Blockchain-Based Digital Traceability in Shipping and Logistics

Abstract

The maritime industry is a complex multi-tiered sector that plays a major role in international trade. Seaborne transportation is responsible for 90% of goods moved globally, and containerized shipments account for more than 50% of those goods. This volume of movement requires an efficient tracking and traceability mechanism that secures the documentation process and ensures the involved stakeholders. Most of today’s systems and technologies leveraged for managing shipping containers in unimodal and multimodal logistics fall short of providing transparency, traceability, reliability, audit, security, and trust features. Moreover, due to its complexity, the maritime industry suffers from a lack of trust, lack of secure ownership evidence, protracted documentation procedures, and excessive data aggregation. These shortcomings are reflected in cargo processing delays and elevated costs in the shipping process. To mitigate these problems, we propose in this thesis a blockchain-based solution for the traceability and ownership history provenance of shipping containers in unimodal and multimodal logistics. Blockchain is an immutable, transparent, and decentralized ledger that secures its transactions with hashing algorithms. Furthermore, we propose a solution that utilizes smart contracts and non-fungible tokens (NFTs) on the Ethereum blockchain to accelerate the inefficient shipping process by digitizing process documentation and ownership records and transfers. The proposed system allows the owners to auction off their cargo at the destination ports, freeing warehouses and containers in the process. Finally, we integrate digital twins (DTs) and NFTs for secure real-time shipment data updates and the traceability of the shipping containers and their contents. In this thesis, we analyze the proposed systems in terms of transaction costs and smart contract security. The generated reports emphasize the efficiency and resiliency of the proposed systems, as well as the cost savings and significant process security enhancements.

Date of Award	Dec 2022
Original language	American English
Supervisor	Khaled Salah (Supervisor)