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Supply chain traceability and counterfeit detection of COVID-19 vaccines using novel blockchain-based Vacledger system

journal contribution
posted on 2024-11-03, 09:48 authored by Uvini Munasinghe, Malka N HalgamugeMalka N Halgamuge
We propose a novel framework, Vacledger, for supply chain traceability and counterfeit detection of COVID-19 vaccines using a blockchain network. It includes four smart contracts on a private-permissioned blockchain network for supply chain traceability and counterfeit detection of COVID-19 vaccine, more specifically to (i) handle the rules and regulations of vaccine importing countries and provide authorization for cross the borders (regulatory compliance and border authorization smart contract), (ii) register new and imported vaccines in the Vacledger system (vaccine registration smart contract), (iii) find the number of stocks that have arrived in the Vacledger system (stock accumulation smart contract), and (iv) identify the exact location of the stock (location tracing update smart contract). Our results show that the proposed system keeps track of all activities, events, transactions, and all other past transactions, permanently stored in an immutable Vacledger connected to decentralized peer-to-peer file systems. We observe no algorithm complexity differences between the proposed Vacledger system and existing supply chain frameworks based on different blockchain types. In addition, based on four use cases, we estimate our model's overall gasoline cost (transaction or gas price). The Vacledger system empowers distribution companies to manage their supply chain operations effectively and securely using an in-network, permissioned distributed network. This study employs the COVID-19 vaccine supply chain (the healthcare industry) to demonstrate how the proposed Vacledger system operates. Despite this, our proposed approach might be implemented in other supply chain industries, such as the food industry, energy trading, and commodity transactions.

History

Related Materials

  1. 1.
    DOI - Is published in 10.1016/j.eswa.2023.120293
  2. 2.
    ISSN - Is published in 09574174

Journal

Expert Systems with Applications

Volume

228

Number

120293

Start page

1

End page

25

Total pages

25

Publisher

Elsevier

Place published

United Kingdom

Language

English

Copyright

© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Former Identifier

2006123520

Esploro creation date

2023-07-09