@inbook{4639a2e13b4046f883233fdc1bb89446,
title = "KPI-Driven Metric Acquisition Methodology with a Energy-Centric Robotic Performance Case Study",
abstract = "This study introduces a robust n-tier implementation architecture for Key Performance Indicator (KPI)-driven energy-related data monitoring and visualisation, focusing on industrial robotic manipulators in small and medium-sized enterprises (SMEs). Emphasising interoperability, customisation, and ease of integration, the architecture is demonstrated through a case scenario involving a collaborative industrial robotic manipulator. Real-time energy consumption data is collected and visualised using layers for data acquisition, network gateway, data management with OPC-UA and InfluxDB, and data visualisation with Grafana. The user-centric design includes dashboards tailored for shop-floor engineers and managerial decision-makers, incorporating key energy-related metrics. Future work is proposed to integrate advanced data analytics techniques and enhance network layers for scalability in broader smart manufacturing scenarios, aligning with benchmarking practices for sustainability and adaptability in evolving industry needs.",
keywords = "Digital twins, Industry 4.0, Key performance indicators",
author = "Louie Webb and Tokhi, {Mohammad Osman} and Bugra Alkan",
year = "2024",
month = jun,
day = "5",
doi = "10.1007/978-3-031-55817-7_7",
language = "English",
isbn = "978-3-031-55816-0",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "105--117",
booktitle = "Studies in Computational Intelligence",
address = "Germany",
}
