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Applying and Refining a Project Mining Method at the Example of the Global Miele Call Center

Informationen zur Arbeit

Abschlussarbeitstyp: Bachelor, Master
Betreuer: Andreas OberweisClemens Schreiber
Forschungsgruppe: Betriebliche Informationssysteme
Partner: Bee360Miele
Archivierungsnummer: 4807
Abschlussarbeitsstatus: Offen
Beginn: 29. Juli 2021
Abgabe: unbekannt

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There exist a number of project methodologies, which have been developed to specifically guide process mining projects: e.g., Process Diagnostic Method, L*life-cycle model, PM². Each method consists of different stages and has to be adopted to the particular context. The main goal of this thesis is to apply and adapt a chosen project mining methodology to the empirical context of the global Miele call center. The outcomes are two-fold: (1.) apply a PM method and critically reflect and document related challenges and identified solutions within the context of this thesis and (2.) make the Miele Call Center processes explicit, identify KPIs, and propose managerial implications for raising efficiency and/or quality.

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. Related open questions are: What are the advantages and disadvantages of these methods? How do these methods compare to other more general project management methods? How could the methods be improved (e.g., in terms of agility)? How suitable are these methodologies in the context of IT management at the example of the Miele Call Center processes?

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.