Citation :

Ravi Garg, Rajesh Anne, "Generative AI for Supply Chain Resilience in Critical Infrastructure," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 8, pp. 25-32, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I8P104

Abstract

The forum of national security policy and its links to industrial practices remains to be fully translated, despite the rising importance of critical infrastructure resilience, including in a globalized context of major disruptions such as natural disasters, cyber-attacks and geopolitical conflicts. Up and coming AI Among the emerging technologies that could dramatically transform the business landscape of tomorrow, nothing has captured everyone's imagination quite like artificial intelligence. And deep learning, or generative AI, is leading this new commercial revolution, and it is the strongest version of AI there is. Supply chain managers are now able to actively consider a broader set of potential responses and make more informed decisions by exploring a variety of risks (ranging from travel or staffing disruptions in a region to demand surges driven by fear-based reactions) and becoming more data-driven and strategic in doing so. For supply chains of this nature, this paper examines the potential of applying generative AI to achieve system readiness (e.g., early warning), system defensive designs, and assured delivery of essential services. In this paper, we demonstrate a potential input of generative AI for event preparation by generating synthetic data for scenario modelling. Generative AI can provide endless simulations of various types of catastrophes using deep learning algorithms. This identifies holes in the supply chain and prepares them to create more malleable or adaptive solutions. To be able to increase the resilience of a system at large, stock levels can be optimized, alternative supply sources can be sourced, and fast decisions can be made with the use of AI-based solutions. Issues such as data privacy, AI integration in non PBR systems, and the required collaboration between governments, industry, and AI researchers are also examined as obstacles to the implementation of generative AI into critical infrastructure. Given the promising and potentially game-changing implications of generative AI as a backup plan for supply chain resilience, this research can be considered as path-breaking as it consolidates existing knowledge assembled so far and prescribes what to do next. And future attempts to bring AI to other critical infrastructure domains should focus on explainability, fairness, and scalability, the argument goes. In the end, generative AI might be essential to ensuring that supply chains are robust enough to survive both expected and unexpected changes. 

Keywords

Essential Facilities, Emerging AI, Supply Chain Resilience, AI, Disruption Anticipation.

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