[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123714-en":3,"doc-seo-123714-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123714,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning based internal and external energy assessment of automotive factories - Research report","The document presents a data-driven methodology for predicting factory energy demand and benchmarking performance to support industrial decarbonisation. A machine learning approach quantifies factors influencing energy performance, enables identification of “best in class” factories, and derives improvement fields of action. Results are validated using assessments across an automotive OEM both internally and against external competitors. The approach is designed to be transferable using accessible public data, enabling broader industry-wide studies.","ARTICLE IN PRESS   \nJID: CIRP [m191;June 2, 2023;3:33]  \nCIRP Annals-Manufacturing Technology 00 (2023) 1􀀁4  \nContents lists available at ScienceDirect  \nCIRP Annals-Manufacturing Technology  \n journal homepage: [https://www.editorialmanager.com/CI RP/default. aspx](https://www.editorialmanager.com/CI RP/default. aspx)  \n| Machine learning based internal and external energy assessment of automotive factories\u003Cbr>Dominik Flicka,b, Melina Vrunab, Milan Bartosc, LiJib, Christoph Herrmann (2)a, Sebastian Thiede (2)d,*\u003Cbr>a Institute of Machine Tools and Production Technology, Technische Universit€at Braunschweig, 38106 Braunschweig, Germany b Stellantis N. V.,Global Manufacturing Decarbonisation Engineering, 2132 LS Hoofddorp,the Netherlands\u003Cbr>c Toyota Motor Manufacturing Czech Republic s.r.o., 280 02 Kolín, Czech Republic\u003Cbr>d Chair of Manufacturing Systems, Department of Design, Production and Management, University of Twente, Enschede, the Netherlands |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Article history:\u003Cbr>Available online xxx |  | 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.\u003Cbr>© 2023 The Author(s). Published by Elsevier Ltd on behalf ofCIRP. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) |\n| Keywords:\u003Cbr>Energy efficiency Factory Machine learning |  |  |\n\n1. Introduction  \nAs again pointed out by the IPCC report, human induced greenhouse gas emissions and related global warming are of critical concern nowadays [1]. The energy demand of the industrial sector is oneof the largest contributors and therefore in special focus and of high importance to governments and regulatory bodies. Environmental and economic factors (e.g. rising costs) of energy demand also increase the relevance for individual manufacturing companies. From global industrial perspective the automotive industry certainly plays a key role with over 60 million cars produced each year [2]. Supplied car components come together in several hundred assembly plants worldwide operated by diverse automotive OEM (original equipment manufacturer) -just in Europe there are over 130 facilities in place [3]. This last phase of automotive manufacturing typically consists of three main steps 􀀁 body shop, paint shop and final assembly. The lifecycle assessment (LCA) of an automotive factory clearly indicates the use stage as most relevant for global warming potential (GWP) with a share of 77% over the total factory life cycle [4]. Within that, energy demand of both the production equipment and the necessary technical building services (TBS, e.g. heating, ventilation, air conditioning or lighting) are by far the most important factors. The most widely applied energy performance indicator (EnPI) for analysis and evaluation of automotive production plants is the specific energy consumption (SEC), expressed as energy required to produce one vehicle (kWh/veh) [5]. As shown in Fig. 1(a), based on sustainability reporting, the SEC of automotive OEMs significantly varies between 900 kWh/veh and 3500 kWh/veh. This variance could be driven by inhomogeneous scopes. But Fig. 1(b) narrows this down and shows the SEC of comparable assembly plants [6,7] with similar product  \n* Corresponding author.  \nE-mail address: [s.thiede@utwente.nl](s.thiede@utwente.nl) (S. Th","cbCaigFMkqiv9Vcc","https://ap.wps.com/l/cbCaigFMkqiv9Vcc","pdf",1017343,1,4,"English","en",105,"# Introduction\n## Data-driven energy prediction and benchmarking\n## Methodology transferability\n# Energy performance indicators and comparability\n## Specific energy consumption (SEC)\n# Machine learning based internal and external assessment\n## Validation with OEM internal and competitor data","[{\"question\":\"What problem does the methodology address for automotive factories?\",\"answer\":\"It targets systematic energy demand analysis to reduce industrial greenhouse gas emissions, focusing on predicting energy demand and benchmarking factory performance for improvement.\"},{\"question\":\"How does the machine learning approach support benchmarking?\",\"answer\":\"It quantifies performance-influencing factors, helps identify “best in class” factories and pinpoints fields of action for energy improvement.\"},{\"question\":\"On what data is the approach validated and how is it transferred?\",\"answer\":\"Validation is performed within an automotive OEM using both internal data and external competitor assessment, and the method can be applied broadly using well-accessible public data.\"}]","Machine learning based internal and external energy assessment of automotive factories - 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