RESEARCH TITLE
Prediction of industrial production based on Industrial Early Warning Composite Index (IEWCI)
RESEARCH FIELD
Economy
RESEARCH SUBJECT
Prediction of industrial production
PROBLEM
The project aimed to help Serbian companies to navigate the volatile economic environment post-2008 financial crisis by providing tools to predict short-term market movements.
RESEARCH
The invention lies in the combination of traditional economic indicators, such as GDP, exports, and industrial production data, with advanced machine learning algorithms to generate more accurate and timely predictions. Machine learning, particularly Random Forest models, was applied to identify the most influential indicators for forecasting, offering improved predictive accuracy over traditional statistical models. This enables businesses to predict market movements (such as rising or falling industrial production) with minimal error, enhancing their ability to adapt operations proactively.
The novelty of the project is in creating a machine learning-based composite index specifically tailored to Serbian industries, which, to the best of the team’s knowledge, has not been implemented before. The web platform further supports this innovation by offering a user-friendly interface for businesses to input data and receive forecasts. The platform is initially offered free for one month, followed by a paid subscription model, creating a clear pathway to market.
RESULT
The application of the project’s results is far-reaching. By providing SMEs with real-time, data-driven insights into market movements, the platform helps them make informed decisions on inventory management, production adjustments, and market strategies. This predictive capability can significantly reduce the risks of being caught off guard by market changes, resulting in potential cost savings and higher profitability. Furthermore, adapting to market trends more effectively gives businesses a competitive edge over companies relying on subjective decision-making, thereby increasing their market success.
