Maria Ibanez is an Assistant Professor of Operations Management at the Kellogg School of Management at Northwestern University. She received her doctorate in Technology and Operations Management from Harvard Business School.

Operations management research typically prescribes policies (such as schedules) for managers to implement. Yet, in practice, workers often have discretion—freedom to decide which tasks to work on, when, and how. As a result, they often deviate from their prescribed assignments. Her research investigates how to "manage" discretion by creating the conditions that promote desirable deviations and mitigate undesirable ones.

From a practical perspective, her research focuses on improving performance by designing data-driven systems that lead individuals to exercise discretion in ways that increase their productivity and work quality. Her work spans archival big data and field experiments in contexts ranging from radiology to restaurant inspections and emergency departments. With a primary focus on healthcare, she collaborates with organizations to understand their work and develop implementable solutions for relevant challenges. Combining operations management with economic theory and the psychology of decision-making, she analyzes large-scale field data to identify causal relationships that generate new insights regarding the connections between operational factors, decision-making, and performance.

 

See Research and Publications

 

• DBA, 2018, Technology and Operations Management, Harvard Business School

• Assistant Professor of Operations, Operations, Kellogg School of Management, Northwestern University, 2018-present

Emerging Areas in Operations Managements (OPNS-525-0)
This course studies novel, emerging topics and methods used in academic research of operations management. Content will depend on the expertise and interests of the instructor. Past content included statistical (machine) learning and sequential decision-making, such as bandit learning, balancing exploration/exploitation, and reinforcement learning, including methods for value function approximation and algorithms for efficient exploration.

Empirical Methods in Operations Management (OPNS-524-0)
This course examines: (1) how to critically read empirical studies, (2) how to ask questions that are interesting and worthwhile studying empirically, (3) what each method of causal inference (e.g. instrumental variables, panel data methods, regression discontinuity, etc.) does and why, when, and how to use each method, and (4) how an empirical researcher goes from an idea to a finished paper.

Operations Management (OPNS-430-0)
1Ys: This course is typically waived through the admissions process or the equivalent course Operations Management (Turbo) (OPNS-438A) was completed during the Summer term. MMMs: This course is equivalent to the MMM core course Designing and Managing Business Processes (OPNS-440) Operations management is the management of business processes--that is, the management of the recurring activities of a firm. This course aims to familiarize students with the problems and issues confronting operations managers, and to provide the language, concepts, insights and tools to deal with these issues to gain competitive advantage through operations. We examine how different business strategies require different business processes and how different operational capabilities allow and support different strategies to gain competitive advantage. A process view of operations is used to analyze different key operational dimensions such as capacity management, cycle time management, supply chain and logistics management, and quality management. Finally, we connect to recent developments such as lean or world-class manufacturing, just-in-time operations, time-based competition and business re-engineering.