Time Series Modeling for the Development of a Systematic, Cost Effective, and ML-Supported Cargo Tracking System: Optimizing Supply Chain Efficiency

This study proposes a time series model by utilizing machine learning to enhance cargo tracking cost-effectively. It aims to optimize supply chains by identifying minimum-cost routes and integrating demand forecasting strategies. Through time series analysis, the proposed model predicts transportation costs and demand fluctuations. Real-time decision-making is facilitated by machine learning, allowing adaptation to changing conditions. Key components include algorithms for route optimization, facility assignment, and demand forecasting, all geared towards minimizing operational costs and establishing a resilient supply chain. This research study provides valuable insights for businesses adopting a data-driven approach to logistics with a strong emphasis on efficiency and sustainability.