Digital Fleet Optimization for Auctioned Lease Returns: A Data-Driven Framework for Enhanced Operational Efficiency

Abstract

The management of auctioned lease returns in automotive fleet operations presents significant challenges including fragmented data architectures, static decision-making processes, and limited scalability. This paper proposes a comprehensive digital framework that leverages artificial intelligence, Internet of Things sensors, and distributed ledger technology to address these inefficiencies. The framework employs predictive analytics for demand forecasting, dynamic algorithms for fleet allocation, and real-time optimization for routing decisions. Built on cloud-native microservices architecture, the system integrates vehicle telematics, blockchain-based transaction verification, and automated reconciliation processes to create an end-to-end solution. The proposed framework demonstrates potential for substantial improvements in operational efficiency, including reduced delivery times, decreased manual intervention requirements, and optimized resource utilization. Additionally, the system provides enhanced transparency through immutable transaction records and contributes to environmental sustainability through route optimization that minimizes fuel consumption and carbon emissions. By transitioning from reactive to proactive fleet management, this framework offers a strategic approach to transforming automotive logistics operations, positioning organizations to achieve competitive advantages through data-driven decision-making and intelligent automation.