About the Project
Industry Challenges
- Limited integration with external data sources in proprietary MES systems
- Expensive vendor add-ons with limited end-user customisation
- Lack of built-in time series analysis and data interactivity
- Manual data preprocessing for third-party tools
Project Objectives
- Implementation Specific Objectives:
- Build a functioning data analysis platform.
- Support a modular build design.
- Facilitate basic audit trailing.
- Enable fast processing of large quantities of data.
- Enable the use of basic and advanced time-series analysis methods.
- Build an intuitive, simple front-end to allow user interactions with data endpoints but also the platform itself.
- Implementation Specific Scope:
- Deploy a containerised analytics platform using Proxmox.
- Build support for multiple databases, SQL Server PostgreSQL using an abstraction layer.
- Implement Redis caching to improve response time and reduce database load.
- Include an analysis engine capable of complex and basic time-series analysis on selected datasets.
- Develop a frontend using Dash to allow selection of data, time windows, and analysis methods.
- Maintain modular code design with reusable Python classes to support modularity where possible.
- Demonstrate compliance considerations with basic audit trails.
- Focus is mostly on historical data analysis with simulated endpoints. Live data is acknowledged but not fully implemented.
Relevance to Industry 4.0
This project addresses real-world needs by providing a flexible, future-proof platform for smarter industrial analytics. Its modular design supports evolving infrastructure while improving access to time-sensitive decision-making data — a core goal of Industry 4.0.