Areas of Focus
Advances in smart sensors have created a unique opportunity to monitor and coordinate the performance of physical engineering systems with broader enterprise operations, such as manufacturing operations, service logistics, maintenance management, after-sales services, etc. This transformation demands methodologies and solutions capable of analyzing and modeling in-situ multi-stream sensor signals to support and facilitate optimal decision making strategies. Combined with state-of-the-art real-time optimization techniques nearly instantaneous decision can be computed in fast changing business environments unlocking significant cost-savings.
In response to these industrial challenges, the Stewart School of Industrial and Systems Engineering established the Center for Predictive Analytics and Intelligent Systems (PAIS). Research activities at PAIS are focused on bridging the gap between data science and decision science in industrial applications with a specific emphasis on IoT ecosystems. Housed in the Supply Chain and Logistics Institute, PAIS brings together experts from various disciplines and backgrounds to address cross-industry challenges through a single point of contact using a problem-driven approach.
Key research areas: