Article

Integrating 5PL Frameworks with Drone-Based Last-Mile Delivery: A Model for Future-Ready Logistics

Tejinder Singh Lakhwani ORCID
    Received
    17 May 2025
    Published
    30 Jun 2025

    Abstract

    The rapid evolution of logistics service providers from 1PL to 5PL has underscored the growing need for intelligent, data-driven orchestration across the supply chain. Simultaneously, drone-based delivery has emerged as a promising solution to last-mile challenges, particularly in urban congestion zones and infrastructure-deficient rural areas. However, current deployments of drone logistics remain largely siloed and unintegrated with broader digital logistics platforms. This paper proposes a novel conceptual framework that embeds drone-based last-mile delivery within the orchestration architecture of fifth-party logistics (5PL) systems. Leveraging a multi-agent digital twin model, the study integrates technologies such as IoT for real-time tracking, AI-based metaheuristics (ALNS, PSO, NSGA-II) for route and hub optimization, and blockchain for SLA compliance. A simulation case based on India’s rural healthcare supply chain and ONDC clusters demonstrates substantial improvements in delivery time (↓65%), operational cost (↓40%), and carbon footprint (↓90%) over conventional 4PL systems. Sensitivity analyses under weather fluctuations, demand surges, and battery degradation validate the model’s resilience and adaptability. The findings position 5PLs as future-ready orchestrators of autonomous delivery systems and offer actionable insights for policymakers, supply chain managers, and technology developers toward building sustainable and scalable drone logistics ecosystems.The framework emphasizes
    interoperability and modular deployment, ensuring ease of integration with evolving logistics platforms.

    Keywords

    Blockchain-Orchestrated Logistics, IoT in Supply Chains, AI-Enabled Logistics Optimization, Drone-Based Last-Mile Delivery, Fifth-Party Logistics (5PL), Sustainable Logistics Frameworks

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