Implementation of Business Process Mining using
Celonis

Στυλιανού, Ανδρέας; Stylianou, Andreas

dc.contributor.author	Στυλιανού, Ανδρέας	el
dc.contributor.author	Stylianou, Andreas	en
dc.date.accessioned	2025-10-16T06:15:09Z
dc.date.available	2025-10-16T06:15:09Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62718
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.30414
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Εξόρυξη	el
dc.subject	Εξόρυξη Διαδικασιών	el
dc.subject	Λογισμικό	el
dc.subject	Διαδικασιών	el
dc.subject	Εξόρυξη Δεδομένων	el
dc.subject	Software	en
dc.subject	Celonis	en
dc.subject	Data Mining	en
dc.subject	Process Mining	en
dc.subject	Process	en
dc.title	Implementation of Business Process Mining using Celonis	en
dc.contributor.department	Τομέας Βιομηχανικής Διοίκησης & Επιχειρησιακής Έρευνα	el
heal.type	bachelorThesis
heal.classification	Industrial Engineering	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2025-06
heal.abstract	This thesis investigates the application of process mining techniques, with a particular focus on the Celonis platform, to address inefficiencies in business processes within modern organizations. Traditional Business Process Management (BPM) approaches often rely on static models and subjective inputs, which can lead to suboptimal process design and limited adaptability in dynamic business environments. While Business Intelligence (BI) systems offer data-driven insights, they are frequently constrained by their dependence on structured data and lack the contextual analysis necessary for actionable improvements. In contrast, process mining leverages event logs generated by information systems to objectively discover, monitor, and enhance actual business processes. By bridging the gap between operational data and process models, process mining enables organizations to perform conformance checking, detect deviations, and uncover bottlenecks, thereby supporting data-driven decision-making and continuous improvement. The thesis presents a comprehensive review of process mining theory, tools, and methodologies, followed by an in-depth case study utilizing Celonis to analyze real-world business processes . The results demonstrate the effectiveness of process mining in identifying inefficiencies, supporting compliance, and providing actionable recommendations for process optimization. The findings underscore the growing importance of process mining in the digital transformation of organizations, highlighting its potential to enhance operational efficiency, reduce costs, and foster a culture of data-driven management. Limitations and future research directions are also discussed, emphasizing the need for improved data quality and further integration of process mining with emerging technologies.	en
heal.advisorName	Παναγιώτου, Νίκος
heal.committeeMemberName	Πόνης, Σταύρος
heal.committeeMemberName	Χατζηστέλιος, Γεώργιος
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Μηχανολόγων Μηχανικών. Τομέας Βιομηχανικής Διοίκησης και Επιχειρησιακής Έρευνας	el
heal.academicPublisherID	ntua
heal.numberOfPages	138
heal.fullTextAvailability	false