Machine learning based internal and external energy assessment of automotive factories
Dominik Flick, Melina Vruna, Milan Bartos, Li Ji, Christoph Herrmann, Sebastian Thiede
Abstract
In order to reduce industrial greenhouse gas emissions, systematic energy demand analysis and the derivation of improvement strategies are key. Against this background, a methodology for data driven energy demand prediction and performance benchmarking for factories is presented. The machine learning based approach enables to quantify performance influencing factors, identify “best in class” factories and fields of action for improvement. The results are validated within an automotive OEM internal and even external competitor assessment. The transferable approach based on well accessible public data also enables larger industry wide studies.
Topics & Concepts
BenchmarkingAutomotive industryOriginal equipment manufacturerManufacturing engineeringEngineeringAutomotive engineeringKey (lock)Industrial engineeringComputer scienceBusinessOperating systemAerospace engineeringMarketingComputer securityEnergy Efficiency and ManagementEnvironmental Impact and SustainabilityFlexible and Reconfigurable Manufacturing Systems