Automated OCEL Transformation for Real-Time Conformance in Complex Manufacturing

Automated OCEL Transformation for Real-Time Conformance in Complex Manufacturing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Automated OCEL Transformation for Real-Time Conformance in Complex Manufacturing Michael Maiko Matonya, István Budai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9001426/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Object-Centric Process Mining (OCPM) offers a powerful framework for analyzing multi-object interactions in complex manufacturing systems; however, its practical deployment remains constrained by the labor-intensive transformation of heterogeneous production data into Object-Centric Event Logs (OCELs). This study addresses that bottleneck by developing and empirically validating an automated transformation pipeline capable of converting raw industrial event streams into conformance-ready OCELs with 97.5% role-assignment accuracy. Unlike static mapping approaches, the proposed architecture integrates a multi-criteria decision-making (MCDM) layer to systematically evaluate and select classifiers and process discovery algorithms under competing performance criteria, including precision, robustness, latency, and model fitness. An uncertainty-aware active learning mechanism further reduces manual intervention by requesting expert validation only when entropy thresholds exceed predefined bounds. Experimental validation was conducted using a large-scale dataset from HewSaw timber manufacturing operations comprising 54,976 events across 2,000 production cases. Results indicate that GA-optimized XGBoost provides superior balance across accuracy and operational throughput, while Inductive Miner achieves the highest fitness (96.7%) and generalization (90.1%) for process discovery. Furthermore, integrating object-centric process features with traditional operational indicators improves real-time conformance prediction accuracy from 75.6% to 92.4% (AUC = 0.934), demonstrating the predictive value of multi-object contextual modeling. With average transformation latencies of approximately 1.27 ms per event, the pipeline satisfies real-time deployment constraints. The findings suggest that systematic algorithm selection combined with object-centric feature integration enables scalable, high-fidelity conformance monitoring in complex manufacturing environments, effectively reducing reliance on manual data preparation Physical sciences/Engineering Physical sciences/Mathematics and computing Production Data Transformation Conformance Analysis Object-Centric Process Mining Multi-Criteria Decision Making Real-Time Monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 14 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Editor invited by journal 09 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 04 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9001426","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":606125488,"identity":"1429f6aa-63b5-4e89-a1ae-548fc7d9ae61","order_by":0,"name":"Michael Maiko Matonya","email":"","orcid":"","institution":"University of Debrecen","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"Maiko","lastName":"Matonya","suffix":""},{"id":606125489,"identity":"1c0b8c54-d25e-4103-93d2-738643e93cc6","order_by":1,"name":"István Budai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACAxBKYJCA8D5ARQ/wMBwASeHXwgPkMM4gWgsIgLQw88CE8Wkxl0je+OEBg4W9vUTys8c2FXfsGiRyDx54U3EHKJWAVYvljLRioJREYo9EmrlxzplnyQ0SeQkH55x5BpTCrsXgRo4BSEsCD88BM+nctsPJDBI5Bod52w4DpXBqMf4B1GLPw3P8m7TlP5iWf3i1mIFsYexh7zGTZmw4bAfR0oBbi2XPszKLBAOgX473lEn2HDucwMbzxuDgnGPPeAzOPMAeYuzJm2/+qKizZ29m3ybxo+awPT97jvGHNzV35AyOY7cF6jwEM7ENyuDBqhIbsCda5SgYBaNgFIwYAABac17l3RRLRwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Debrecen","correspondingAuthor":true,"prefix":"","firstName":"István","middleName":"","lastName":"Budai","suffix":""}],"badges":[],"createdAt":"2026-03-01 12:28:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9001426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9001426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104694189,"identity":"236165a2-7aed-4951-a3e1-bea5fa510742","added_by":"auto","created_at":"2026-03-16 07:03:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2030448,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscriptAutomatedOCELTransformationforRealTime.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9001426/v1_covered_5b0cac63-bb77-472f-87d4-4f4ceac4cc30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automated OCEL Transformation for Real-Time Conformance in Complex Manufacturing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Production Data Transformation, Conformance Analysis, Object-Centric Process Mining, Multi-Criteria Decision Making, Real-Time Monitoring","lastPublishedDoi":"10.21203/rs.3.rs-9001426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9001426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Object-Centric Process Mining (OCPM) offers a powerful framework for analyzing multi-object interactions in complex manufacturing systems; however, its practical deployment remains constrained by the labor-intensive transformation of heterogeneous production data into Object-Centric Event Logs (OCELs). This study addresses that bottleneck by developing and empirically validating an automated transformation pipeline capable of converting raw industrial event streams into conformance-ready OCELs with 97.5\\% role-assignment accuracy.\n\nUnlike static mapping approaches, the proposed architecture integrates a multi-criteria decision-making (MCDM) layer to systematically evaluate and select classifiers and process discovery algorithms under competing performance criteria, including precision, robustness, latency, and model fitness. An uncertainty-aware active learning mechanism further reduces manual intervention by requesting expert validation only when entropy thresholds exceed predefined bounds.\n\nExperimental validation was conducted using a large-scale dataset from HewSaw timber manufacturing operations comprising 54,976 events across 2,000 production cases. 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