Advanced Technologies to Enable Optimized Maintenance Processes in Extreme Conditions: Machine Learning, Additive Manufacturing, and Cloud Technology

Abstract

The way routine maintenance is conducted is not an optimal way to handle maintenance in extreme battlefield conditions. This is a common maintenance problem across various domains, such as repairing battle damage to aircraft or ships without access to a port or depot. The extreme conditions context can also include repairing the Alaska pipeline in the extreme cold, or handling repairs during COVID-19. The researcher examined how modern technology can optimize productivity and reduce the cycle time of the extreme maintenance process. The results of this research found that three emerging technologies: additive manufacturing, cloud in a box, and machine learning (ML), could improve process value, save labor costs, and reduce cycle time. ML had the most significant impact on improving productivity and cycle time. When all technologies were utilized together, productivity and cycle time improvement were more significant and consistent. The research accounted for the riskiness of these technologies, which is necessary to accurately forecast the value added for this extreme maintenance process context. This research is vital because getting correct valued repairs done quickly for the Department of Defense can make the difference between winning and losing a conflict.