Forging a New Discipline: Data-driven Supply Chain Management

October 03 - 05, 2018

Data Science and Supply Chain Management

Content

Overview

Advisory:

Data science working groups at the IMA

Working groups can consist of 2-10 collaborators spending 1-5 days at the IMA. Applicants should complete a short online application and email a one-page proposal to datascience@ima.umn.edu. More details about the proposal requirements are available on the application. Please contact the datascience@ima.umn.edu with additional inquiries.

Please note only one participant of the working group needs to fill out the online application and submit the proposal to datascience@ima.umn.edu. The submitted proposal should include name, affiliation, and email address of all collaborators.

Poster:

W10.3-5.18_poster.pdf

Organizers:

Shipra Agrawal	Columbia University
J. George Shanthikumar	Purdue University
David Simchi-Levi	Massachusetts Institute of Technology

Data analytics has become ever more important in business and industry, driven partly by availability of data and by progress in computing. Yet the science of data analytics for supply chain management is still at a nascent stage. We are still far from directly transforming data into executable supply chain decisions.

The size and the variety of data pose a challenge. It is critical to identify the key features in the data that allow for effective decision-making in highly dynamic business environments. Everyone agrees on the need for data exchange to enable coordination mechanisms and enhance overall capability of supply chain networks, but there have been endless debates on the nature of appropriate data to be exploited. The velocity of data coming from internal operations, IoT, and public domains requires a careful choice on what data should be collected and stored. Progress can only be accomplished when machine learning/artificial intelligence expertise are integrated with domain knowledge of supply chains.

The first workshop will explore the avenues by which new methodologies and theories should be developed to push the frontier of data analytics specifically for supply chain management. We plan to explore and expand the existing data analytics approaches for supply chain management and develop data-driven structure models for addressing supply chain management issues with machine learning/artificial intelligence.

Schedule

Wednesday, October 03, 2018

8:00 am - 8:45 am	Coffee and registration	Keller 3-176
8:45 am - 9:00 am	Welcome and introduction	Keller 3-180
9:00 am - 9:45 am	Spotting Influential Customers for Targeted Offers: From Social to Nonsocial Georgia Perakis (Massachusetts Institute of Technology)	Keller 3-180
9:45 am - 10:30 am	Procurement in the twenty first century: New approaches to old problems Awi Federgruen (Columbia University)	Keller 3-180
10:30 am - 11:00 am	Break	Keller 3-176
11:00 am - 11:45 am	Crowdsourcing Operations: Incentivizing Bike Angels in America David Shmoys (Cornell University)	Keller 3-180
11:45 am - 12:30 pm	Auction Design for ROI-Constrained Buyers Ilan Lobel (New York University)	Keller 3-180
12:30 pm - 2:00 pm	Lunch
2:00 pm - 2:45 pm	Predicting with Proxies Hamsa Bastani (Wharton School of the University of Pennsylvania)	Keller 3-180
2:45 pm - 3:30 pm	Interpretable Optimal Stopping Velibor Misic (University of California, Los Angeles)	Keller 3-180
3:30 pm - 4:00 pm	Break	Keller 3-176
4:00 pm - 4:45 pm	Online Assortment Optimization with Reusable Resources Vineet Goyal (Columbia University)	Keller 3-180
4:45 pm - 5:30 pm	A Conditional Gradient Approach for Nonparametric Estimation of Mixing Distributions Srikanth Jagabathula (New York University)	Keller 3-180
6:00 pm - 8:00 pm	Social hour at Republic

Thursday, October 04, 2018

8:30 am - 9:00 am	Coffee	Keller 3-176
9:00 am - 9:45 am	Behavioral Analytics for Myopic Agents Philip Kaminsky (University of California, Berkeley)	Keller 3-180
9:45 am - 10:30 am	Approximation Algorithms for Network Revenue Management Huseyin Topaloglu (Cornell University)	Keller 3-180
10:30 am - 10:40 am	Group photo	Keller 3-180
10:40 am - 11:00 am	Break	Keller 3-176
11:00 am - 11:45 am	Smart Predict, then Optimize Adam Elmachtoub (Columbia University)	Keller 3-180
11:45 am - 12:30 pm	Panel	Keller 3-180
12:30 pm - 2:00 pm	Lunch
2:00 pm - 2:45 pm	Joint pricing and inventory control models with long lead times Xin Chen (University of Illinois at Urbana-Champaign)	Keller 3-180
2:45 pm - 3:30 pm	Distributionally Robust Linear and Discrete Optimization with Marginals Karthik Natarajan (Singapore University of Technology and Design)	Keller 3-180
3:30 pm - 4:00 pm	Break	Keller 3-176
4:00 pm - 4:45 pm	Closing the Gap: A Learning Algorithm for the Lost-Sales Inventory System with Lead Times Cong Shi (University of Michigan)	Keller 3-180
4:45 pm - 5:30 pm	Dynamic Assortment Planning under Various Discrete Choice Models Xi Chen (New York University)	Keller 3-180

Friday, October 05, 2018

8:30 am - 9:00 am	Coffee	Keller 3-176
9:00 am - 9:45 am	Omni-Channel Order Fulfillment: From Concept to Practice Vivek Farias (Massachusetts Institute of Technology)	Keller 3-180
9:45 am - 10:30 am	Where to find the next passenger on e-hailing platform? - A reinforcement learning approach Sharon Di (Columbia University)	Keller 3-180
10:30 am - 11:00 am	Break	Keller 3-176
11:00 am - 11:45 am	Design of Information Sharing Mechanisms in Service Systems Krishnamurthy Iyer (Cornell University)	Keller 3-180
11:45 am - 12:30 pm	Sample-Based Optimal Pricing Omar Besbes (Columbia University)	Keller 3-180

Participants

Name	Department	Affiliation
Shipra Agrawal	Department of Industrial Engineering and Operations Research	Columbia University
Doane Baber	Supply Chain Process	Cargill, Inc.
Dustin Barvels		Cargill, Inc.
Hamsa Bastani	Department of Operations, Information & Decisions	Wharton School of the University of Pennsylvania
Saif Benjaafar	Department of Industrial and Systems Engineering	University of Minnesota, Twin Cities
Omar Besbes	Decision, Risk & Operations Division	Columbia University
Xiuli Chao	Department of Industrial & Operations Engineering	University of Michigan
Xi Chen	Department of Information, Operations and Management	New York University
Xin Chen	Department of Industrial and Enterprise Systems Engineering	University of Illinois at Urbana-Champaign
Allen Clark	School of Statistics	University of Minnesota
Sharon Di	Department of Civil Engineering and Engineering Mechanics	Columbia University
Jie Ding	School of Statistics	University of Minnesota, Twin Cities
Adam Elmachtoub	Department of Industrial Engineering and Operations Research	Columbia University
Alexander Estes	Institute for Mathematics and its Applications	University of Minnesota, Twin Cities
Vivek Farias	Sloan School of Management	Massachusetts Institute of Technology
Awi Federgruen	Decision, Risk and Operations Division	Columbia University
Sandeep Gangarapu	Carlson School of Management	University of Minnesota, Twin Cities
Natarajan Gautam	Department of Industrial and Systems Engineering	Texas A & M University
Vineet Goyal	Department of Industrial Engineering and Operations Research	Columbia University
Qie He	Department of Industrial and Systems Engineering	University of Minnesota, Twin Cities
Krishnamurthy Iyer	School of Operations Research and Information Engineering	Cornell University
Srikanth Jagabathula	Department of Information, Operations, and Management Sciences	New York University
Tikhon Jelvis		Target Corporation
Philip Kaminsky	Department of Industrial Engineering and Operations Research (IEOR)	University of California, Berkeley
Prasad Kandlakunta	EDABI	Target Corporation
Sang-Phil Kim	Business Administration	Winona State University
Guangwen Kong	Department of Industrial and Systems Engineering	University of Minnesota, Twin Cities
Xiaobo Li		National University of Singapore
Yiran Li	Institute for Mathematics and its Applications	University of Minnesota, Twin Cities
Ilan Lobel	Department of Information, Operations, and Management Sciences	New York University
Quan Long	Systems Department	United Technologies Corporation
Mengshi Lu	Krannert School of Management	Purdue University
Sona Maniyan	Enterprise Data Analytics and Business Intelligence	Target Corporation
Tyler Mecham	Division of Production & Supply	Syngenta
Velibor Misic	Department of Decisions, Operations and Technology Management	University of California, Los Angeles
Karthik Natarajan	Department of Engineering Systems and Design	Singapore University of Technology and Design
Georgia Perakis	Department of Operations Research	Massachusetts Institute of Technology
Joe Petrulo		Cargill, Inc.
Behrooz Pourghannad	Department of Industrial and Systems Engineering	University of Minnesota, Twin Cities
Bharath Rangarajan	Department of Data Sciences	Target Corporation
Michael Redmond	Tippie College of Business	The University of Iowa
John Rios	Department of Management Sciences	The University of Iowa
Stuart Rogers	Institute for Mathematics and its Application	University of Minnesota, Twin Cities
Abhishek Roy		Cargill, Inc.
Minseok Ryu	Department of Industrial and Operations Engineering	University of Michigan
J. George Shanthikumar	Department of Supply Chain and Operations Management	Purdue University
Justin Shelite		Cargill, Inc.
Cong Shi	Department of Industrial & Operations Engineering	University of Michigan
David Shmoys	School of Operations Research and Information Engineering	Cornell University
David Simchi-Levi	Department of Civil and Environmental Engineering	Massachusetts Institute of Technology
Kingshuk Sinha	Carlson School of Management	University of Minnesota, Twin Cities
Yongjia Song	Department of Industrial Engineering	Clemson University
Kyle Stahl		Cargill, Inc.
Jack Starr	Department of Engineering R&D	Cargill, Inc.
Luna Su	Production and Supply	Syngenta
Gautham Sunder	Department of Industrial and Systems Engineering	University of Minnesota, Twin Cities
Jayashankar Swaminathan	Operations, Technology and Innovation Management	University of North Carolina, Chapel Hill
Chandrashekar Tallichetty	Department of Data Science and Optimization	Target Corporation
Chung-Piaw Teo	Department of Analytics & Operations	National University of Singapore
Jeff Thaler		Cargill, Inc.
Elif Tokar-Erdemir	Data Science and Engineering	Target Corporation
Vladimir Tonchev	Department of Mathematics	Michigan Technological University
Huseyin Topaloglu	School of Operations Research and Information Engineering	Cornell University
Zachary Voller	Enterprise Data Analytics & Business Intelligence	Target Corporation
Ruyuan Wan	Department of Computer Science and Engineering	University of Minnesota, Twin Cities
Yining Wang	School of Computer Science	Carnegie Mellon University
Ruel White		Cargill, Inc.
Di Wu	Big Data and Smart Supply Chain	JD.COM
Henry Wyneken	Seeds Production Research	Syngenta
Weijun Xie	Department of Industrial and Systems Engineering	Virginia Polytechnic Institute and State University
Guanglin Xu	Institute for Mathematics and its Application	University of Minnesota, Twin Cities
Elena Yudovina	Department of IT	CH Robinson
Qi Zhang	Department of Chemical Engineering and Materials Science	University of Minnesota, Twin Cities