[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127218-en":3,"doc-seo-127218-105":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},127218,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning","Business process management uses business process redesign (BPR) to improve processes, but traditional BPR has relied heavily on manual work and remains constrained by labor and time. Growing volumes of process data and advances in generative machine learning (GML) enable automated BPR. Existing methods largely operate offline on historical samples, motivating runtime redesign. The research-in-progress paper develops a design-science approach for a GML-based technique and reports initial promising results for process model prediction from real-life event data.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ECIS 2024 Proceedings | European Conference on Information Systems\u003Cbr>(ECIS) |\n| --- | --- |\n| June 2024\u003Cbr>Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning\u003Cbr>Maximilian Victor Harl\u003Cbr>ETH Zurich, [maximilian.harl@fau.de](maximilian.harl@fau.de)\u003Cbr>Sandra Zilker\u003Cbr>Technische Hochschule Nuremberg, [sandra.zilker@fau.de](sandra.zilker@fau.de)\u003Cbr>Sven Weinzierl\u003Cbr>Friedrich-Alexander University Erlangen-Nuremberg, [sven.weinzierl@fau.de](sven.weinzierl@fau.de)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/ecis2024](https://aisel.aisnet.org/ecis2024) |  |\n\nRecommended Citation  \nHarl, Maximilian Victor; Zilker, Sandra; and Weinzierl, Sven, \"Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning\" (2024) . ECIS 2024 Proceedings. 7.  \n[https://aisel.aisnet.org/ecis2024/track07_busanalytics/track07_busanalytics/7](https://aisel.aisnet.org/ecis2024/track07_busanalytics/track07_busanalytics/7)  \nThis material is brought to you by the European Conference on Information Systems (ECIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ECIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nTOWARDS AUTOMATED BUSINESS PROCESS REDESIGN IN RUNTIME USING GENERATIVE MACHINE LEARNING  \nShort Paper  \nMaximilian Harl, ETH Zurich, Zurich, Switzerland, [mharl01@student.ethz.ch](mharl01@student.ethz.ch)  \nSandra Zilker, TH Nürnberg, Nuremberg, Germany, [sandra.zilker@th-nuernberg.de](sandra.zilker@th-nuernberg.de)[ ](sandra.zilker@th-nuernberg.de)Sven Weinzierl, FAU Erlangen-Nürnberg, Nuremberg, Germany, [sven.weinzierl@fau.de](sven.weinzierl@fau.de)  \nAbstract  \nIn business process management, business process redesign (BPR) aims to improve business processes. In the past, BPR was mainly a manual task, with little computational power and typically high labor and time intensity. The increasing amount of stored process data and great advancements in generative machine learning (GML) and other analytical approaches have paved the way for automated BPR. However, existing BPR approaches are designed for offline applications and therefore restricted to computing historical data samples of business processes. In this paper, we argue performing BPR in runtime and leveraging prediction capabilities via GML achieves a higher degree of BPR automation, allowing organizations to improve their processes proactively. Accordingly, this research-in-progress paper outlinesa design-science research process for designing a GML-based technique for automated BPR in runtime.  \nIn our preliminary evaluation, we present promising results for the proposed technique’s first online task, namely process model prediction, based on real-life event data.  \nKeywords: Business process redesign, business process management, generative artificial intelligence.  \n1 Introduction  \nOrganizations operate in a volatile economic environment (Beverungen et al., 2021), characterized by political instability (e.g., Carmignani, 2003) or rising customer expectations (e.g., Kreuzer, Röglinger, and Rupprecht, 2020) . At the same time, business processes are the organizational backbone for value creation (Dumas et al., 2018) . Consequently, business processes need to be flexible and organizations are forced to steadily change their business processes to tackle influences emerging in the ever-changing environment (Beverungen et al., 2021) . Business process redesign (BPR) has been established in the domain of business process management (BPM) to improve business processes (Dumas et al., 2018) . While the general idea of BPM is to improve business processes incrementally and cyclically (van der Aalst, La Rosa, and Santoro, 2016), BPR is generally considered t","cbCaiaIgjI9F0yJu","https://ap.wps.com/l/cbCaiaIgjI9F0yJu","pdf",479159,1,9,"English","en",105,"# Introduction\n## Business process management and BPR background\n## Limitations of manual and offline BPR approaches\n## Motivation for runtime BPR with GML\n# Research-in-progress approach\n## Design-science process for a GML-based technique\n# Preliminary evaluation\n## First online task: process model prediction","[{\"question\":\"Why does the paper argue for performing BPR in runtime instead of offline?\",\"answer\":\"Existing automated BPR methods focus on offline computation using historical data samples. Runtime execution enables organizations to use prediction capabilities to improve processes proactively as events unfold.\"},{\"question\":\"What role does generative machine learning play in the proposed technique?\",\"answer\":\"GML is used to learn the underlying distribution of process data, supporting prediction tasks that drive automated redesign decisions during runtime.\"},{\"question\":\"What does the preliminary evaluation cover?\",\"answer\":\"The paper reports promising results for the technique’s first online task: process model prediction based on real-life event data.\"}]","Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning | PDF",1785937595,23,{"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},"towards-automated-business-process-redesign-in-runtime-using-generative-machine-learning","",{"@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/towards-automated-business-process-redesign-in-runtime-using-generative-machine-learning/127218/",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","2026-08-05",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 the paper argue for performing BPR in runtime instead of offline?","Question",{"text":75,"@type":76},"Existing automated BPR methods focus on offline computation using historical data samples. Runtime execution enables organizations to use prediction capabilities to improve processes proactively as events unfold.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does generative machine learning play in the proposed technique?",{"text":80,"@type":76},"GML is used to learn the underlying distribution of process data, supporting prediction tasks that drive automated redesign decisions during runtime.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the preliminary evaluation cover?",{"text":84,"@type":76},"The paper reports promising results for the technique’s first online task: process model prediction based on real-life event data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]