Towards Assisted Autonomy for Supply Chain Compliance Management
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Description
In an agricultural supply chain, farmers, food processors, transportation agencies, importers, and exporters must comply with different regulations imposed by one or more jurisdictions depending on the nature of their business operations. Supply chain stakeholders conventionally transport their goods, along with the corresponding documentation via regulators for compliance checks. This is generally followed by a tedious and manual process to ensure the goods meet regulatory requirements. However, supply chain systems are changing through digitization. In digitized supply chains, data is shared with the relevant stakeholders through digital supply chain platforms, including blockchain technology. In such datadriven digital supply chains, the regulators may be able to leverage digital technologies, such as artificial intelligence and machine learning, to automate the compliance verification process. However, a barrier to progress is the risk that information will not be credible, thus reversing the gains that automation could achieve. Automating compliance based on inaccurate data may compromise the safety and credibility of the agricultural supply chain, which discourages regulators and other stakeholders from adopting and relying on automation. Within this article we consider the challenges of digital supply chains when we describe parts of the compliance management process and how it can be automated to improve the operational efficiency of agricultural supply chains. We introduce assisted autonomy as a means to pragmatically automate the compliance verification process by combining the power of digital systems while keeping the human in-the-loop. We argue that autonomous compliance is possible, but that the need for human led inspection processes will never be replaced by machines, however it can be minimised through 'assisted autonomy'.
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ID Code: | 226429 | ||||||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||||||
Series Name: | Proceedings - 2021 3rd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2021 | ||||||||
ORCID iD: |
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Additional Information: | eCF Paper Id: 1637720482626 | ||||||||
Measurements or Duration: | 10 pages | ||||||||
DOI: | 10.1109/TPSISA52974.2021.00035 | ||||||||
ISBN: | 978-1-6654-1624-5 | ||||||||
Pure ID: | 102070088 | ||||||||
Divisions: | Current > Research Centres > Centre for Agriculture and the Bioeconomy Current > Research Centres > Centre for Clean Energy Technologies & Practices Current > Research Centres > Centre for Justice Current > QUT Faculties and Divisions > Faculty of Business & Law Current > Schools > School of Law Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science Current > QUT Faculties and Divisions > Faculty of Creative Industries, Education & Social Justice |
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Copyright Owner: | 2021 IEEE | ||||||||
Copyright Statement: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Deposited On: | 25 Nov 2021 02:59 | ||||||||
Last Modified: | 08 May 2024 06:27 |
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