SYSTEMS ANALYSIS OF THE RESOURCE PROVISION OF AN INDUSTRIAL ENTERPRISE BASED ON MATHEMATICAL MODELING

V. G. Mokhov, T. S. Demyanenko, D. V. Smagin

Abstract


In today's highly competitive and economically volatile environment, industrial enterprises are faced with the need to optimize production processes and improve resource efficiency. Mathematical modeling of resource provision allows for both analyzing the current enterprise status and predicting the outcomes of management decisions, making it a crucial tool for making informed strategic decisions. This approach is particularly relevant for industrial enterprises, where the accuracy of resource planning and provision directly impacts financial performance. The advancement of information technologies and the increasing availability of statistical data have facilitated the widespread use of mathematical modeling in today's economy. This is particularly relevant for industrial enterprises operating in highly turbulent conditions, largely due to US and EU sanctions pressure, as well as challenges with investment borrowing caused by the high key rate of the Central Bank of Russia. Among various modeling approaches, the use of production functions, regression models, general equilibrium models, as well as econometric and ecological models, is particularly significant. To conduct a systems analysis of industrial enterprises, the authors propose a mathematical model based on the Cobb-Douglas production function, which describes the dependence of output on production resources. To simulate enterprise operations, they developed a proprietary model-building algorithm implemented in the form of a computer program in Java and registered with the State Registry of Computer Programs. The adequacy of the resulting model was verified using the coefficient of determination, which demonstrated its high reliability.

Keywords


systems analysis; resource provision; mathematical modeling; computer software

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References


Saurin T.A., Formoso C.T., Reck R., Beck da Silva Etges B.M., Ribeiro J.L.D. Findings from the Analysis of Incident-Reporting Systems of Construction Companies. Journal of Construction Engineering and Management, 2015, vol. 141, no. 9. DOI: 10.1061/(asce)co.1943-7862.0000988

Pop L.D. Digitalization of the System of Data Analysis and Collection in an Automotive Company. Procedia Manufacturing, 2020, vol. 46, pp. 238-243. DOI: 10.1016/j.promfg.2020.03.035

Vogel-Heuser B., Fischer J., Feldmann S., Ulewicz S., Rosch S. Modularity and Architecture of PLC-Based Software for Automated Production Systems: An Analysis in Industrial Companies. Journal of Systems and Software, 2017, vol. 131, pp. 35-62. DOI: 10.1016/j.jss.2017.05.051

Pecina E., Milos D.S., Dvorski I.L. Qualitative Analysis of Enterprise Risk Management Systems in the Largest European Electric Power Companies. Energies, 2022, vol. 15, no. 15. DOI: 10.3390/en15155328

Lestari A., Fauzan M. Analisis Du Pont System Measuring Company Financial Performance (Case Study of Telecommunication Companies Listed on the IDX IN 2016-2021). Mankeu (Jurnal Manajemen Keuangan), 2023, vol. 1, no. 2, pp. 98-127. DOI: 10.61167/mnk.v1i2.38

Sharov S., Sushko E. The Development of an Information System for the Analysis of the Company Employees. Ukrainian Journal of Educational Studies and Information Technology, 2017, vol. 5, no. 3, pp. 73-83. DOI: 10.32919/uesit.2017.03.07

Andry J.F., Bernanda D.Y., Honni, Christianto K., Andriani A. Analysis of Information Systems Strategic Planning Using Ward and Peppard Framework Case E-Commerce Company. International Journal of Advances in Applied Sciences, 2023, vol. 12, no. 2, pp. 179-187. DOI: 10.11591/ijaas.

STT L.S., Putera A.R. Analisis Sistem Informasi CRM Untuk Meningkatkan Pelayanan dengan Pendekatan SDLC (Studi Kasus: UMKM ``SAKTI''). Jurnal Pilar Teknologi: Jurnal Ilmiah Ilmu Ilmu Teknik, 2019, vol. 4, no. 2. DOI: 10.33319/piltek.v4i2.41

Jain A. A Case Study on Measurement Systems analysis (MSA) at Pump Company. International Research Journal of Engineering and Technology (IRJET), 2017, vol. 4, no. 5, pp. 1567-1571.

Tania S.D., Rumiasih N.A., Riani D., Prasetia A. Du Pont Systems Analysis in Measuring Financial Performance of Pharmaceutical Companies Listed on The Indonesia Stock Exchange (Idx). Jurnal HARMONI: Jurnal Akuntansi Dan Keuangan, 2022, vol. 1, no. 1, pp. 17-23. DOI: 10.32832/jharmoni.v1i1.7207

Roca-Riu M., Estrada M. An Evaluation of Urban Consolidation Centers Through Logistics Systems Analysis in Circumstances Where Companies Have Equal Market Shares. Procedia - Social and Behavioral Sciences, 2012, vol. 39, pp. 796-806. DOI: 10.1016/j.sbspro.2012.03.148

Beiler B.C., Ignacio P.S. de A., Pacagnella Junior A.C., Anholon R., Rampasso I.S. Reverse Logistics Systems Analysis of a Brazilian Beverage Company: An Exploratory Study. Journal of Cleaner Production, 2020, vol. 274, article ID: 122624. DOI: 10.1016/j.jclepro.2020.122624

Sanchis P.I., Bonavia T. Internal Communication Systems Analysis in a Small Company. WPOM-Working Papers on Operations Management, 2017, vol. 8, no. 1, pp. 9-21.

Hidayat W., Pramono B., Afdulloh M. Systems Analysis of Inventory Information on Raw Material Companies. Aptisi Transactions on Management (ATM), 2019, vol. 3, no. 2, pp. 126-130. DOI: 10.33050/atm.v3i2.903

Jisana T.K. Consumer Behavior Models: An Overview. Sai Om Journal of Commerce and Management, 2014, vol. 1, pp. 34-43.

Mandal N., Rucker D.D., Levav J., Galinsky A.D. The Compensatory Consumer Behavior Model: How Self-Discrepancies Drive Consumer Behavior. Journal of Consumer Psychology, 2017, vol. 27, pp. 133-146.

Martynenko A.V., Vicharev S.V. A Firm Model with Strict Regulation and Management Influence on Profit. Mathematical Notes NEFU, 2020, vol. 27, pp. 39-53.

Mokhov V., Chebotareva G. Mathematical Modeling and Analysis Activities of PJSC ``Fortum''. Bulletin of the South Ural State University. Series Mathematical Modelling, Programming and Computer Software, 2022, vol. 15, pp. 111-117.

Chen J., Wang X., Liu J. Corporate Social Responsibility and Capacity Sharing in a Duopoly Model. Applied Economics Letters, 2021, vol. 28, pp. 512-517.

Ren J., Sun H., Xu G., Hou D. Convergence of Output Dynamics in Duopoly Co-Opetition Model With Incomplete Information. Mathematics and Computers in Simulation, 2023, vol. 207, pp. 209-225.

Baqaee D.R., Farhi E. The Microeconomic Foundations of Aggregate Production Functions. NBER Working Paper n.25293, 2019, available at: http://www.nber.org/papers/w25293.pdf (accessed on 26 January 2020).

Zagrebina S.A., Mokhov V.G., Tsimbol V.I. Electrical Energy Consumption Prediction Is Based on the Recurrent Neural Network. Procedia Computer Science, 2019, vol. 150, pp. 340-346.

Deride J., Jofre A., Wets R.J.B. Solving Deterministic and Stochastic Equilibrium Problems via Augmented Walrasian. Computational Economics, 2019, vol. 53, pp. 315-342.

Gowdy J., Hall C., Klitgaard K., Krall L. What Every Conservation Biologist Should Know About Economic Theory. Conservation Biology, 2010, vol. 24, pp. 1440-1447.

Mokhov V.G. Mathematical Modeling Program for Enterprise Operational Activities. Computer program registration certificate RU 2021615082, April 2, 2021. Application No. 2021613877.


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