Bayesian Networks for Combat Equipment Diagnostics

Abstract

The lives of U.S. soldiers in combat depend on complex weapon systems and advanced technologies. In combat conditions, the resources available to support the operation and maintenance of these systems are minimal. Following the failure of a critical system, technical support personnel may take days to arrive via helicopter or ground convoy—leaving soldiers and civilian experts exposed to battlefield risks. To address this problem, the U.S. Army Communications Electronics Command (CECOM) developed a suite of systems, Virtual Logistics Assistance Representative (VLAR), with a single purpose: to enable a combat soldier to maintain critical equipment. The CECOM VLAR team uses an operations research (OR) approach to codifying expert knowledge about Army equipment and applying that knowledge to troubleshooting equipment diagnostics in combat situations. VLAR infuses a classic knowledge-management spiral with OR techniques: from socializing advanced technical concepts and eliciting tacit knowledge, to integrating expert knowledge, to creating an intuitive and instructive interface, and finally, to making VLAR a part of a soldier’s daily life. VLAR is changing the Army’s sustainment paradigm by creating an artificial intelligence capability and applying it to equipment diagnostics. In the process, it has generated a sustainable cost-savings model and a means to mitigate combat risk. Through 2015, VLAR saved the Army $27 million in direct labor costs from an investment of $8 million by reducing the requirement for technical support personnel. We project additional direct costs savings of $222 million from an investment of $60 million by the end of 2020. Most importantly, VLAR has prevented an estimated 4,500 casualties by reducing requirements for helicopter and ground-convoy movements. This translates to short- and long-term medical cost savings of over $9 billion. In this paper, we discuss the OR methods that underpin VLAR, at the heart of which lie causal Bayesian networks, and we detail the process we use to translate scientific theory and experiential knowledge into accessible applications for equipment diagnostics.