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Auflistung nach Autor:in "Fahland, Dirk"

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  • Zeitschriftenartikel
    Automating Data Exchange in Process Choreographies.
    (EMISA Forum: Vol. 36, No. 2, 2016) Fahland, Dirk; Meyer, Andreas; Pufahl, Luise; Batoulis, Kimon; Weske, Mathias
  • Zeitschriftenartikel
    Discovering Interacting Artifacts from ERP Systems (Extended Abstract).
    (EMISA Forum: Vol. 36, No. 2, 2016) Fahland, Dirk; Lu, Xixi; Nagelkerke, Marijn; Wiel, Dennis van de
  • Zeitschriftenartikel
    Dynamic Skipping and Blocking and Dead Path Elimination for Cyclic Workflows.
    (EMISA Forum: Vol. 38, No. 1, 2018) Fahland, Dirk; Völzer, Hagen
  • Zeitschriftenartikel
    Process Mining for Six Sigma
    (Business & Information Systems Engineering: Vol. 63, No. 3, 2021) Graafmans, Teun; Turetken, Oktay; Poppelaars, Hans; Fahland, Dirk
    Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management.