Operational Data Analytics
This book develops Operational Data Analytics (ODA) as a rigorous theoretical foundation, a unified research methodology, and a guiding philosophy for data-integrated decision making. Moving beyond conventional approaches that emphasize general-purpose learning and optimization procedures, ODA places the structure of the decision problem at the heart of decision learning. Its central focus is the design and validation of data-to-decision mappings, termed operational statistics, that systematically exploit the knowledge and structural properties of the underlying system. Bringing together optimization, statistical inference, machine learning, and universal approximation, the authors develop a unified spectrum of decision-learning approaches distinguished by what is known, what remains to be learned, and how decision quality should be validated. The book equips researchers and doctoral students with a rigorous framework for integrating advanced analytical methods with mathematical structure and operational insight.
Qi (Annabelle) Feng, Ph.D., is the John and Donna Krenicki Chair in Operations Management at Purdue University’s Daniels School of Business. Her research focuses on stochastic functions, data-driven decision-making, and negotiations with applications spanning stochastic inventory and pricing models, supply chain structure, resource planning, not-for-profit operations, and proliferation management. A Fellow of the Production and Operations Management Society (POMS), Dr. Feng is a recipient of the Franz Edelman Award and the Wickham Skinner Early-Career Award. She holds a Ph.D. from University of Texas at Dallas.
J. George Shanthikumar, Ph.D., is the Richard E. Dauch Distinguished Chair in Manufacturing and Operations Management at Purdue University. He previously served as a Chancellor’s Professor of Industrial Engineering and Operations Research at the University of California, Berkeley. His research integrates interdisciplinary decision-making, model uncertainty and learning, production systems, queueing theory, reliability, and scheduling. Additionally, his work encompasses semiconductor yield management, simulation, and stochastic processes. He is a Fellow of both the Institute for Operations Research and the Management Sciences (INFORMS) and the Production and Operations Management Society (POMS). He holds a Ph.D. from University of Toronto.