Data-based description of process performance in end-to-end order processing

Günther Schuh (Corresponding author), Andreas Gützlaff, Seth Schmitz, Wil M.P. van der Aalst

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

To master ongoing market competitiveness, manufacturing companies try to increase process efficiency through process improvements. Mapping the end-to-end order processing is particularly important, as one needs to consider all order-fulfilling core processes to evaluate process performance. However, today's traditional process mapping methods such as workshops are subjective and time-consuming. Therefore, process improvements are based on gut feeling rather than facts, leading to high failure probabilities. This paper presents a process mining approach that provides data-based description of process performance in order processing and thus objectively and effortlessly maps as-is end-to-end processes. The approach is validated with an industrial case study.

Original languageEnglish
Pages (from-to)381-384
Number of pages4
JournalCIRP Annals
Volume69
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • Machine learning
  • Performance
  • Process
  • Process mining

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