Integrated Parcel Management System with ML-based Resource Analysis for Goods Transport

Abstract

The rapid growth of e-commerce and logistics services has increased the demand for efficient parcel transportation systems. However, the logistics sector continues to face challenges such as inefficient capacity utilization, poor financial planning, suboptimal route management, high operational costs, and limited scalability. The absence of predictive analytics further reduces operational efficiency and decision-making capabilities. This study proposes an integrated parcel management system (IPMS) powered by machine learning (ML)-based resource analysis to enhance logistics performance. The system integrates predictive demand forecasting, capacity optimization, expense monitoring, fleet tracking, and route optimization within a unified framework. Real-time data processing enables intelligent resource allocation and operational automation. The proposed architecture improves vehicle utilization, reduces transportation costs, enhances supply chain coordination, and supports scalable logistics operations. Experimental analysis using simulated logistics datasets demonstrates improvements in route efficiency, cost reduction, and service reliability. The proposed IPMS offers a data-driven decision-support system for modern logistics and goods transportation, aligned with Industry 4.0 principles.