Senthil Veeraraghavan researches on the role of Information and Uncertainty in Operations Management, using Theory and Data Analysis. His current research is on designing dynamic pricing based on customer reactions.
His research has appeared in Management Science, Operations Research, Manufacturing and Service Operations Management and Production and Operations Management journals. His research on Quality Speed Tradeoffs in services won the first ever award for Best paper in Operations published in Management Science.
Recently, Senthil Veeraraghavan advised a undergraduate thesis on a project that received Class of 2018 Penn President’s Engagement Prize, awarded to senior projects designed to make a substantial, sustainable impact in the world.
Currently, Senthil teaches Operations Strategy, for which he has received several Wharton Excellence in Teaching Awards.
Senthil graduated from Indian Institute of Technology, Bombay and received his PhD from Carnegie Mellon University.
I study the role of Information in Service Operations and Pricing.
Revenue Management, Dynamic Pricing Applications, Service Operations, Operations Strategy, Supply Chain Management, Queueing Games.
Senthil Veeraraghavan, Li Xiao, Hanqin Zhang, Impatience and Learning in Queues.
Abstract: Customers often abandon waiting in queues when they get impatient. Prior literature on Markovian queues shows that it is not rational for customers to quit ``midway": Customers should either quit immediately on arrival (balk) or wait till the completion of their service. We show how in-queue abandonment behavior can be rational in queues because of learning. We compare how rational Bayesian customers make abandonment decisions under different information disclosures. Our paper reveals interesting features in waiting behavior, showing that customers can be (rationally) more patient in slower shorter queues, than in faster longer queues. Using stochastic comparisons, we demonstrate that customers who anticipate a congested system can become more impatient. Finally, we show that Bayesian customers may exhibit a more conservative threshold joining behavior compared to myopic customers with the same priors.
Abstract: We model the presence of collusion amongst customers who wait to consume a service. In congested queues, individual customers seek to maximize their own payoff and compete with each other for limited resources. To overcome this disadvantage, some customers may collude as a group to seek and receive better benefits, often in the form of reduced prices for group members. Using a queuing framework, we characterize a service provider's optimal pricing and priority decisions when serving a market made up by individual customers and collusive customers. We demonstrate how individual customers may benefit from the presence of colluding groups when a single price is charged for the service. The presence of collusion enables individual customers to ``free-ride” and enjoy an increased consumer surplus. Under price discrimination, the service provider prefers to serve individual customers at a higher price, over collusive customers. However, despite the preference for individual customers, the service provider can improve the revenue by better service management, by prioritizing collusive customers and reducing their price benefit. Prioritization of collusive customers can also improve social welfare over the first come, first served (FCFS) policy, by better utilization and increased market coverage.
Laurens Debo, Uday Rajan, Senthil Veeraraghavan (2018), Signaling Quality via Long Lines and Uninformative Prices, M&SOM (Forthcoming).
Abstract: Firms sometimes nurture long lines, rather than raising prices to eliminate waiting times. We justify this practice by considering the informational role of a queue in a setting in which a rm can also adjust its price to signal its quality to uninformed consumers. When the proportion of informed consumers is low, to separate on price a high-quality rm must raise its price above the monopoly price. We show that there exist pooling equilibria in which firms instead charge a price lower than the monopoly price. We characterize two pooling equilibria. In both equilibria, a high-quality rm on average has a longer queue than a low-quality one, and long lines signal high quality. In the first one, the price is sufficiently low that short lines in turn signal low quality, so that the queue length almost perfectly reveals the type of the rm. In the second one, the price is in an intermediate range (but remains lower than the monopoly price), and short lines are an imperfect signal of quality. In each of the pooling equilibria, the high-quality firm earns a higher prot than in the separating equilibrium, despite the longer lines. Therefore, a rm will sometimes signal the quality of a new or improved product via long queues rather than by charging a high price to reduce the queue length.
Joseph Jiaqi Xu, Peter Fader, Senthil Veeraraghavan (2018), Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets, M&SOM (Forthcoming).
Abstract: Many firms have difficulty evaluating the impact of their pricing policy, which further inhibits their ability to properly design and implement dynamic pricing. We address this issue in the context of single-game ticket pricing for a Major League Baseball franchise. We develop and estimate a comprehensive demand model to help evaluate and design dynamic pricing policies for the franchise. Our model encompasses all relevant aspects of the demand generation process, including ticket quantity and stadium seat section choice. The demand model reveals factors that drive sport ticket revenue such as the effect of home team performance on the overall price sensitivity and the relationship between customers' arrival timing and product choice. We show that by leveraging these insights and allowing sufficient pricing flexibility, the franchise can achieve a potential revenue improvement of 17.2% through daily price re-optimization, which is comparable to that of a clairvoyant policy in which the future evolution of demand is assumed to be known.
Hummy Song and Senthil Veeraraghavan, “Quality of Care: An Operations Perspective of Health Care Quality”. In Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, edited by Tinglong Dai and Sridhar Tayur (Eds.), (Hoboken, NJ: John Wiley & Sons, 2018)
Abstract: In many scenarios, a fixed capacity is shared flexibly between multiple products. To manage such multi-product systems, firms need to make two sets of decisions. The first one requires setting an inventory target for each product and the second decision requires dynamically allocating the scarce capacity among the products. It is not known how to make these decisions optimally. In this paper, we propose easily implementable policies that have both theoretical and practical appeal. We first suggest simple and intuitive allocation rules that determine how such scarce capacity is shared. Given such a rule, we calculate the optimal inventory target for each product. We demonstrate analytically that our policies are optimal under two asymptotic regimes represented by high service levels (i.e. high shortage costs) and heavy traffic (i.e. tight capacity). We also demonstrate that our policies outperform current known policies over a wide range of problem parameters. In particular, the cost savings from our policies become more significant as the capacity gets more restrictive.
Abstract: We develop a model of crowdfunding dynamics that maximizes revenue for a given fundraising campaign by optimizing both the pledge level sought from donors or backers and the duration of the campaign. Our model aligns with the patterns of backer/donor arrival and pledging observed on crowdfunding platforms, such as Kickstarter. Using our model, we calibrate the revenue lost from using pre-specified pledge levels or campaign durations. We show that under the optimal design, the pledge level sought decreases as the goal of a campaign increases, with a more pronounced effect for both very low and very high campaign goals. We further demonstrate how uncertainty in pledge accumulation improves campaign revenue and aids campaign success. In particular, we show that campaigns with high goals benefit from highly uncertain environments more than campaigns with low goals.
Wharton MBA Core Curriculum Teaching Award: 2016
Wharton Teaching Commitment and Curricular Innovation Award 2016.
Wharton MBA Excellence in Teaching Award, 2015.
Wharton Undergraduate Excellence in Teaching Award, 2013.
Currently teaching Operations Strategy (OIDD 615).
Operations strategy is about organizing people and resources to gain a competitive advantage in the delivery of products (both goods and services) to customers. This course approaches this challenge primarily from two perspectives: 1) how should a firm design their products so that they can be profitably offered; 2) how can a firm best organize and acquire resources to deliver its portfolio of products to customers. To be able to make intelligent decisions regarding these high-level choices, this course also provides a foundation of analytical methods. These methods give students a conceptual framekwork for understanding the linkage between how a firm manages its supply and how well that supply matches the firm's resulting demand. Specific course topics include designing service systems, managing inventory and product variety, capacity planning, approaches to sourcing and supplier management, constructing global supply chains, managing sustainability initiatives, and revenue management. This course emphasizes both quantitative tools and qualitative frameworks. Neither is more important than the other.
Seminar on distribution systems models and theory. Reviews current research in the development and solution of models of distribution systems. Emphasizes multi-echelon inventory control, logistics management, network design, and competitive models.
Torn between unsustainable delivery costs and its strategy of wooing customers with low prices, Amazon will need to rethink its business model to ensure continued growth, say experts.Knowledge @ Wharton - 2019/09/10