[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123328-en":3,"doc-seo-123328-105":30,"detail-sidebar-cat-0-en-105":91},{"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},123328,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Concept of a plug-and-play, Machine Learning Digital Twin - Detailed Capacity Planning and Scheduling","Digital Production and the Industrial Internet of Things enable continuous, highly detailed shop-floor data, which can improve Detailed Capacity Planning and Scheduling (DCPS) within Production Planning and Control software. A Digital Twin combined with Machine Learning can provide accurate, automated virtual representations of production resources, but data connection, preparation, and modelling require significant effort. Connection standards supporting interoperability reduce these burdens and enable plug-and-play software integration. The article compiles requirements for virtual resource representation, proposes a plug-and-play Machine Learning Digital Twin concept, describes its software elements, and outlines directions for future research.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia Computer Science 253 (2025) 1266–1275  \n6th International Conference on Industry 4.0 and Smart Manufacturing Concept of a plug-and-play, Machine Learning Digital Twin of the production resource for Detailed Capacity Planning and Scheduling  \nMario Lubera*, Sarah Wagnerb, Marc Wegmannb, Johannes Schilpc  \naFraunhofer Institute for Casting, Composite and Processing Technology IGCV, Am Technologiezentrum 10, 86159 Augsburg, Germany bTechnical University of Munich, Institute for Machine Tools and Industrial Management, Boltzmannstraße 15, 85748 Garching, Germany c University of Augsburg, Institute of Computer Science, Am Technologiezentrum 8, 86159 Augsburg, Germany  \nAbstract  \nDue to the progress of Digital Production and the Industrial Internet of Things, continuous shop floor data is available with high coverage, accuracy, and in high detail for Production Planning and Control software. Detailed Capacity Planning and Scheduling (DCPS) can benefit by applying the Digital Twin concept and Machine Learning for an accurate and automated virtual representation of the production resource. However, the effort and difficulty required for data connection, data preparation, and modelling are high. Connection standards enable interoperability and plug-and-play software, and constitute an opportunity to reduce the effort and difficulty. This article compiles requirements regarding the virtual representation of the production resource for DCPS. It then proposes the concept of a plug-and-play, Machine Learning Digital Twin to meet these requirements. The elements of the according Digital Twin software are described, and the need for future research is identified.  \n© 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 6th International Conference on Industry 4.0 and Smart Manufacturing Keywords: Production Planning and Control, Digital Twin, Machine Learning, interoperability  \n1. Introduction  \nDetailed capacity planning and scheduling (DCPS) is a subtask of Production Planning and Control (PPC), at which production operations are allocated to production resources, capacity demand and availability are determined and aligned, and exact operation dates are scheduled [1, 2] . By this, DCPS improves the on-time delivery rate and the  \n* Corresponding author. Tel.: +49-821-90678-312 [E-mail address:](E-mail address: mario.luber@igcv.fraunhofer.de)[ mario.luber@igcv.fraunhofer.de](E-mail address: mario.luber@igcv.fraunhofer.de)  \n1877-0509 © 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 6th International Conference on Industry 4.0 and Smart Manufacturing  \n10.1016/j.procs.2025.01.188  \nMario Luber et al. / Procedia Computer Science 253 (2025) 1266–1275 1267  \nutilisation of production resources. DCPS also increases the probability of actual shop floor adherence to the production plan. Buffer times and subsequent work-in-progress inventory can therefore be kept at a low level, and suboptimal production plans resulting from replanning are avoided [1] . In sum, DCPS increases the logistical performance and efficiency of production systems. These goals are strategically important for production organisations [3] . The deployment of PPC software which include DCPS functionalities, such as Manufacturing Execution Systems (MES) and Advanced Planning and Scheduling systems (APS), is widespread [4] .  \nThe primary production resources that operations are allocated to in DCPS are work","cbCaibWzSTGnTBqp","https://ap.wps.com/l/cbCaibWzSTGnTBqp","pdf",1027144,1,10,"English","en",105,"# Abstract\n# Introduction\n## DCPS role in Production Planning and Control\n## Virtual representation of production resources\n## Digital Twin and Machine Learning via shop-floor data\n## Challenges and need for interoperability","[{\"question\":\"Why does DCPS benefit from Digital Twins and Machine Learning?\",\"answer\":\"DCPS can use Digital Twins to maintain an accurate, automated virtual representation of production resources. Machine Learning enables data-driven modelling that aligns predicted capacity parameters with real shop-floor conditions.\"},{\"question\":\"What main obstacles limit applying Digital Twins for DCPS?\",\"answer\":\"The effort and difficulty of data connection, data preparation, and modelling make implementation burdensome. Existing scientific approaches do not provide a clear solution tailored to DCPS.\"},{\"question\":\"How do connection standards support a plug-and-play Digital Twin approach?\",\"answer\":\"Connection standards enable interoperability between systems, lowering integration effort. This creates an opportunity to reduce the difficulty of connecting and using shop-floor data for modelling and decision support.\"}]","Concept of a plug-and-play, Machine Learning Digital Twin - Detailed Capacity Planning and Scheduling | PDF",1785815961,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"concept-of-a-plug-and-play-machine-learning-digital-twin-detailed-capacity-planning-and-scheduling","",{"@graph":36,"@context":85},[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/concept-of-a-plug-and-play-machine-learning-digital-twin-detailed-capacity-planning-and-scheduling/123328/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Why does DCPS benefit from Digital Twins and Machine Learning?","Question",{"text":75,"@type":76},"DCPS can use Digital Twins to maintain an accurate, automated virtual representation of production resources. Machine Learning enables data-driven modelling that aligns predicted capacity parameters with real shop-floor conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What main obstacles limit applying Digital Twins for DCPS?",{"text":80,"@type":76},"The effort and difficulty of data connection, data preparation, and modelling make implementation burdensome. Existing scientific approaches do not provide a clear solution tailored to DCPS.",{"name":82,"@type":73,"acceptedAnswer":83},"How do connection standards support a plug-and-play Digital Twin approach?",{"text":84,"@type":76},"Connection standards enable interoperability between systems, lowering integration effort. 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