Högskolan i Skövde

Manufacturing companies are gradually moving from Industry 4.0’s technology focus to Industry 5.0’s sustainability focus, and identifying and addressing energy bottlenecks is a part of this transition. In practice, this is challenging due to limited availability of energy data and its poor integration with systems like MES and SCADA. Energy dashboards are capable of consolidating energy data, visualizing consumption patterns, and tracking related KPIs for sustainability. However, most existing implementations are limited to facility-level overviews or machine-specific views without consideration of operational details. To identify energy bottlenecks, the dashboards must also analyze machine states, batch sizes, product mixes, and cycle times. Therefore, this paper presents a Python-based web application built with the Dash framework and open-source packages. The application integrates data from EMS, MES, and SCADA systems. It is capable of performing statistical time-series analysis, joint energy-stop analysis, state-based mapping of energy use, and visualizing various Key Performance Indicators. The proposed integrated dashboard targets discrete manufacturing and is demonstrated on a gear machining line at Volvo Group Trucks Operations. The dashboard currently operates offline with data from enterprise systems, but aims for real-time API integration as a digital twin in the future. This could support simulation for detecting inefficiencies, predicting energy bottlenecks, and optimizing energy consumption.