A process mining approach for production flow analysis

Tomidei, Laura

A process mining approach for production flow analysis

Tomidei, Laura

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Tomidei, Laura
dc.date.accessioned	2025-05-26T02:51:13Z
dc.date.available	2025-05-26T02:51:13Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/187504
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Lean Management principles and methods have been adopted by manufacturing companies for decades, as they enable the design, planning, and control of efficient production systems. One of the key principles of Lean Management and essential requirement for the effective application of Production Planning and Control tasks is the identification of value streams in production. Traditionally, this task could be addressed by using the established principles of Production Flow Analysis, a technique introduced by Burbidge in the 1970s that enables the identification of product families and associated processing steps. However, the increasing complexity of production systems pushes the direct application of these principles to their limits. At the same time, the increasing adoption of Industry 4.0 technologies has unlocked the opportunity to extract increasing amounts of data and unlock valuable insights using Data Analytics capabilities. Against this backdrop, this research provides a method for automatically identifying value streams from production data building on the principles of Production Flow Analysis and enhancing them with Machine Learning and Process Mining techniques. By providing a solution for effectively using production data and accurately identifying value streams, the method represents a novel contribution to both academia and industry.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/187504/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Laura Tomidei
dc.rights	au.edu.uts.lib/nph
dc.title	A process mining approach for production flow analysis	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Lean Management principles and methods have been adopted by manufacturing companies for decades, as they enable the design, planning, and control of efficient production systems. One of the key principles of Lean Management and essential requirement for the effective application of Production Planning and Control tasks is the identification of value streams in production. Traditionally, this task could be addressed by using the established principles of Production Flow Analysis, a technique introduced by Burbidge in the 1970s that enables the identification of product families and associated processing steps. However, the increasing complexity of production systems pushes the direct application of these principles to their limits. At the same time, the increasing adoption of Industry 4.0 technologies has unlocked the opportunity to extract increasing amounts of data and unlock valuable insights using Data Analytics capabilities. Against this backdrop, this research provides a method for automatically identifying value streams from production data building on the principles of Production Flow Analysis and enhancing them with Machine Learning and Process Mining techniques. By providing a solution for effectively using production data and accurately identifying value streams, the method represents a novel contribution to both academia and industry.

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http://hdl.handle.net/10453/187504