The current conditions of wartime instability present Ukrainian enterprises with significant challenges associated with the disruption of logistics chains, growing operational risks, foreign exchange market volatility, resource shortages, and declining predictability of financial outcomes. Under such conditions, the ability of enterprises to rapidly adapt to changes in the external environment on the basis of real-time data analysis and decision support becomes critically important.
One of the key instruments of enterprise digital transformation is the ERP system, which ensures centralised accumulation of data on the financial and economic activities of an enterprise [2; 7]. The use of ERP systems enables the integration of information on sales, procurement, inventories, accounts receivable and payable, cash flows, and other business processes into a unified information environment.
Traditional ERP systems, however, are primarily oriented towards the automation of accounting and operational processes, whereas contemporary conditions require intelligent mechanisms for analysis and forecasting [3]. AI-driven ERP analytics is defined here as a system that integrates transactional ERP data with machine learning and predictive analytics algorithms to support managerial decision-making in real time [8].
The use of AI-driven ERP analytics enables scenario-based forecasting of an enterprise's financial condition on the basis of historical data and current changes in the external environment [1; 6]. The methodological foundation of such systems comprises gradient boosting algorithms, recurrent neural networks (LSTM), and time-series regression models, which allow processing large volumes of transactional data to assess enterprise liquidity, profitability, and cash flows.
The scenario-based approach is of particular relevance under current conditions, as it involves constructing several development models: a baseline, a crisis, a pessimistic, and a recovery scenario [4; 5]. This approach improves the quality of managerial decisions under conditions of high uncertainty.
Further research should focus on integrated approaches to assessing the adaptability and financial resilience of enterprises using ERP data, artificial intelligence technologies, and predictive analytics. A promising direction is the development of a scenario-based AI analytics framework built on ERP data as a methodological foundation for crisis management of enterprises under conditions of wartime and economic instability.
Thus, the integration of ERP systems, artificial intelligence technologies, and scenario-based forecasting creates the prerequisites for enhancing enterprise adaptability, improving crisis management effectiveness, and ensuring business financial resilience in wartime conditions. The proposed framework may serve as a methodological basis for domestic enterprises in formulating digital resilience strategies.
References
1. Davenport T., Harris J. Competing on Analytics: The New Science of Winning. Harvard Business Review Press, 2017. 240 p.
2. Klaus H., Rosemann M., Gable G. What is ERP? // Information Systems Frontiers. 2000. Vol. 2, No. 2. P. 141–162.
3. Brynjolfsson E., McAfee A. The Second Machine Age. New York : W. W. Norton & Company, 2016. 336 p.
4. OECD. Enhancing Resilience by Boosting Digital Business Transformation in Ukraine. OECD Publishing, 2024. URL: https://www.oecd.org/ukraine.
5. Porter M., Heppelmann J. How Smart, Connected Products Are Transforming Competition // Harvard Business Review. 2015. Vol. 93, No. 10. P. 96–114.
6. Wamba S. F., Gunasekaran A., Akter S. Big Data Analytics and Firm Performance // Journal of Business Research. 2017. Vol. 70. P. 356–365.
7. Markuts V. I., Kyzenko O. O. ERP System as a Tool for Ensuring Rational Use of Company Resources // Scholarly Notes: Collection of Scientific Papers of KNEU. 2023. Iss. 32(3). P. 68–78. DOI: 10.33111/vz_kneu.32.23.03.06.045.051
8. Kovernianska Yu. V. The Impact of Artificial Intelligence on Innovation in the Financial Sector of Ukraine // Current Issues in Economics. 2024. URL: https://a-economics.com.ua/index.php/home/article/view/25
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