Senthil Veeraraghavan researches on the role of Information and Uncertainty in Operations Management, using Theory and Data Analysis. His current research is on Operational implications of consumer reactions to Dynamic Pricing.
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 and Decision Making in Operations.
Revenue Management, Customer Response oriented Dynamic Pricing, Pricing Models, Operations Strategy, Supply Chain Management, Queueing Games.
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, (2018)
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.
Joseph Jiaqi Xu, Peter Fader, Senthil Veeraraghavan, Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets.
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.
Abstract: Platforms in the sharing economy such as Uber and Lyft adopt a bilateral rating system (BRS) that allows service providers to rate customers and to make accepting/rejecting decisions based on the customers' ratings while in the traditional online platforms (e.g., eBay), only customers have the privilege to rate the other party, i.e., a unilateral rating system (URS) is implemented. This novel feature of the rating system in the sharing economy changes the service provider's effort structure in a fundamental way, which in turn affects the pricing strategy of the platform and the welfare of service providers as well as customers. With a stylized model, we compare URS and BRS in the context of ride-sharing service to study their impact on the decisions as well as revenue/welfare of all stakeholders. Our results show that being empowered to turn down customers at the service providers' discretion (in BRS) may not always improve the economic situation of service providers. Specically, the platform could squeeze the service providers' profit margin (per order) forcing them to serve only highly rated customers and hence reducing both their transaction volume and the profit margin. This leads to a decline in the service quality compared with URS. Meanwhile, the platform could also suffer a loss from BRS when the customers' valuation to the service is high (e.g., in a city with a less developed public transportation system), as the service providers' "cherry-picking" behavior in selecting customers is particularly costly to the platform in that case. This makes the platform have to give some of its revenue back to the service providers to mitigate their over-selection behavior, which can potentially reduce the transaction volume substantially. In practice, the platform could change the decision time of drivers (to reject customers' request) based on the estimation of the customers' valuation (in each city) to the ride-sharing service and hence, switching between bilateral and unilateral rating systems effectively.
Ganesh Janakiraman, Mahesh Nagarajan, Senthil Veeraraghavan (2017), Simple Policies for Managing Flexible Capacity, M&SOM.
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.
Shiliang Cui and Senthil Veeraraghavan (2016), Blind Queues: The Impact of Consumer Beliefs on Revenues and Congestion, Management Science, 62 (12), pp. 3656-3672.
Necati Tereyagoglu, Peter Fader, Senthil Veeraraghavan (2016), Multi-attribute Loss Aversion and Reference Dependence: Evidence from the Performing Arts Industry, Management Science.
Necati Tereyagoglu, Peter Fader, Senthil Veeraraghavan (2016), Pricing Theater Seats: The Value of Price Commitment and Monotone Discounting, Production and Operations Management.
Senthil Veeraraghavan and M. Fazil Pac (Work In Progress), False Diagnosis and Overtreatment in Services.
Senthil Veeraraghavan, Shiliang Cui, Xuanming Su (Work In Progress), A Model of Rational Retrials in Queues.
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.
The largest market for Uber, Lyft and other ride-hailing services last week had its first successful attempt at regulating the growth of the nascent industry.Knowledge @ Wharton - 2018/08/14