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Analysing Process Variants against de-facto Processes at the Example of Global IT Management at Miele

Informationen zur Arbeit

Abschlussarbeitstyp: Bachelor, Master
Betreuer: Andreas OberweisClemens Schreiber
Forschungsgruppe: Betriebliche Informationssysteme
Partner: Bee360Miele
Archivierungsnummer: 4808
Abschlussarbeitsstatus: Offen
Beginn: 29. Juli 2021
Abgabe: unbekannt

Weitere Informationen

Business Process Variant analysis is an important approach in process mining and can be seen as an extension to automated process discovery, conformance checking and process enhancement. A process variant is a subset of process executions, which can be identified by the a similarity analysis of process traces in an event log. The main goal of this thesis is to identify and to analyse process variants in order to answer questions like how, why and when do variants occur. These questions will be answered by the example of Miele, a globally active manufacturer of domestic appliances. The foundation for process mining is provided by Miele’s global IT Management platform Bee4IT. Bee4IT provides the basis for corporate process documentation and integrates various tools like GitLab, Jira, Confluence, and SAP. An intermediate target will be to describe requirements for making Bee4IT mining-ready. Common approaches to identify process variants are: (1) Machine learning (e.g., clustering, decision trees), (2) Specific Process Mining algorithms (e.g., fuzzy miner, causal relation analysis).

The outcome of the analysis can be rule-based, model-based or descriptive. Related open questions are: How to choose an appropriate algorithm for variant discovery? How to identify causes and dependencies between the process variants? How to adapt the analysis according to the case study?

Relevant Literature: [1] Van Eck, M. L., Lu, X., Leemans, S. J., & Van Der Aalst, W. M. (2015, June). PM^2: a process mining project methodology. In International Conference on Advanced Information Systems Engineering (pp. 297-313). Springer, Cham. [2] Suriadi, S., Andrews, R., ter Hofstede, A. H., & Wynn, M. T. (2017). Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Information systems, 64, 132-150.