Leveraging business process mining to obtain business intelligence and improve organizational performance

Zandkarimi, Fareed

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URN: urn:nbn:de:bsz:180-madoc-665713
Document Type: Doctoral dissertation
Year of publication: 2023
Place of publication: Mannheim
University: Universität Mannheim
Evaluator: Hoehle, Hartmut
Date of oral examination: 14 December 2023
Publication language: English
Institution: Business School > Enterprise Systems (Höhle 2017-)
Subject: 004 Computer science, internet
Keywords (English): business process mining
Abstract: The utilization of process mining event logs has emerged as a pivotal strategy for organizations to achieve business intelligence, comprehend their processes, pinpoint inefficiencies, and assess performance. By enhancing process mining event logs with contextual data such as production and error reporting data, organizations can derive more profound insights into their operations, highlighting best practices, successful process paths, and areas for performance improvement. This dissertation explores the intrinsic value of analyzing event logs to acquire business intelligence and enhance organizational performance. Drawing on previous research in event log analysis, particularly in process discovery, this study aims to delve into untapped potentials within event logs, striving to extract comprehensive insights. Additionally, this research extends its scope to contribute to the domains of business intelligence and organizational behavior, focusing on organizational routines, routine performance, and error management. Through an exploratory journey comprising four papers, this study addresses the overarching research question by advancing the understanding of event logs in process mining and organizational behavior. The first paper introduces a framework for trace clustering, highlighting the substantial potential of event log analysis. The second paper proposes a process inefficiency index based on identifying unwanted patterns in process execution. The third paper offers a unified library of process measures, fostering further exploration of event logs. Finally, the fourth paper explores performance changes in response to errors in production processes, leveraging event log data to conduct a natural experiment. Through this multifaceted approach, this research enriches the process mining and organizational studies fields, contributing to understanding organizational routines and performance enhancement.

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