Predictive AI for Supply Chain Management: Addressing Vulnerabilities to Cyber-Physical Attacks

Abstract

The rising global supply chain digitization has enhanced the efficiency of global supply chains but rendered them more susceptible to advanced cyber-physical attacks. Predictive Artificial Intelligence (AI) has become a potent element in predicting disruptions, operations optimization, and resilience reinforcement in logistics, manufacturing, and distribution systems. The incorporation of predictive AI, however, brings about some additional risks, such as exposure to adversarial data manipulation, system compromise, or attacks targeting interconnected infrastructures that are powered by the IoT. The present paper explains how predictive artificial intelligence can be used to deal with vulnerabilities in the management of supply chains in a manner that guarantees continuity and security of operations. It focuses on the major uses of predictive AI in demand prediction, data anomaly detection, and risk surveillance, as well as the threats in this domain including data poisoning and sensor manipulation. Mechanisms of developing resilient, explainable and secure AI-based supply chain frameworks are provided, such as adversarial resilience, data integrity facilitated by blockchain, and hybrid human-AI management. The results indicate that predictive AI has a two-fold use as an object, and as a protection system in the contemporary supply chain, and that transdisciplinary cooperation and legal frameworks should unite to protect international trade against new cyber-physical threats.