时间：2025年8月30日 15:00 - 17:30

地点：东校区财科馆317会议室

【讲座一】

题目Title：From Ancillaries to Bundles: Pricing Analytics using Conversion Data.

摘要Abstract：In this talk, I will review research from the SIA–NUS Digital Aviation Corporate Laboratory that explores how airlines and service firms can unlock value from ancillary pricing and bundle design. Ancillaries—ranging from baggage and seat upgrades to Wi-Fi and meal packages—now account for a substantial share of industry revenue. Yet the challenge lies not only in deciding what to offer, but also how to structure and price these optional services alongside the core product.

Our research shows that pricing ancillaries is not a simple “add-on” problem: changing the price of the base fare can alter the appeal and uptake of the extras, while packaging services into bundles can either accelerate or dilute revenue. By analyzing conversion data from experiments, we demonstrate how firms can systematically learn customer choice behavior and optimize prices across both core and ancillary products. The findings have broad implications for firms seeking to balance revenue growth, customer experience, and competitive positioning in markets where add-ons play a decisive role.

This talk draws on joint work with Liu Changchun (XJTU), Sun Hailong (SJTU), and Jin Xiao (NUS).

主讲人Biography：

Chung Piaw TEO is Stephen Riady Professor and Executive Director of the Institute of Operations Research and Analytics (IORA) in the National University of Singapore. Prior to the current appointments, he was a Head of Department, Acting Deputy Dean, Vice-Dean of the Research and Ph.D Program as well as Chair of the Ph.D Committee in the NUS Business School.

He was a fellow in the Singapore-MIT Alliance Program, an Eschbach Scholar in Northwestern University (US), Professor in Sungkyunkwan Graduate School of Business (Korea), and a Distinguished Visiting Professor in YuanZe University (Taiwan). He is currently spearheading an effort to develop the IORA, as part of the University’s strategic initiatives in the Smart Nation Research Program, as well as a department editor for MS (Optimization), and a former area editor for OR (Operations and Supply Chains).

He studied issues in service and manufacturing operations, supply chain management, discrete optimization, and machine learning. He has also served on several international committees such as the Chair of the Nicholson Paper Competition (INFORMS, US), member of the LANCHESTER and IMPACT Prize Committee (INFORMS, US), Fudan Prize Committee on Outstanding Contribution to Management (China).

【讲座二】

题目Title：Replacing What Could Be Repaired: A Structural Analysis of Two-Stage Diagnostic Decisions in Managing Shared-Bike Returns

摘要Abstract：Bike-sharing platforms face significant challenges from high maintenance costs, driven by heavy usage and inefficiencies in diagnostic decision-making. Using task-level data from a leading bike-sharing platform, we develop a structural estimation model to analyze two-stage diagnostic decisions made by inspectors (stage 1) and workers (stage 2). These decisions are modeled as a strategic interaction governed by a Bayesian Nash Equilibrium (BNE). To address the computational complexity of Maximum Likelihood Estimation with BNE constraints, we employ machine learning to approximate BNE. We identify systematic overtreatment tendencies among inspectors and workers, resulting in a higher false positive rate than that under the firm’s optimal decisions and thus inflating maintenance costs. Our counterfactual analyses show that higher part costs, reducing workers’ piece-rate wages, adopting structured job matching, and prioritizing worker training can substantially reduce costs. Transitioning from a two-stage to a one-stage process lowers diagnostic accuracy and increases costs, although optimizing wages narrows this gap. This framework provides actionable insights for mitigating inefficiencies in multi-agent diagnostic decision systems and is generalizable to other credence goods industries, such as heavy equipment maintenance and healthcare, where diagnostic errors have significant financial, operational, and health implications.

主讲人Biography：

Guangwen (Crystal) Kong is an Associate Professor of Statistics, Operations & Data Science at Temple University’s Fox School of Business. She received her Ph.D. Degree in Operations Management at the University of Southern California in 2013 and was a faculty of the University of Minnesota’s Department of Industrial & Systems Engineering.Dr. Kong’s research investigates how information and incentives shape behavior and operational decisions in sharing-economy, on-demand platforms, service operations, and supply chains. Her research appears in Management Science, Manufacturing & Service Operations Management, and Production & Operations Management and has received field-leading honors such as the 2022 Management Science Best Paper in Operations Management Award, the 2021 M&SOM Service SIG Best Paper Award, and the 2021 DSI Best Problem-Driven Analytical Research Paper Award, etc.. She serves as an associate editor of M&SOM, Naval Logistics Research and Service Science. She's currently the president of POMS College of Behavioral Operations Management and secretary/treasurer of INFORMS Service Science Section.