Cyber Technologies, Machine Learning, Additive Manufacturing, and Cloud in the Box to Enable Optimized Maintenance Processes in Extreme Conditions

Abstract

Routine maintenance processes (e.g., peacetime conditions) are not optimized for extreme maintenance conditions (e.g., aircraft or ship battle damage repair, extreme cold Alaska pipeline repair, and COVID-19 depot repair processes). In extreme contexts, modern information technology (e.g., machine learning [ML], additive manufacturing [AM], and Cloud in the Box [CIB]) is typically not being leveraged to optimize productivity and cycle time in these maintenance processes. Literature on process optimization does not address the use of modern technology for optimization in extreme maintenance conditions. This research aims to test the value added to information technology to optimize process productivity and cycle time for extreme maintenance conditions. It will extend the use of process optimization theory to include the effect of modern information technology as well as extreme maintenance contexts. This research is critical because failure to make correct repairs can affect the organization at its most vulnerable cyber infrastructure.