FEATURES OF THE APPLICATION OF PREDICTIONAL ANALYTICS TO ASSESS THE ECONOMIC DEVELOPMENT OF AN ENTERPRISE

Abstract

The economic development of an enterprise is a multidimensional and dynamic category formed by the interaction of financial, market, production, innovation, digital, and organizational indicators. Traditional assessment approaches based mainly on isolated financial ratios are not capable of capturing the systemic, nonlinear, and forward-looking nature of enterprise development, especially under conditions of market turbulence, digital transformation, geopolitical instability, and increasing competitive pressure. In such an environment, management decisions require analytical tools that combine retrospective evaluation with predictive capabilities. Therefore, predictive analytics becomes a key instrument for forward-looking assessment, early risk detection, and evidence-based strategic decision-making. The purpose of this study is to develop and substantiate an integrated algorithm for assessing the prospects of enterprise economic development based on a комплексне використання predictive analytics tools. The research methodology combines trend extrapolation, regression modeling, pseudo-ARIMA forecasting techniques implemented in Excel, construction of a composite innovation development index, risk identification and prioritization using FMEA methodology, investment efficiency assessment, and stochastic scenario modeling based on Monte Carlo simulation. Such methodological integration ensures both analytical depth and practical feasibility for corporate use. The proposed model incorporates several interrelated analytical blocks: forecasting of key financial and digital performance indicators (revenue, ARPU, digital income, EBITDA dynamics), evaluation of innovation potential through a weighted composite index, identification and quantitative ranking of innovation-related risks, assessment of investment return parameters, and scenario-based forecasting under uncertainty conditions. The practical application of the model is demonstrated using the case of Vodafone Ukraine, where publicly available financial statements, industry reports, and expert assumptions were used to simulate innovation-driven economic growth trajectories. The findings confirm that predictive analytics enhances the ability of enterprises to detect long-term development patterns, quantify the economic impact of innovation initiatives, evaluate risk exposure, and improve the overall quality of strategic planning. The proposed approach is flexible, scalable, and adaptable to different industries, particularly high-technology and service-oriented sectors, and supports sustainable economic growth, competitiveness, and resilience in volatile environments.