An Event-Driven Architecture for Autonomous Supply Chain Risk Detection and Decision Automation

Abstract

The primary goal of the supply chain risk detection and alerting MVP architecture is to support critical functionality with limited user input with the aid of various data sources (e.g., ERP, WMS, IoT sensors) to synthesize the data amplified as a real-time visualization for a user. Autonomous AI agents support a modular and event-driven architecture that adds awareness of a situation for predictive and risk mitigation purpose. Advantages of this architecture are to provide the capability of identifying risk through machine learning and anomaly detection, a scalable microservices architecture to build or incorporate sophisticated AI agents that emphasize logistics and compliance, and allow for the automation of work processes, aiding in creating a feedback loop for complicated decision-making capability. The architecture is also touching upon governance regarding security and ethical AI (both required aspects of any successful operation) while monitoring real-time KPI status, which is beneficial for governance of system health. Further, the architecture provides an additional useful capability for continuous iterative improvements to the automated processes, which aids in supply chain risk governance and reinforces oversight of supplier risk networks by using a process for implementing reinforcement learning and risk governance of operational functions. There is also a strong opportunity for companies to leverage semi-advanced technologies (i.e., edge computing) to intelligently build AI based systems to support real-time proactive risk management practices in supply chain operations, while creating potential implications to enhance agility and the quality of strategic decision-making.