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Experimental Characterisation of Tool Hardness Evolution Under Consideration of Process Relevant Cyclic Thermal and Mechanical Loading During Industrial Forging Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Definition of Process Performance Indicators for the Application of Process Mining in End-to-End Order Processing Processes

Authors : S. Schmitz, F. Renneberg, S. Cremer, A. Gützlaff, G. Schuh

Published in: Production at the leading edge of technology

Publisher: Springer Berlin Heidelberg

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Abstract

Todays’ increasing market volatility and product variety result in growing business processes complexity. To master the arisen challenges of high process complexity, the process performance description of the end-to-end order processing process (ETEOPP) is crucial. With the trend of digitalization in manufacturing companies, an increasing availability of data is created that can be used to master process complexity by data-based methods. One suitable method is process mining (PM), which offers a continuous analysis of event data from business information systems. This paper aims to describe the minimum viable dataset to thoroughly evaluate the process performance in ETEOPP by PM. Therefore, process performance indicators (PPI) are first scientifically derived by a systematic literature review and afterward defined across the ETEOPP as quantifiable parameters based on processes and data. By doing so, the required event log attributes, as well as corresponding data requirements, are presented and information systems for data extraction are pointed out.

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Metadata
Title
Definition of Process Performance Indicators for the Application of Process Mining in End-to-End Order Processing Processes
Authors
S. Schmitz
F. Renneberg
S. Cremer
A. Gützlaff
G. Schuh
Copyright Year
2021
Publisher
Springer Berlin Heidelberg
Book
Production at the leading edge of technology

Print ISBN: 978-3-662-62137-0

Electronic ISBN: 978-3-662-62138-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-662-62138-7_67

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