讲座题目： Strategic Capacity Management when Customers Have Bounded Rationality

主讲人：刘倩 副教授 (香港科技大学, The Hong Kong University of Science and Technology)
时间： 2015年10月12日（周一）15:00-17:00
地点： 文科楼1400会议室
主持人：马利军 副教授 管理科学系副主任
主讲人简介：
Qian Liu is an associate professor in the department of Industrial Engineering and Logistics Management at Hong Kong University of Science and Technology. Her research interests are primarily in revenue management and pricing, consumer-driven operations management, marketing-operations interfaces. She received the Ph.D. degree in the division of Decision, Risk and Operations from the Graduate School of Business, Columbia University, and the B.E. and M.E. in Control Theory and Control Engineering from Tsinghua University in China. Her research articles have appeared in Management Science, Manufacturing & Service Operations Management, Productions and Operations Management, and among others.
讲座内容简介：
In retailing industries, such as apparel, sporting goods, customer electronics and appliances, many firms deploy sophisticated modeling and optimization software to conduct dynamic pricing in response to uncertain and fluctuating market conditions. However, the possibility of markdown pricing creates an incentive for customers to strategize over the timing of their purchases. How should a retailing firm optimally account for customer behavior when making its pricing and stocking/capacity decisions in a supply chain? For example, is it optimal for a firm to create rationing risk by deliberately understocking products? In this paper, we develop a stylized modeling framework to answer these questions. In our model, customers strategize over the timing of their purchases. However, customers have boundedly rational expectations in the sense of anecdotal reasoning about the firms fill rate, i.e., they have to rely on anecdotes, past experiences, or word-of-mouth to infer the firms fill rate. In our modeling framework, we distinguish two settings: (i) capacity commitment, where the firm commits to its capacity level in the long run, or (ii) the firm dynamically changes it in each season. For both settings, within the simplest form of anecdotal reasoning, we prove that strategic capacity rationing is not optimal independent of customer risk preferences. Then, using a general form of anecdotal reasoning, we provide sufficient conditions for capacity rationing to be optimal for both settings respectively.  We show that the result of strategic capacity rationing being suboptimal is fairly robust to different valuation distributions and utility functions, heterogeneous sample size, price optimization, and discounted utility. (This is joint work with Tingliang Huang from Boston College.)
 
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