Accurate Predictions

Traditional DoW vehicle fleet maintenance approaches often lead to premature part replacements and increased lifecycle expenses. To address this, KBR implemented an AI-powered Condition-Based Maintenance Plus (CBM+) solution. Leveraging predictive analytics, machine learning models, and sensor-driven health monitoring, our system forecasts component failures and optimizes maintenance schedules. Our solution integrates advanced anomaly detection algorithms and automates decision-support workflows, enabling real-time insights across multiple platforms. Our approach has delivered measurable benefits: reducing unscheduled maintenance events by over 20%, improving equipment availability and generating significant cost savings in sustainment operations. By shifting from reactive to predictive maintenance, users achieved higher operational efficiency and established a scalable, data-driven framework for future fleet management.

KBR leverages AI-driven predictive modeling to support natural disaster response and emergency management, delivering actionable insights for events such as wildfires and oil spills. By integrating deep learning architectures, including convolutional neural networks and transformer models, with decades of historical data, satellite imagery, and real-time environmental inputs, our solutions enable agencies to forecast wildfire risk, predict ever-changing wildfire perimeters (even in the presence of thick smoke or cloud cover), and classify environmental hazards like oil slicks with unprecedented accuracy and speed. These capabilities empower emergency responders and resource managers to make faster, more informed decisions, optimize the allocation of critical assets, and ultimately protect lives, property, and natural resources.