bankruptcy. E a M: Ekonomie a Management, 20(2), 116-133. doi: 10.15240/tul/001/2017-2-009

Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094-15102. doi: 10.1016/j.eswa.2011.05.035

Spicka, J. (2013). The financial condition of the construction companies before bankruptcy. European Journal of Business and Management, vol. 5(23), 160-169.

Thomas Ng, S., Wong, J. M. W., & Zhang, J. (2011). Applying Z-score model to distinguish insolvent construction companies in China. Habitat International, vol. 35(4), 599-607. doi: 10.1016/j.habitatint.2011.03.008

Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking and Finance, vol. 52, pp. 89-100.

Tseng, F., & Hu, Y. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, vol. 37(3), 1846-1853. doi:10.1016/j.eswa.2009.07.081

Wang, Y., & Lee, H. (2008). A clustering method to identify representative financial ratios. Information Sciences, vol. 178(4), 1087-1097. doi: 10.1016/j.ins.2007.09.016

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, vol. 22(SUPPL.), 59-82. Retrieved from www.scopus.com

Stetsenko, S.P., Tytok, V.V., Emelianova, O.M., Bielienkova, O. Yu and Tsyfra T.Yu. (2020). Management of Adaptation of Organizational and Economic Mechanisms of Construction to Increasing Impact of Digital Technologies on the National Economy. Journal of Reviews on Global Economic. no. 9, 149-164.

Zvarikova, K., Spuchlakova, E., & Sopkova, G. (2017). International comparison of the relevant variables in the chosen bankruptcy models used in the risk management. Oeconomia Copernicana, vol. 8(1), 145-157. doi: 10.24136/oc.v8i1.10

Rutkovskaya, D., Pilins’kij, & M.,Rutkovskij, L. (2007). Nejronnye seti, geneticheskie algoritmy I nechetkie sistemy. M.: Goryachaya liniya – Telekom.

Tugai O.A., Hryhorovskyi P.Ye., Khyzhniak V.O., Stetsenko S.P., Bielienkova O.Yu., Molodid О.S., Chernyshev D.O (2019). Organizational and technological, economic quality control aspects in the construction industry: collective monograph – Lviv-Toruń: Liha-Pres.

Baležentis, T., & Zeng, S. (2013). Group multi-criteria decision making based upon interval-valued fuzzy numbers: An extension of the MULTIMOORA method. Expert Systems with Applications, vol. 40(2), 543-550. doi: 10.1016/j.eswa.2012.07.066

Downloads

Published

2020-09-30

How to Cite

Bielienkova, O., Stetsenko, S., Sorokina, L., Molodid, O., & Bolila, N. (2020). SYSTEM OF PREVENTIVE АCTION OF CONSTRUCTION ENTERPRISES ON THE BASIS OF IDENTIFICATION OF ANTICRISIS POTENTIAL. Scientific Journal of Astana IT University, 3(3), 15–27. https://doi.org/10.37943/AITU.2020.53.13.002

Issue

Section

Articles
betpas
maltepe escort ataşehir escort ataşehir escort ümraniye escort pendik escort kurtköy escort anadolu yakası escort üsküdar escort şerifali escort kartal escort gebze escort kadıköy escort bostancı escort göztepe escort kadıköy escort bostancı escort üsküdar escort ataşehir escort maltepe escort kurtköy escort anadolu yakası escort ataşehir escort beylikdüzü escort