[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290471-105":59,"doc-detail-290471-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","digital-assistance-systems-to-implement-machine-learning-in-manufacturing-a-systematic-review","Digital Assistance Systems to Implement Machine Learning in Manufacturing - A Systematic Review","","Implementing machine learning technologies in manufacturing depends heavily on human expertise spanning domain knowledge and ML know-how. Many companies lack the required capabilities, creating a competence gap that hinders viable deployment. A systematic literature review using PRISMA-P examined 29 papers, analyzing ML use cases, required resources, and future research outlook. Six key findings and development requirements show current systems emphasize technical components while user integration and industrial validation lag behind; future work should better embed users in both development and validation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/digital-assistance-systems-to-implement-machine-learning-in-manufacturing-a-systematic-review/290471/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/digital-assistance-systems-to-implement-machine-learning-in-manufacturing-a-systematic-review/290471.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why do manufacturing companies struggle to implement machine learning?","Question",{"text":112,"@type":113},"Implementation relies on both domain expertise and machine learning programming knowledge, which many companies—especially SMEs—do not have in sufficient depth. This skills and prerequisite gap contributes to poor dissemination and project failure.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What method was used in the study?",{"text":117,"@type":113},"The study conducted a systematic literature review based on the PRISMA-P process, resulting in the identification and in-depth analysis of 29 relevant papers.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the review findings indicate about current digital assistance systems?",{"text":121,"@type":113},"Existing assistance systems focus more on technical aspects, while integration of users and validation in industrial environments lag behind. 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Digital Assistance Systems to Implement Machine Learning in Manufacturing: A Systematic Review. Mach. Learn. Knowl. Extr. 2024, 6, 2808–2828 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)make6040134  \nAcademic Editor: Anna Helena Reali Costa  \nReceived: 27 September 2024  \nRevised: 15 November 2024  \nAccepted: 26 November 2024  \nPublished: 4 December 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institut for Production Management, Technology and Machine Tools, TU Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany; [j.rosemeyer@ptw.tu-darmstadt.de](j.rosemeyer@ptw.tu-darmstadt.de)  \n2 Department of Management Engineering, Politecnico di Milano, Via Lambruschini 4b, 20156 Milan, Italy; [marta.pinzone@polimi.it](marta.pinzone@polimi.it)  \n* Correspondence: j.metternich@ptw.tu-darmstadt.de  \nAbstract: Implementing machine learning technologies in manufacturing environment relies heavily on human expertise in terms of domain and machine learning knowledge. Yet, the required machine learning knowledge is often not available in manufacturing companies. A possible solution to overcome this competence gap and let domain experts with limited machine learning programming skills build viable applications are digital assistance systems that support the implementation. At the present, there is no comprehensive overview over corresponding assistance systems. Thus, within this study a systematic literature review based on the PRISMA-P process was conducted. Twentynine papers were identi􀀂ed and analyzed in depth regarding machine learning use case, required resources and research outlook. Six key 􀀂ndings as well as requirements for future developments are derived from the investigation. As such, the existing assistance systems basically focus on technical aspects whereas the integration of the users as well as validation in industrial environments lack behind. Future assistance systems should put more emphasis on the users and integrate them both in development and validation.  \nKeywords: machine learning; systematic literature review; manufacturing; digital assistance systems; work-based learning  \n1. Introduction and Background  \nCaused by latest innovations such as a strong decrease of computing times of processors as well as advances in algorithms, Machine Learning (ML) applications yield high potentials for manufacturing companies [1,2] . Yet, a survey by the World Economic Forum identi􀀂es a mismatch between ML capabilities and operational needs as well as insuf􀀂cient skills at the intersection of ML and operations leading to a poor dissemination in practice [3] . Indeed, most initiated ML projects fail due to unstructured project management and a lack of prerequisites such as ML knowledge, infrastructure and data [4,5] . Despite all technical advances, implementing such applications still relies strongly on the expertise of different specialists like domain experts, software engineers and data scientists [6,7]—as well as external experts not familiar with speci􀀂c processes [3] . Nevertheless, recent publications show that data scientists and their required ML expertise are missing in many","cbCais99jfQ2LAu7","https://ap.wps.com/l/cbCais99jfQ2LAu7","pdf",1887289,22,"English","# Introduction and Background\n# Digital Assistance Systems and ML Competence Gap\n# Review Method (PRISMA-P)\n# Findings and Requirements for Future Development","[{\"question\":\"Why do manufacturing companies struggle to implement machine learning?\",\"answer\":\"Implementation relies on both domain expertise and machine learning programming knowledge, which many companies—especially SMEs—do not have in sufficient depth. This skills and prerequisite gap contributes to poor dissemination and project failure.\"},{\"question\":\"What method was used in the study?\",\"answer\":\"The study conducted a systematic literature review based on the PRISMA-P process, resulting in the identification and in-depth analysis of 29 relevant papers.\"},{\"question\":\"What do the review findings indicate about current digital assistance systems?\",\"answer\":\"Existing assistance systems focus more on technical aspects, while integration of users and validation in industrial environments lag behind. Future systems should place more emphasis on users and involve them in development and validation.\"}]","Digital Assistance Systems to Implement Machine Learning in Manufacturing - A Systematic Review | PDF",1789641761,55]