Project Goal

The goal of this project is to develop an economic model for the implementation of an innovative product using neural network technology. The model will be built based on existing economic models while considering sustainable trends in the digital economy. It is essential to form a "skills profile" based on these trends and provide recommendations for university curricula. Practical recommendations will be proposed to support effective managerial decision-making regarding the implementation of an innovative product based on neural network methods.

Potential Applications

1. Efficiency in Recruitment: The project aims to implement machine learning algorithms to maximize the accuracy of matching employer requirements with candidate skills. This will optimize the hiring process and enhance the efficient use of corporate resources.

2. Adaptive Educational Modules: The development will include algorithms for creating personalized educational programs, fostering continuous professional growth in response to the dynamically evolving labor market demands in megacities.

3. Analytical Support for Enterprises: The project will provide access to advanced analytical tools that facilitate workforce demand forecasting and allow businesses to adjust their strategies according to changing market conditions.

Implementation Methods

The project will be deployed as a SaaS platform, ensuring a high level of accessibility and integration flexibility. This will enable corporations to easily adapt the solution into their operational processes without significant capital investments.

Implementation Plan

The project’s execution strategy includes active collaboration with large corporate entities, professional associations, and municipal and regional authorities to ensure large-scale adoption of the technology in the megacity labor market. Strategic alliances and partnerships will facilitate the rapid penetration and adaptation of this technological solution.

Scientific and Technological Potential

The project will contribute to the creation of significant scientific and technological reserves, which serve as a foundation for economic progress and socio-cultural development in both megacities and Russia as a whole. Upon completion of the research phase, all necessary documentation for verifying the obtained results will be provided.

Methodology and Algorithms for Data Structuring and Economic Modeling

• Application: The developed methodologies will allow enterprises to create reliable information databases and economic models using feedforward neural networks, ensuring accurate forecasting and decision-making.

• Implementation: The economic models will be used to analyze and forecast market trends, assess risks, and make strategic decisions.

Forecasting the Demand for the Innovative Product

• Application: The predictive model will enable businesses to assess the demand for innovative products in advance, minimize risks, and optimize costs.

• Implementation: The forecasts will be utilized in production planning and marketing strategy formulation, accelerating product launch into the market.

Practical Recommendations for Managerial Decision-Making

• Application: The recommendations will provide methodological support to enterprises in making decisions related to the introduction of innovative products.

• Implementation: These recommendations will be tested in two universities and five enterprises, forming a framework for broader scalability across other organizations.

Economic and Social Impact

Economic Growth

• Scientific and Technological Advancements: The project will lay the groundwork for economic growth by increasing business efficiency and competitiveness.

• Advanced Technologies: The implementation of new technologies will allow businesses to optimize processes, reduce costs, and improve product quality.

Social Development

• Workforce Qualification Enhancement: The formation of a "skills profile" and university curriculum recommendations will ensure the preparation of specialists in demand in the digital economy.

• Improved Socio-Labor Relations: The application of managerial decision support methods will enhance socio-labor relations, boosting employee motivation and productivity.

Conclusion

The implementation of the project's results will provide Russian enterprises with advanced tools for data-driven managerial decision-making, contributing to their sustainable development and competitiveness in the market.

Coordinator: Olga Titovna Ergunova (ergunova_ot@spbstu.ru)