About the project

Target:

Development of a digital twin of a food industry enterprise using artificial intelligence and industrial Internet of Things (IIoT) technologies to improve production efficiency, ensure process sustainability and support the digital transformation of the industry in accordance with the Industry 4.0 concept.

Project objectives:

Conducting marketing and analytical research on automation and digitalization of production, technological and business processes to identify the needs of the enterprise with an assessment of the current level of technological readiness.
Designing a digital twin of an enterprise using CAD/CAM technologies, simulation modeling methods and process optimization taking into account the principles of lean manufacturing.
Development of a data collection and storage system, including sensors, IIoT devices and other data sources for monitoring various parameters at the enterprise, ensuring data security and confidentiality.
Development of big data analysis algorithms for a cyber-physical system of a digital twin and machine learning methods for identifying patterns, anomalies, optimizing processes and predicting future events at the enterprise.
Integration of a three-dimensional modeling system for industrial facilities, mathematical models of production optimization and a cyber-physical system based on IIoT with existing enterprise management systems for automation and optimization of processes.
Creation of a user-friendly interface for data visualization, analytics and making management decisions based on information obtained from a digital twin.
Conducting pilot work on testing and validating a digital twin of an enterprise on real data and usage scenarios.
Support for educational and scientific research, creation of training stands for production simulation using artificial intelligence and IIoT technologies, development of a virtual environment for human-machine interaction of a digital twin with a decision support system.

By the end of the project, the expected results include:

Development of a digital twin of an enterprise using CAD/CAM technologies, simulation modeling and process optimization methods, and taking into account lean manufacturing principles.
Development of a comprehensive data collection system using IIoT devices.
Development of big data analysis algorithms and application of machine learning methods to identify patterns, anomalies, optimize processes and predict future events.
Integration of a cyber-physical system based on IIoT with existing enterprise management systems.
Development of a user-friendly interface with visualization and data analysis tools for making management decisions based on data from a digital twin.
Development of training stands for production simulation using AI and IIoT.