Clustering and Network Analysis of Mobility Patterns as an Analysis Tool for Lean Project

https://doi.org/10.28991/ESJ-2025-09-01-013
Indoor Positioning System Position Data Warehouse Clustering Multi-Layer Network.

Authors

  • András Rácz-Szabó 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.
  • Tamás Ruppert
    ruppert@abonyilab.com
    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. https://orcid.org/0000-0001-9441-843X
  • János Abonyi 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.
Vol. 9 No. 1 (2025): February
Research Articles

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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

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