A Knowledge-Graph Approach to Digital Materiel Management

A major challenge to successful implementation of Digital Materiel Management (DMM) to develop, deliver, operate, and sustain materiel capabilities is the ability to efficiently flow data, models, and knowledge across multiple disparate functional boundaries to accelerate and improve key decisions over the lifecycle of systems. A primary enabler for efficient knowledge flow across the entire lifecycle lies in the application of Knowledge Graphs (KG) in the development and validation of Authoritative Digital Surrogate models to support effective decision analytics at the speed of relevance. A concept employing a KG capability is presented to generate, capture, preserve, syndicate authoritative sources of truth, and share across functional lines to enable better decision making over the lifecycle application of DMM. The KG capability also enables sound knowledge assurance processes imperative for implementing responsible artificial intelligence (AI) to not only develop authoritative digital surrogate models but to employ AI capabilities to integrate KG with Large Language Models as a bidirectional connector between knowledge and decision making to provide risk-informed, value-focused decisions to accelerate integrated materiel capability delivery.