Issue: № 2, 2025
Doi: https://doi.org/10.37634/efp.2025.2.4
Predictive analytics has become a crucial tool in modern enterprise management, enabling organizations to anticipate future trends, optimize business processes, and mitigate risks. With the rise of digital transformation, companies increasingly rely on data-driven decision-making to enhance operational efficiency and maintain competitiveness. Predictive analytics leverages historical data, machine learning, and artificial intelligence to develop models that forecast outcomes and provide actionable insights. This paper explores the theoretical foundations of predictive analytics, its core methodologies, and its practical applications in enterprise management. The purpose of the paper is to analyze the role of predictive analytics in enterprise management, assess its effectiveness across various business functions, and identify the key challenges and opportunities associated with its implementation. The study employs a comprehensive research methodology, combining big data analysis, statistical modeling, and machine learning techniques to evaluate the efficiency of predictive analytics in enterprise management. A comparative analysis of successful case studies highlights best practices and challenges in implementing predictive analytics across different industries. Factor analysis is used to determine the critical success factors influencing the adoption of predictive technologies. The research also utilizes a systems approach to examine the interconnections between predictive analytics, business processes, and strategic decision-making. The findings indicate that predictive analytics significantly improves decision-making by enabling businesses to forecast market trends, optimize production cycles, and manage risks proactively. Companies that implement predictive analytics report higher efficiency in supply chain management, enhanced customer targeting, and improved resource allocation. The study identifies key predictive techniques such as machine learning algorithms, regression analysis, and neural networks as critical tools for data-driven business strategies. Furthermore, the integration of predictive analytics with cloud computing and IoT enhances real-time data processing, allowing enterprises to adapt dynamically to changing business environments.
Keywords : predictive analytics, machine learning, business process optimization, risk management
References:
1. Malynovska Yu.B., Kozak A.M., Remez O.S., Mykolaichuk V.M. Quality management system at enterprises in the conditions of European integration. Economics. Finance. Law. 2023. No. 11. pp. 56–60. (in Ukrainian).
2. Pylypenko A.A., Tyrinova M.V. Development of Predictive Analytics in Accounting Support for Enterprise Economic Security Management. Sustainable Economic Development. 2024. No. 2(49). pp. 64–70. (in Ukrainian).
3. Khailkevych A.M. Predictive Analytics in the System of Information and Analytical Support of the Security Service of Ukraine. Legal Scientific Electronic Journal. 2024. No. 11. pp. 475–478. (in Ukrainian).
4. Bashynska I., Niekrassova L., Malynovska Y. Bayesian Network as a Decision Support System in the Company's Risk Management System of Emergency Situations. 2023 IEEE 4th KhPI Week on Advanced Technology: Conference Proceedings, October 02–06, 2023, Kharkiv, Ukraine. 2023.
5. Horodetskyi Yu.D. Predictive analytics and its role in making strategic decisions in marketing. Journal of Strategic Economic Research. 2023. No. 5. pp. 65–72.
6. Bashynska I., Malanchuk M., Zhuravel O., Olinichenko K. Smart solutions: risk management of crypto-assets and blockchain technology. International Journal of Civil Engineering and Technology. 2019. No. 10(2). pp. 1121–1131.
7. Sereda B.P., Mukovska D.Ya., Sereda D.B. Simulation of the manufacturing process of transportation on the technological routes of the career of a metallurgical enterprise. Mathematical Modeling. 2022. No. 2(47). pp. 63–69.