Analysis of a Logistics Process Based on the Event Log

This article presents an analysis of a logistics process using process mining methods. Additionally, it highlights the possibilities of analyzing industrial process data using the process mining tools available in the ProM software 6.12. The paper explains what process mining is, its types and persp...

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Vydané v:Applied sciences Ročník 15; číslo 16; s. 8968
Hlavní autori: Napieraj, Aneta, Pyzik, Natalia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.08.2025
Predmet:
Algorithms
Case studies
event log
Information systems
Logistics
logistics process
Performance evaluation
process analysis
process data analysis
process mining
process mining methods
Readability
Sensitivity analysis
Poland
Germany
ISSN:2076-3417, 2076-3417
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Popis
Shrnutí:This article presents an analysis of a logistics process using process mining methods. Additionally, it highlights the possibilities of analyzing industrial process data using the process mining tools available in the ProM software 6.12. The paper explains what process mining is, its types and perspectives, and its potential applications in business process management. This analysis is based on an event log, while the process model is presented in the form of a Petri net. The process research was carried out using ProM software and its available tools. The dataset is characterized, and the results of the conducted studies are presented, including an analysis of the event log, process flow network analysis, and conformance checking between the model and the event log, as well as time-based, organizational, and error detection analyses. Solutions to the identified problems are proposed. In summary, this article presents an analysis of a logistics process that was generated based on system process data, and demonstrates the possibilities of using selected algorithms from the ProM software.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app15168968