Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project
The study aims to optimize internal logistics processes by applying Lean philosophy and data science tools, with a primary focus on qualifying processes to determine their value-added contribution within the logistics context. Utilizing a novel two-step methodology, the research first employs a modi...
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| Format: | Article |
| Language: | English |
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Ital Publication
2025-02-01
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| Series: | Emerging Science Journal |
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| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2572 |
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| author | András Rácz-Szabó Tamás Ruppert János Abonyi |
| author_facet | András Rácz-Szabó Tamás Ruppert János Abonyi |
| author_sort | András Rácz-Szabó |
| collection | DOAJ |
| description | The study aims to optimize internal logistics processes by applying Lean philosophy and data science tools, with a primary focus on qualifying processes to determine their value-added contribution within the logistics context. Utilizing a novel two-step methodology, the research first employs a modified DBSCAN algorithm to analyze indoor positioning data and categorize activities. This is followed by multi-layer network modeling to understand processes and create a framework that enables the reduction of idle activities through optimization algorithms. A real warehouse case study, using a UWB-based Indoor Positioning System (IPS) to track forklifts, demonstrates the method's effectiveness in identifying non-value-added activities. The results reveal specific opportunities for reducing idle, enhancing resource utilization, and improving operational efficiency. This innovative combination of advanced data analysis techniques and Lean principles provides a comprehensive framework for logistics optimization, significantly enhancing process efficiency through optimized task scheduling and resource allocation.
Doi: 10.28991/ESJ-2025-09-01-013
Full Text: PDF |
| format | Article |
| id | doaj-art-f5aeec3aec99453189599b20716a26c6 |
| institution | Kabale University |
| issn | 2610-9182 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-f5aeec3aec99453189599b20716a26c62025-02-08T14:26:27ZengItal PublicationEmerging Science Journal2610-91822025-02-019122924210.28991/ESJ-2025-09-01-013773Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean ProjectAndrás Rácz-Szabó0Tamás Ruppert1János Abonyi21) HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Egyetem str. 10, POB 158, H-8200 Veszprém, Hungary. 2) Department of Process Engineering, University of Pannonia, Egyetem str. 10, POB 158, H-8200 Veszprém, Hungary.1) HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Egyetem str. 10, POB 158, H-8200 Veszprém, Hungary. 3) Department of System Engineering, University of Pannonia, Egyetem str. 10, POB 158, H-8200 Veszprém, Hungary.1) HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Egyetem str. 10, POB 158, H-8200 Veszprém, Hungary. 2) Department of Process Engineering, University of Pannonia, Egyetem str. 10, POB 158, H-8200 Veszprém, Hungary.The study aims to optimize internal logistics processes by applying Lean philosophy and data science tools, with a primary focus on qualifying processes to determine their value-added contribution within the logistics context. Utilizing a novel two-step methodology, the research first employs a modified DBSCAN algorithm to analyze indoor positioning data and categorize activities. This is followed by multi-layer network modeling to understand processes and create a framework that enables the reduction of idle activities through optimization algorithms. A real warehouse case study, using a UWB-based Indoor Positioning System (IPS) to track forklifts, demonstrates the method's effectiveness in identifying non-value-added activities. The results reveal specific opportunities for reducing idle, enhancing resource utilization, and improving operational efficiency. This innovative combination of advanced data analysis techniques and Lean principles provides a comprehensive framework for logistics optimization, significantly enhancing process efficiency through optimized task scheduling and resource allocation. Doi: 10.28991/ESJ-2025-09-01-013 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2572indoor positioning systemposition datawarehouseclusteringmulti-layer network. |
| spellingShingle | András Rácz-Szabó Tamás Ruppert János Abonyi Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project Emerging Science Journal indoor positioning system position data warehouse clustering multi-layer network. |
| title | Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project |
| title_full | Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project |
| title_fullStr | Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project |
| title_full_unstemmed | Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project |
| title_short | Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project |
| title_sort | clustering and network analysis of mobility patterns as an analysis tool for lean project |
| topic | indoor positioning system position data warehouse clustering multi-layer network. |
| url | https://ijournalse.org/index.php/ESJ/article/view/2572 |
| work_keys_str_mv | AT andrasraczszabo clusteringandnetworkanalysisofmobilitypatternsasananalysistoolforleanproject AT tamasruppert clusteringandnetworkanalysisofmobilitypatternsasananalysistoolforleanproject AT janosabonyi clusteringandnetworkanalysisofmobilitypatternsasananalysistoolforleanproject |