[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121677-en":3,"doc-seo-121677-105":30,"detail-sidebar-cat-0-en-105":95},{"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},121677,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","The Sustainable Adoption of Industry 4.0 & Machine Learning in the Automotive Industry - Doctorate of Engineering Thesis","The automotive industry is undergoing major transformation driven by environmental legislation, evolving consumer expectations, Industry 4.0 technologies, and rapid battery-technology progress that is accelerating the shift to electric powertrains. Competitive positioning requires both technical and organisational innovation to enable digital, data-driven business practices across manufacturers. This research addresses these change opportunities through a three-step approach: a critical review of machine-learning applications and development barriers, a structured framework to evaluate Industry 4.0 maturity and guide factory-level digital transformation, and detailed presentation of two machine-learning projects. The work contributes industrially relevant anomaly detection advancements and explores data-as-a-service value-chain models for EV customers.","The Sustainable Adoption of Industry 4.0 & Machine Learning in the Automotive Industry  \nby  \nJames Flynn  \nThesis submitted to  \nSwansea University  \nIn fulfillment of the requirements  \nFor the Doctorate of Engineering  \nEng.D  \nDepartment  \nMechanical Engineering  \n2023  \nCopyright: The Author, James Flynn, 2023.  \nSupervisors  \nThe following acted as the academic and industrial supervisors for this thesis.  \nAcademic Supervisor (1): Cinzia Giannetti,  \nProfessor, Dept. of Engineering,  \nSwansea University  \nAcademic Supervisor (2): Christian Griffiths,  \nAssociate Professor, Dept. of Engineering, Swansea University  \nIndustrial Supervisor: Steven Buck,  \nPTO Business Manager,  \nDunton Technical Centre,  \nFord Motor Company  \nAuthor’s Declaration  \nThis work has not previously been accepted in substance for any degree and is not being concurrently submitted in candidature for any degree.  \nSigned........  ..................................  \n12.06.23  \nDate......................................................................  \nThis thesis is the result of my own investigations, except where otherwise stated. Other sources are acknowledged by footnotes giving explicit references. A bibliography is appended.  \nSigned..........  ....................................  \n12.06.23  \nDate......................................................................  \nI hereby give my consent for my work, if relevant and accepted, to be available for photocopying and for inter-library loans after expiry of a bar on access approved by the University.  \nSigned........  .....................................  \nDate............1.2...0.6...2.3. ............................................  \nThe University’s ethical procedures have been followed and, where appropriate, that ethical approval has been granted.  \nSigned.........  ...................................  \n12.06.23  \nDate......................................................................  \nAbstract  \nThe automotive industry is undergoing a major transformation. New environmental legislation, changing consumer requirements, Industry 4.0 technologies, and advancementsin battery technologies, have contributed to an industry-wide shift towards electric powertrain. To remain competitive in this rapidly changing environment, automotive manufacturers must ensure high levels of technical and organisational innovation to transition towards digital and data-driven business practices.  \nThis research aims to address these growth opportunities and manage ongoing change in three steps. First, the literature on machine learning applications in automotive manufacturing is critically reviewed and the barriers to developing and implementing machine learning are discussed. Secondly, a structured framework is developed to assess the industry 4.0 maturity of automotive manufacturing operations and guide digital transformation at the factory level. In the third and final step of this research, two machine learning projects identified by the assessment are presented in detail. The first case study presentsan anomaly detection solution to identify process errors in engine assembly. This research introduces multiple advancements in anomaly detection in manufacturing, including the introduction of the Anomaly No Concern class. The second case study is a greenfield project to explore new digital value chains to add value to EV customers and explore dataas-a-service business models. This case study uses a combination of Google Street View data and GIS data to identify houses suitable for EV charging and represents a major advancement towards fully automated remote surveying of the built environment. To conclude, multiple advancements are presented that contribute to the academic literature. Clear stepwise frameworks support the proposed industrial solutions to develop, implement and replicate these solutions across the business. These contributions have been used to support ongoing digitalisation efforts, impleme","cbCaiaskV351KW7F","https://ap.wps.com/l/cbCaiaskV351KW7F","pdf",28657785,1,315,"English","en",105,"# Abstract\n# Acknowledgements\n# Dedication","[{\"question\":\"What transformation is occurring in the automotive industry according to the thesis abstract?\",\"answer\":\"The industry is shifting toward electric powertrains due to new environmental legislation, changing consumer requirements, Industry 4.0 technologies, and advancements in battery technology.\"},{\"question\":\"What are the three main steps of the research described in the abstract?\",\"answer\":\"First, it critically reviews literature on machine learning in automotive manufacturing and discusses barriers. Second, it develops a framework to assess Industry 4.0 maturity and guide factory digital transformation. Third, it presents two detailed machine-learning projects selected by the assessment.\"},{\"question\":\"What does the first case study focus on?\",\"answer\":\"It focuses on anomaly detection to identify process errors in engine assembly, including the introduction of the Anomaly No Concern class.\"},{\"question\":\"What does the second case study investigate?\",\"answer\":\"It explores greenfield digital value chains and data-as-a-service business models for EV customers using Google Street View and GIS data to identify suitable homes for EV charging and enable more automated remote surveying.\"}]","The Sustainable Adoption of Industry 4.0 & Machine Learning in the Automotive Industry - Doctorate of Engineering Thesis | PDF",1785806158,794,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"the-sustainable-adoption-of-industry-40-machine-learning-in-the-automotive-industry-doctorate-of-engineering-thesis","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-sustainable-adoption-of-industry-40-machine-learning-in-the-automotive-industry-doctorate-of-engineering-thesis/121677/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What transformation is occurring in the automotive industry according to the thesis abstract?","Question",{"text":75,"@type":76},"The industry is shifting toward electric powertrains due to new environmental legislation, changing consumer requirements, Industry 4.0 technologies, and advancements in battery technology.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the three main steps of the research described in the abstract?",{"text":80,"@type":76},"First, it critically reviews literature on machine learning in automotive manufacturing and discusses barriers. Second, it develops a framework to assess Industry 4.0 maturity and guide factory digital transformation. Third, it presents two detailed machine-learning projects selected by the assessment.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the first case study focus on?",{"text":84,"@type":76},"It focuses on anomaly detection to identify process errors in engine assembly, including the introduction of the Anomaly No Concern class.",{"name":86,"@type":73,"acceptedAnswer":87},"What does the second case study investigate?",{"text":88,"@type":76},"It explores greenfield digital value chains and data-as-a-service business models for EV customers using Google Street View and GIS data to identify suitable homes for EV charging and enable more automated remote surveying.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]