Research in the Supply Chain Analytics program investigates challenging topics in descriptive, prescriptive and predictive analytics inspired by real-life problems and novel strategies in logistics and operations management. Subjects relate to improving methods for well-established problems in, e.g., performance benchmarking, human decision making, production, inventory and distribution planning, and breaking new grounds by leveraging the potential of sensory data for offline and online planning, designing secure supply chains, and tackling open issues for supporting collaborative and self-driving supply chains. Methods include network data envelopment methods, various exact, metaheuristics and simulation-optimization solution approaches, and machine learning methods.