Rogelio Oliva is the Robyn L. '89 and Alan B. Roberts '78 Chair in Business in the Department of Information and Operations Management at Mays Business School, and Research Affiliate at MIT Center for Transportation & Logistics. His research explores how behavioral and social aspects of an organization interact with its technical components to determine the firm's operational performance. His current research interests include behavioral operations management, retail and service operations, and the transition that product manufacturers are making to become service providers. His research work has been published in several academic journals, among them: Management Science, Organization Science, Journal of Operations Management, Production and Operations Management, and California Management Review. He is the recipient of the 2019 Jay W. Forrester Award for the best written contribution to the System Dynamics field in the preceding five years.
Dr. Oliva teaches courses in operations management, supply chain management, and management information systems for the MBA and Executive MBA programs. He has received multiple teaching awards including the Production and Operations Management Society's Skinner Teaching Achievement Award (2014) and the Association of Former Students of Texas A&M University Distinguished Achievement Award at the College (2009) and University (2013) levels.
A native of Cd. Valles, Mexico, he holds a B.E. in Industrial and Systems Engineering from the Monterrey Institute of Technology (Mexico), an M.A. in Systems in Management from Lancaster University (UK), and a Ph.D. in Operations Management and System Dynamics from the Massachusetts Institute of Technology.
Prior to joining the Mays faculty, Professor Oliva served in the faculty of the Harvard Business School, Universidad Adolfo Ibanez in Chile, and ITESM in Mexico. He has worked for small manufacturing businesses in Mexico and consults on improvement of service and manufacturing operations, organizational change initiatives, and the development of system dynamics models.