Dr. Hailong Cui, University of Minnesota, Carlson School of Management
224 Church St SE
Minneapolis,
MN
55455
Replacing What Could Be Repaired: A Structural Analysis of Two-Stage Diagnostic Decisions in Managing Shared-Bike Returns
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. Full document linked here.
Bio
Hailong Cui is an Assistant Professor in the Supply Chain and Operations Department at the Carlson School of Management, University of Minnesota (UMN). He also holds affiliated faculty roles in UMN’s Industrial & Systems Engineering Department and its system-wide Institute on the Environment.
Dr. Cui joined UMN in 2020 following the completion of his Ph.D. in Data Sciences and Operations at the Marshall School of Business, University of Southern California, under the supervision of Professors Greys Sošić, Sampath Rajagopalan, and Amy R. Ward. Before pursuing his doctoral studies, he gained substantial professional experience in the financial services industry, holding positions at prominent institutions such as American Express and HSBC in the United States. Earlier in his academic journey, he earned dual M.S. degrees in Statistics and Computational Biosciences from the School of Mathematical and Statistical Sciences at Arizona State University. He also holds a B.S. in Mathematics with a minor in Computer Science and Engineering from POSTECH in South Korea. He is fluent in English and a native speaker of both Mandarin and Korean.
Dr. Cui's primary research centers on Supply Chain Management and Retail Operations. Leveraging a broad array of analytical methodologies, including optimization, game theory, statistics and econometrics, structural estimation, and machine learning, he investigates innovative mechanisms to advance environmental sustainability. Concurrently, his research focuses on operational levers aimed at improving economic efficiency while addressing inequities impacting consumers and small businesses. His research has been published in journals including Management Science, Manufacturing & Service Operations Management, and European Journal of Operational Research.