Abstract
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
Published Version
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