Cloud-Based Platform for Industrial Data Management and Analysis
Kitadu, Elia (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202504257615
https://urn.fi/URN:NBN:fi:amk-202504257615
Tiivistelmä
The rapid advancement of digital technologies in manufacturing has heightened the demand for real-time monitoring, effective data management, and process optimization. To address these needs, an IoT-based platform was developed to automate data collection, integrate IoT devices, and enable continuous moni-toring of key performance indicators. The primary objectives included enhancing operational efficiency and advancing the digital transformation of industrial operations in accordance with Industry 4.0 revolu-tion principles.
A modular system architecture was implemented, supporting scalable integration with existing machinery and process chains. Both qualitative and quantitative research methods were utilized, and real-world case studies were included to demonstrate the practical applicability of the solution. The platform was tested in an industrial environment, where it enabled data-driven process optimization, anomaly detec-tion, and intelligent reporting.
Operational efficiency was improved through real-time insights and quicker responses to process anoma-lies. The results were found to be highly usable due to the system’s ease of expansion, intuitive user in-terfaces, and reliable connectivity, making it suitable for daily operations. Immediate benefits included reduced downtime, better product quality, and improved decision-making. Advanced analytics and the potential for future AI integration were identified as important areas for further development.
In conclusion, the implemented IoT platform offered a robust, scalable, and user-friendly basis for ongo-ing digitalization, supporting innovation and long-term adaptability in industrial environments.
A modular system architecture was implemented, supporting scalable integration with existing machinery and process chains. Both qualitative and quantitative research methods were utilized, and real-world case studies were included to demonstrate the practical applicability of the solution. The platform was tested in an industrial environment, where it enabled data-driven process optimization, anomaly detec-tion, and intelligent reporting.
Operational efficiency was improved through real-time insights and quicker responses to process anoma-lies. The results were found to be highly usable due to the system’s ease of expansion, intuitive user in-terfaces, and reliable connectivity, making it suitable for daily operations. Immediate benefits included reduced downtime, better product quality, and improved decision-making. Advanced analytics and the potential for future AI integration were identified as important areas for further development.
In conclusion, the implemented IoT platform offered a robust, scalable, and user-friendly basis for ongo-ing digitalization, supporting innovation and long-term adaptability in industrial environments.