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

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

Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization

[EN] There are some studies and methods in the literature to understand customer needs and behaviors from the path. However, path analysis has a complex structure because the many customers can follow many different paths. Therefore, clustering methods facilitate the analysis of the customer location data to evaluate customer behaviors. Therefore, we aim to understand customer behavior by clustering their paths. We use an intuitionistic fuzzy c-means clustering (IFCM) algorithm for two-dimensional indoor customer data; case durations and the number of visited locations. Customer location data was collected by Bluetooth-based technology devices from one of the major shopping malls in Istanbul. Firstly, we create customer paths from customer location data by using process mining that is a technique that can be used to increase the understandability of the IFCM results. Moreover, we show with this study that fuzzy methods and process mining technique can be used together to analyze customer paths and gives more understandable results. We also present behavioral changes of some customers who have a different visit by inspecting their clustered paths.Dogan, O.; Oztaysi, B.; Fernández Llatas, C. (2020). Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization. Journal of Intelligent & Fuzzy Systems. 38(1):675-684. https://doi.org/10.3233/JIFS-179440675684381Dogan, O., Bayo-Monton, J.-L., Fernandez-Llatas, C., & Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors, 19(3), 557. doi:10.3390/s19030557Dogan, O., & Oztaysi, B. (2019). Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN. Expert Systems with Applications, 136, 42-49. doi:10.1016/j.eswa.2019.06.029Dogan, O., & Öztaysi, B. (2018). 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