Towards Adaptive Inspection for Fraud in I4.0 Supply Chains

Thomas Welsh, Faeq Alrimawi, Ali Farahani, Diane Hassett, Andrea Zisman, Bashar Nuseibeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The effective functioning of society is increasingly reliant on supply chains which are susceptible to fraud, such as the distribution of adulterated products. Inspection is a key tool for mitigating fraud, however it has traditionally been constrained by physical characteristics of supply chains such as their size and geographical distribution. The increasingly cyber-physical nature of supply chains, their autonomy, and their data richness, extends their attack surfaces and thus increases opportunities for fraud. However, it also presents new opportunities for increased and dynamic inspection, which in turn requires more targeted and flexible inspection regimes. In this paper we explore opportunities to engineer adaptive inspection of cyber-physical supply chains to support efforts to reduce fraud. Through using structural representations of supply chains (topological models) we propose defining optimal inspection zones. Such zones circumscribe assets of interest to optimise observation while reducing the intrusiveness of inspection. Using a motivating example of adulterated pharmaceuticals and a proof-of-concept tool we illustrate adaptive inspection, and surface challenges to its realisation, such as value metrics, forensic readiness integration and managing contrasting local and global perspectives.

Original languageEnglish
Title of host publicationProceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728129891
DOIs
Publication statusPublished - 2021
Event26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 - Virtual, Vasteras, Sweden
Duration: 7 Sep 202110 Sep 2021

Publication series

NameIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2021-September
ISSN (Print)1946-0740
ISSN (Electronic)1946-0759

Conference

Conference26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Country/TerritorySweden
CityVirtual, Vasteras
Period7/09/2110/09/21

Keywords

  • Adaptive
  • Fraud
  • I4.0
  • Inspection
  • Supply chains

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