Towards Adaptive Inspection for Fraud in I4.0 Supply Chains

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.