DECISION SUPPORT SYSTEM FOR THE ELECTRIC VEHICLE MARKET

Abstract

This article investigates the problem of decision support within the electric vehicle (EV) market, which is characterized by volatile dynamics and high uncertainty. The study identifies specific decision-making features unique to the EV sector, substantiating the critical necessity of a specialized Decision Support System (DSS). To address this, a conceptual model is proposed, defining the fundamental design principles including a systems approach, an explicit orientation toward nonlinear dynamics, the systematic integration of analytical and forecasting processes, and the formal consideration of uncertainty parameters. Furthermore, a comprehensive modular architecture is developed, explicitly integrating data collection and storage, multi-factor analysis, time-series forecasting, fuzzy modeling, and strategic recommendation-generation subsystems. The functions, composition, and structural interactions of each distinct module within a unified information-analytical framework are thoroughly justified. A key distinctive feature of the proposed DSS lies in the adaptive selection of advanced forecasting models based on the specific behavior of time-series data, alongside the application of fuzzy logic algorithms to generate optimized recommendations for market investors, manufacturers, and public authorities. Ultimately, the empirical results demonstrate that the proposed methodological approach ensures the highly coordinated operation of all integrated DSS subsystems, thereby significantly improving the validity, transparency, and strategic efficacy of managerial and investment decisions within the global electric vehicle market.