Abstract
The automotive industry aims to achieve continual improvement in energy performance to reduce emissions and costs. High complexity in manufacturing systems makes comprehensive analyses economically infeasible thus a root cause analysis (RCA) methodology is required to identify and quantify subsystems with a high energy saving potential. This allows targeted analyses and measures to be derived. For this purpose, a data-based benchmarking approach for identification and quantification of energy saving potential is developed. The methodology has proven to be suitable to derive significant energy savings in the validation based on a real use case of a globally operating car manufacturer.
Original language | English |
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Pages (from-to) | 1037-1042 |
Number of pages | 6 |
Journal | Procedia CIRP |
Volume | 130 |
Early online date | 27 Nov 2024 |
DOIs | |
Publication status | Published - Dec 2024 |
Event | 18th IFAC Workshop on Time Delay Systems, TDS 2024 - Udine, Italy Duration: 2 Oct 2023 → 5 Oct 2023 Conference number: 18 |
Keywords
- artificial intelligence
- energy efficiency
- factory
- machine learning
- manufacturing