Abstract: When customers wait for service, providers often announce forecast information about delays to reduce customer uncertainty and anxiety. Forecasts, however, are imperfect and when delays are longer than predicted, customers can be disappointed. Here we examine how to design delay forecasting and message systems that take into account both the anxiety caused by uncertainty and the relative disappointment of customers when delays are longer than expected. We develop a forecasting framework that allows us to optimize the customers’ experiences while waiting. Customers update their beliefs about the expected start time of service according to Bayes' rule, given both the messages from the service provider and the passage of time. Customers are loss-averse in the sense that an increase in the expected wait causes more distress than the positive response caused by an equivalent decrease, and they are also risk conscious in that variance in delay forecasts reduces utility. We find that when loss aversion dominates, the optimal message strategy emphasizes provision of information about the tails of the distribution rather than simply updating the customers about the relative duration of the wait. When risk consciousness is dominant more traditional ordinal forecasts are optimal, and optimal messages should provide the most accurate information about the longest delays. In general, the model allows us to explore the role of forecasting systems and delay announcements in shaping customer expectations, while providing accurate information and managing expectations.
Bio: Robert Shumsky is a Professor of Operations Management at the Tuck School of Business at Dartmouth and is co-director of Dartmouth’s Master in Health Care Delivery Science program. His research focuses on the improvement of service operations, and he has written about capacity estimation and control, how to allocate work to improve quality, and how to coordinate service supply chains. He has conducted research on the U.S. air traffic management system and studied transportation operations for state agencies and the Federal Aviation Administration. He has also served as a consultant for both manufacturing and service operations, including call centers and health care providers. Professor Shumsky has published articles in Manufacturing and Service Operations Management, Operations Research, Management Science, and the Proceedings of the National Academy of Science. He currently serves in various editorial positions for several academic journals. He received his PhD degree in Operations Research from MIT.