Building LeaPP Analytics : bridging data metrics, data visualisation, and decision making
Lozhkin, Iurii (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202504106132
https://urn.fi/URN:NBN:fi:amk-202504106132
Tiivistelmä
This thesis explores the development of LeaPP Analytics, a data analytics platform designed to enhance operational efficiency and decision-making in industrial contexts such as warehouses, production lines, assembly lines, and workstations. The platform provides tools for visualizing Key Performance Indicators, calculating critical metrics, and analyzing workflows to optimize processes.
The research involved stakeholder interviews, reviewing existing tools, and examination of JSON files exported from LeaPP Visual Planner to define requirements and ensure compatibility. The resulting platform integrates features such as project management, subprocess configuration, workflow visualization, and KPI comparison, all aimed at providing actionable insights for users.
The outcomes of this work include a functional prototype of LeaPP Analytics, demonstrating its capability to streamline operations, identify bottlenecks, and improve productivity. The platform scalability and adaptability make it suitable for a wide range of industrial applications.
By addressing current limitations in industrial data analytics tools, this thesis contributes to the growing field of industrial analytics. It also lays the foundation for integrating emerging technologies, such as the Internet of things and Artificial intelligence, in future iterations of LeaPP Analytics.
The research involved stakeholder interviews, reviewing existing tools, and examination of JSON files exported from LeaPP Visual Planner to define requirements and ensure compatibility. The resulting platform integrates features such as project management, subprocess configuration, workflow visualization, and KPI comparison, all aimed at providing actionable insights for users.
The outcomes of this work include a functional prototype of LeaPP Analytics, demonstrating its capability to streamline operations, identify bottlenecks, and improve productivity. The platform scalability and adaptability make it suitable for a wide range of industrial applications.
By addressing current limitations in industrial data analytics tools, this thesis contributes to the growing field of industrial analytics. It also lays the foundation for integrating emerging technologies, such as the Internet of things and Artificial intelligence, in future iterations of LeaPP Analytics.