Revenue management (RM) is the science of maximizing the sales and price per unit of inventory;
For most industries, a mix of computer models and human judgment are required for RM decision-making. The hotel industry serves as an ideal example. RM packages designed for the hotel industry allow for real-time monitoring of capacity utilization, integration with forecasts and advanced analytics. However, individual RM judgment is still viewed as essential to manage the nuances confronted in real-world RM settings appropriately.
For example, complex large group bookings, last-minute purchases, cancelations, weather-related changes and trip adjustments all complicate the RM system. Hence, the effectiveness of many of the applications of these systems, and their associated operating policies, is still very much human driven.
Given the central role of human decision-making in RM decisions, one might question the extent to which people make optimal versus suboptimal RM decisions. A wealth of research shows that human beings make common, predictable errors in general decision-making.
However, little is known about decision-making errors in RM judgment specifically, and how to mitigate them moving forward. Just how good are people at making challenging RM decisions? It turns out that the answer is complex, yet understanding decision-making errors in RM can make a big difference in optimizing RM outcomes.
This article examines the results of a recent study by the author* that sought to measure the extent to which human decision-making in an RM context differs from optimal levels, and the reasons for such decision errors. The author carefully designed a controlled experiment to test pricing decisions across variations of resource capacity and time urgency.
The findings of the study shed important light on the biases that people may have in making RM decisions. With too little challenge and plenty of time, people are more likely to reject offers they otherwise should accept. Conversely, overwhelmed by limited time and capacity yet to allocate, people are more likely to accept offers they otherwise should reject. This effect is magnified in conditions of complexity.
In especially complex RM decisions, people are less likely to be bored by any decision, thus less reject errors take place. The concern in complex scenarios is higher rates of accept errors, where the combination of complexity and time scarcity with resources yet to allocate adds significant levels of stress. Knowing the types of errors that take place in RM decisions, and why they occur, helps to build actionable tools for preventing and overcoming such biases. The article considers the implications of this for management.
*E. Bendoly, "Linking task conditions to physiology and judgment errors in RM systems." Production and Operations Management 20(6), pp. 860-876, 2011.The complete article was written by:Elliot Bendoly, Associate Professor, Goizueta Business School, Emory University, US
Michael Alan Sacks, Associate Professor, Goizueta Business School, Emory University, US Read the full article HERE