A team of researchers from Khalifa University in UAE has developed a blockchain platform that utilizes digital twins and dynamic Non-Fungible tokens ( NFTs) to revolutionize last mile delivery in e-commerce market. The platform support the monitoring of packages and their security.

Their innovative approach tackles the complex needs of this final step in the delivery process from a distribution center to the recipient by leveraging smart contracts and real-time monitoring capabilities.

Feruz Elmay, Dr. Maha Kadadha, Dr. Rabeb Mizouni, Dr. Shakti Singh, Prof. Hadi Otrok and Prof. Azzam Mourad are all part of Khalifa University’s Center on Cyber-Physical Systems (C2PS). They published their research in Information Processing and Management, a top 1% journal.

It is also often the most complex and expensive part of the supply chain. Last-mile delivery involves navigating local roads, dealing with traffic, and meeting customer expectations for rapid delivery, and as e-commerce continues to boom, the demand for efficient and cost-effective last-mile solutions has never been higher.

Prof. Hadi Otrok, Professor of Computer Science at Khalifa University stated, “Our work aims to bridge the gaps in last-mile delivery by integrating blockchain with digital twins for real-time monitoring and transparency. This approach not only enhances package security and efficiency but also sets a new standard for handling sensitive goods in a rapidly evolving logistics landscape.”

“One of the biggest challenges in last-mile delivery is the inability to monitor package conditions in real-time,” Prof. Otrok explained. “Traditional tracking systems only provide updates on package locations without critical data like temperature, which is essential for sensitive goods. Our system integrates digital twins — virtual models of physical items — into the delivery chain. By embedding sensors within packages, the platform’s digital twins monitor key variables such as temperature and humidity, ensuring that each package remains within safe conditions throughout its journey.”

Digital twins also offer predictive capabilities: If a package encounters extreme conditions, the digital twin can simulate potential risks and notify delivery personnel immediately. This functionality is invaluable for goods like pharmaceuticals, where even minor temperature deviations can compromise produce quality. This way, delivery personnel receive alerts in real time, enabling them to take corrective action before a problem escalates.

The research team’s solution enhances trust and transparency using blockchain. Blockchain’s immutability provides a secure, decentralized ledger that records each package’s journey from sender to receiver, but the team takes it a step further by incorporating dynamic NFTs.

“Traditionally, NFTs are unique digital assets that don’t change over time, but dynamic NFTs evolve as new information is added, which makes them ideal for real-time delivery tracking,” Prof. Otrok explained. “Each package is assigned an NFT that captures all relevant data from the package’s origin to its delivery conditions. If the digital twin detects any discrepancies in package status, it updates the NFT’s metadata, creating an unalterable record of events. This data is stored securely on the blockchain, where anyone with access can verify the package’s history. This transparency not only boosts consumer trust, but also protects delivery personnel from disputes by providing an objective record.”

With last-mile delivery costs comprising up to 50% of total logistics expenses, this system presents a significant opportunity to reduce costs while boosting efficiency. By merging digital twins, blockchain, and dynamic NFTs, the Khalifa University team has created a resilient, transparent, and highly adaptable platform that could transform industries reliant on sensitive goods. Their experiments show that their system improved delivery success by over 75%, and by including smart contracts that assign tasks to delivery personnel based on a quality-of-service score, accountability is enhanced, as each worker’s performance is tracked and evaluated based on the conditions of the packages they handle.