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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20261001T153000
DTEND;TZID=Asia/Singapore:20261001T170000
DTSTAMP:20260928T114002Z
CREATED:20260928T114002Z
LAST-MODIFIED:20260928T114002Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: René Caldentey
DESCRIPTION:Name of Speaker\n\n\nRené Caldentey\n\n\n\n\nSchedule \n\n\n1 Oct 2026\, 3.30pm – 5pm \n (60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nBIZ1 0301\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/oE2qGh5lRpGlisu-MI8QIg\n\n\n\n\nTitle\n\n\nManaging Inventory and Information in Supply Chains\n\n\n\n\nAbstract \n\n\nWe study how information sharing and replenishment policies shape performance in a two-tier supply chain. Retailer orders serve as material requests and informational signals\, so ordering decisions affect inventory flows and the information available upstream. We study these mechanisms across supplier fulfillment modes\, from guaranteed delivery to settings in which supplier forecasts and inventory availability endogenously affect retailer\nReplenishment.\n\nWe derive an inventory decomposition that transforms the retailer’s infinite-dimensional ordering problem into variance-minimization or finite-dimensional covariance-design problems. Optimal ordering depends sharply on the fulfillment environment. Without supplier inventory shortfalls\, the retailer filters predictable demand variation from its order stream; for finite-memory demand\, this can generate iid orders even when demand is serially correlated. With inventory-based fulfillment\, optimal policies are sparse and lead-time-periodic\, front-loading the response to new demand information and later reversing part of that response\, which can generate bullwhip. For a fixed ordering policy\, demand sharing can help or hurt the retailer\, while supplier-side gains require noninvertible orders. Once the retailer optimizes its ordering policy\, full information sharing is weakly optimal whenever supplier information affects material flows and becomes immaterial when optimal orders are invertible. Numerical experiments\, including Amazon’s Chronos-2 forecasts and retail-sales data\, support these findings.\n\n\n\n\nAbout the Speaker\n\n\nRené Caldentey is a Professor of Operations Management at the the University of Chicago Booth School of Business. His primary research interests include stochastic modeling with applications to revenue and retail management\, queueing theory\, inventory management\, and finance. He has been published in numerous journals including Advances in Applied Probability\, Econometrica\, Management Science\, Mathematics of Operations Research\, M&SOM\, Operations Research and Queueing Systems. He serves on the editorial board of Management Science\, M&SOM\, Naval Research Logistics\, Operations Research and  Production and Operations Management.\n\nPrior to joining Booth\, Caldentey was a professor in the department of Information\, Operations and Management Science at New York University Stern School of Business. Before joining NYU Stern in 2001\, he worked for the Chilean Central Bank and taught at the University of Chile and The Sloan School of Management at Massachusetts Institute of Technology (MIT).\n\nProfessor Caldentey received his Master of Arts in civil industrial engineering from the University of Chile and his Doctor of Philosophy in operations management from MIT.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-rene-caldentey/
CATEGORIES:IORA Seminar Series
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DTSTART;TZID=Asia/Singapore:20261014T100000
DTEND;TZID=Asia/Singapore:20261014T113000
DTSTAMP:20261005T083149Z
CREATED:20261005T083149Z
LAST-MODIFIED:20261005T083149Z
UID:28681-1791972000-1791977400@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series: Jing Dong
DESCRIPTION:Name of Speaker\n\n\nJing Dong\n\n\n\n\nSchedule \n\n14 Oct 2026\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nBIZ1 0305\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/pewK2LZ4SZeJWbzoUudBlg\n\n\n\n\nTitle\n\n\nAI for Scalable and Practical Queueing Network Modeling and Control\n\n\n\n\nAbstract \n\n\nQueueing network models are powerful mathematical modeling tools for managing congestion across various applications\, yet broader adoption remains limited due to challenges in accessibility and scalability. This talk explores how recent advances in AI can enable more accessible and practical applications of this modeling tool. We first present a model-based reinforcement learning framework\, called differentiable discrete event simulation\, to solve general queueing network control problems. Our approach enables efficient and scalable policy gradient evaluation using auto-differentiation software. We then present a new data-driven modeling framework based on autoregressive sequence models. This framework allows us to utilize the rich operational data to automatically learn and represent system dynamics. Together\, these methods demonstrate how AI may help enhance the reach and usability of queueing models\, helping address operational challenges across diverse domains.\n\n\n\n\nAbout the Speaker\n\n\nJing Dong is the DeRosa Family Associate Professor of Business in the Decision\, Risk\, and Operations Division at Columbia Business School and a visiting faculty member at the Antai College of Economics and Management at Shanghai Jiao Tong University. Her research lies at the intersection of applied probability and service operations management\, with a particular focus on patient flow management in healthcare delivery systems. She serves as an associate editor for Operations Research\, Mathematics of Operations Research\, Management Science\, Manufacturing & Service Operations Management\, and the INFORMS Journal on Computing\, and as vice chair of the INFORMS Applied Probability Society. She received her Ph.D. in Operations Research from Columbia University and was on the faculty at Northwestern University before joining Columbia Business School.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-jing-dong/
CATEGORIES:IORA Seminar Series
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