Leverage Artificial Intelligence to Learn, Optimize, and Win (LAILOW) for the Marine Maintenance and Supply Complex System

Abstract

A complex enterprise includes multiple subsystems and organizations. The U.S. Marine Corps (USMC) maintenance and supply chain is a complex enterprise and exemplifies a socio-technological infrastructures. It is imperative for the USMC to adopt more advanced data sciences including ML/AI techniques to the entire spectrum or end-to-end (E2E) logistic planning as a complex enterprise including maintenance, supply, transportation, health services, general engineering, and finance. In this paper, we first review an overall framework of leveraging artificial Intelligence to learn, optimize, and win (LAILOW) for a complex enterprise, and then show how a LAILOW framework is applied to the USMC maintenance and supply chain data as a use case. We also compare various machine learning (ML) algorithms such as supervised machine learning/predictive models and unsupervised machine learning algorithms such as lexical link analysis (LLA). The contribution of the paper is that LLA computes stable and sensitive components of a complex system with respective to a perturbation. LLA allows to discover and search for associations, predict probability of demand and fail rates, prepare spare parts, and improve operational availability and readiness.