2001 Sheridan Rd, Operations Department, Evanston, IL 60208
Ruomeng CuiPh.D. Candicate
Ph.D.,Operations Management, Kellogg School of Management, Northwestern University (Expected 2014)
I am a doctoral candidate in Managerial Economics and Decision Sciences at the Kellogg School of Management with a research focus on supply chain management. My work centers on information sharing and signal propagation in supply chains and their effects on operational performance. My research bridges empirical observations and theoretical interpretations of forecasting improvements from sharing downstream information. I conduct empirical research to assess the role of social media information in firms’ performance improvements. I also conduct theoretical research in firms’ inventory information disclosing strategies and evaluation in inventory stocking policies.
Research Interests: Supply Chains, Information Sharing, Forecasting, Empirical Studies in Operations, Inventory Management, Social Media Data in Operations
Job Market Paper
Information sharing in supply chains: An empirical and theoretical valuation, Management Science, major revision for first-round review, Sep 2013, with Gad Allon, Achal Bassamboo and Jan A. Van Mieghem.
- Presented at MSOM 2013, INFORMS 2012, INFORMS 2013 (scheduled), Wharton Empirical Workshop in Operations Management 2013 (scheduled).
We empirically assess the value of sharing downstream sales information in a two-stage supply chain and develop a new theoretical framework that is supported by a data set collected from a CPG company. Even though the theoretical model in the literature suggests that the value of information sharing should be zero for over half of our studied products, we empirically show that the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all products. To reconcile the gap between the literature and the empirical observations, we allow for "decision deviations." While the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate from a policy because of private information that is not observed by the econometrician. Such deviations are prevalent in our data set. It turns out that the decision deviations lead to information losses in demand propagation, resulting in a strictly positive value of downstream information sharing. Furthermore, we generalize this result from the autoregressive integrated moving average (ARIMA) setting to the Martingale Model of Forecast Evolution (MMFE) demand and the generalized order-up-to policy (GOUTP).
- Sharing aggregate inventory information with customers: A strategic way of cross-selling, with Hyoduk Shin.
- Presented at POMS 2012, INFORMS 2011, INFORMS 2013 (scheduled).
- ConDOI replenishment policy under MMFE demand, with Gad Allon, Achal Bassamboo and Jan A. Van Mieghem.
- The value of social media data in supply chains: An empirical study, with Santiago Gallino, Antonio Moreno-Garcia and D. Zhang.