INFORMATION MODEL OF SYSTEM OF SUPPORT OF DECISION MAKING DURING MANAGEMENT OF IT COMPANIES
Yehor TATARCHENKO
gosahi@gmail.comVolodymyr Dahl East Ukrainian University, Faculty of Information Technology and Electronics, Department of Programming and Mathematics, Tsentralnyi Ave., 59A, Severodonetsk, Luhansk Oblast (Ukraine)
Volodymyr LYFAR
Volodymyr Dahl East Ukrainian University, Faculty of Information Technology and Electronics, Department of Programming and Mathematics, Tsentralnyi Ave., 59A, Severodonetsk, Luhansk Oblast (Ukraine)
Halyna TATARCHENKO
Volodymyr Dahl East Ukrainian University, Faculty of Information Technology and Electronics, Department of Programming and Mathematics, Tsentralnyi Ave., 59A, Severodonetsk, Luhansk Oblast (Ukraine)
Abstract
An information model has been carried out, with the help of which it is possible to implement methods that ensure the growth of competitiveness of IT companies. Growth conditions for companies provide mergers and acquisitions (M&A). The analysis of the data obtained as a result of the P&L financial report is mainly based on current indicators and can be partially used to prolong economic indicators for a certain (most often limited) period. The authors propose using methods for assessing stochastic indicators of IT development processes based on the solution of a number of problems: (1) Development of models to assess the impact of indicators in the analysis of the financial condition of companies; (2) Creation of an information model and methods for processing current stochastic data and assessing the probability of the implementation of negative and positive outcomes.
Keywords:
IT company, risk, decision making, mergers and acquisitions, FTA, ETAReferences
Darnall, R., & Preston, J. M. (2016). Project Management from Simple to Complex. University of Minnesota Libraries Publishing.
Google Scholar
Draft Federal Information Processing Standards Publication 183. (1993). Integration Definition For Function Modeling (IDEF0).
Google Scholar
Draft Federal Information Processing Standards Publication 184. (1993). Integration Definition For Information Modeling (IDEF1X).
Google Scholar
IEC 60300-3-9:1995. (1995). Dependability management – Part 3: Application guide – Section 9:Risk analysis of technological systems.
Google Scholar
Iovanella, A. (2017). Vital few e trivial many. In Il Punto (pp. 10–13).
Google Scholar
Kringel, D., Ultsch, A., Zimmermann, M., Jansen, J. P., Ilias, W., Freynhagen, R., & Resch, E. (2017). Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses. The pharmacogenomics Journal, 17(5), 419–426, https://doi.org/10.1038/tpj.2016.28.
DOI: https://doi.org/10.1038/tpj.2016.28
Google Scholar
Pagach, D., & Warr, R. (2011). The Characteristics of Firms That Hire Chief Risk Officers. The Journal of Risk and Insurance, 78(1), 185–211.
DOI: https://doi.org/10.1111/j.1539-6975.2010.01378.x
Google Scholar
Pawelek, B., Pociecha, J., & Baryla, M. (2017). ABC Analysis in Corporate Bankruptcy Prediction. In Abstracts of the IFCS Conference (p. 17). Tokyo, Japan.
Google Scholar
SS-IEC 1025:1990. (1990). Fault tree analysis (FTA).
Google Scholar
Ultsch, A., & Lötsch, J. (2015). Computed ABC analysis for rational selection of most informative variables in multivariate data. PLOS One, 10(6), e0129767. https://doi.org/10.1371/journal.pone.0129767
DOI: https://doi.org/10.1371/journal.pone.0129767
Google Scholar
What are the Main Valuation Methods? (2019). Retrieved August 12, 2019 from https://corporatefinanceinstitute.com/resources/knowledge/valuation/valuation-methods
Google Scholar
Authors
Yehor TATARCHENKOgosahi@gmail.com
Volodymyr Dahl East Ukrainian University, Faculty of Information Technology and Electronics, Department of Programming and Mathematics, Tsentralnyi Ave., 59A, Severodonetsk, Luhansk Oblast Ukraine
Authors
Volodymyr LYFARVolodymyr Dahl East Ukrainian University, Faculty of Information Technology and Electronics, Department of Programming and Mathematics, Tsentralnyi Ave., 59A, Severodonetsk, Luhansk Oblast Ukraine
Authors
Halyna TATARCHENKOVolodymyr Dahl East Ukrainian University, Faculty of Information Technology and Electronics, Department of Programming and Mathematics, Tsentralnyi Ave., 59A, Severodonetsk, Luhansk Oblast Ukraine
Statistics
Abstract views: 37PDF downloads: 2
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Eduardo Sánchez-García, Javier Martínez-Falcó, Bartolomé Marco-Lajara, Jolanta Słoniec, ANALYZING THE ROLE OF COMPUTER SCIENCE IN SHAPING MODERN ECONOMIC AND MANAGEMENT PRACTICES. BIBLIOMETRIC ANALYSIS , Applied Computer Science: Vol. 20 No. 1 (2024)
- Tilla IZSÁK, László MARÁK, Mihály ORMOS, EVALUATION OF SUPPORT VECTOR MACHINE BASED STOCK PRICE PREDICTION , Applied Computer Science: Vol. 19 No. 3 (2023)
- Siti ROHAJAWATI, Hutanti SETYODEWI, Ferryansyah Muji Agustian TRESNANTO, Debora MARIANTHI, Maruli Tua Baja SIHOTANG , KNOWLEDGE MANAGEMENT APPROACH IN COMPARATIVE STUDY OF AIR POLLUTION PREDICTION MODEL , Applied Computer Science: Vol. 20 No. 1 (2024)
- Małgorzata ŚLIWA, Ewelina KOSICKA, A MODEL OF KNOWLEDGE ACQUISITION IN THE MAINTENANCE DEPARTMENT OF A PRODUCTION COMPANY , Applied Computer Science: Vol. 13 No. 3 (2017)
- Alexis J. LOPEZ, Perfecto M. QUINTERO, Ana K. HERNANDEZ, ANALYTICS AND DATA SCIENCE APPLIED TO THE TRAJECTORY OUTLIER DETECTION , Applied Computer Science: Vol. 16 No. 2 (2020)
- Miguel Angel BELLO RIVERA, Carlos Alberto REYES GARCÍA, Tania Cristal TALAVERA ROJAS, Perfecto Malaquías QUINTERO FLORES, Rodolfo Eleazar PÉREZ LOAIZA, AUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS , Applied Computer Science: Vol. 19 No. 4 (2023)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Pannangi Naresh, R. Suguna, IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES , Applied Computer Science: Vol. 17 No. 3 (2021)
- Elvis COUTIÑO-MORENO, Quirino ESTRADA, Daniel MALDONADO-ONOFRE, Alejandro RODRIGUEZ-MENDEZ, Julio GOMEZ-GIRON, RESOLUTION IN THE 3D MODELING OF OBJECTS FOR ADDITIVE MANUFACTURING AND REVERSE ENGINEERING – SHUTTER EFFECT , Applied Computer Science: Vol. 17 No. 1 (2021)
- Saleh ALBAHLI, A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION , Applied Computer Science: Vol. 16 No. 3 (2020)
You may also start an advanced similarity search for this article.
