I am the Emerson Professor of Manufacturing Management and a professor of operations, technology, and information management (OTIM) at the Samuel Curtis Johnson Graduate School of Management in the Cornell SC Johnson College of Business. I conduct research on data-driven and optimization-based analysis of problems in supply chains, retailing, and marketplace operations.

I also serve as the faculty director of Johnson School’s Master of Science in Business Analytics program. This program focuses on teaching business analytics methods, applications, and implementation to students in various career paths. It was founded in 2021 as a part-time online degree program for working professionals who are seeking to develop their careers and grow their skills in business analytics.

I’ve been appointed as the next Anne and Elmer Lindseth Dean of the Johnson School from July 1, 2023, taking over from Dean Mark Nelson.

I joined Cornell in 2007. Previously, I was an assistant professor at NYU Stern during 2000-2007, and a visiting associate professor at Harvard Business School during 2007-08. I graduated from IIT Delhi in 1993 with a B.Tech. in Computer Science & Engineering, received an MBA from IIM Ahmedabad in 1995, and Ph.D. in Operations Management at the Wharton School in 2001.

I live in Ithaca, NY with my wife Shailja and two kids. In my free time, I enjoy hiking on the trails around Ithaca and exploring its rich history.

E-mail: vg77@cornell.edu
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Address: 377 Sage Hall, Ithaca, NY 14853.

For my faculty web page at Cornell University, please click here.


Ph.D. students and graduates

  • Current students: Allu Rakesh, Yuan Cheng, Ziwei Zhu
  • Former students: Kashish Arora (Cornell 2022), Amber Xiaoyan Liu (Cornell 2021), Dayoung Kim (Cornell 2017, co-advisor with Andrew Davis), Arzum Akkas (MIT 2015, committee member/co-advisor with David Simchi-Levi), Joonkyum Lee (Cornell 2014, co-advisor with Suresh Muthulingam), William Schmidt (Harvard 2014, committee member/co-advisor with Ananth Raman), Yasin Alan (Cornell, 2012), Nikolay Osadchiy (NYU 2010, co-advisor with Sridhar Seshadri and Gustavo Vulcano), Saravanan Kesavan (Harvard 2007, committee member/co-advisor with Ananth Raman), Dorothee Honhon (NYU 2006, co-advisor with Sridhar Seshadri)


Firm-level operations management: Firm-level operations management deals with operational performance analysis and operational decisions at the firm level. The ready availability of public firm-level data on inventories and associated variables, and the growing ability to obtain other types of datasets and relate them to operations has made it possible to solve a number of problems in firms ranging from performance assessment to better demand forecasting. My first work in this area was on benchmarking of inventory turnover in 2001. This work was done in the context of U.S. public retailers (see picture below), but has since been used not only in retailing but also in manufacturing and e-commerce businesses.

Other topics that I have worked on in this area include forecasting of sales and gross margins using inventory data, deciphering the information content of inventories for the stock market, and measuring the implications of inventory turnover on the ability to recover from shocks. Some of this work is described in an early article called Linking operations with financial performance. My work in this area also includes managerial articles on inventories and retail lifecycle, and a case study that I have used in the MBA and executive classroom for a number of years. I have co-authored on different papers in this stream with Marshall Fisher, Saravanan Kesavan, Ananth Raman, and Yasin Alan. My current work on this topic deals with forecasting cash flows using inventory data (with Kashish Arora), analyzing growth trajectories of firms under operational constraints (with Yasin Alan), and studying the bullwhip effect empirically (with Maximiliano Udenio and Jan Fransoo).

Annual Inventory Turns Vs. Gross Margin for four consumer electronics retailers, 1987-2000, Figure 1 in this paper.

Financial market information in operations models: Sridhar Seshadri and I showed in the paper Hedging Inventory Risk Through Market Instruments (M&SOM: 2005) that the returns on aggregate stock market indexes were predictive of future retail sales and that this information could be used by retailers for hedging inventory risk. Following on this paper, we have written papers on both theoretical analysis and econometric measurement of this phenomenon including forecasting retail sales using stock market returns, studying the relevance of market incompleteness in the valuation of operations, information sharing in supply chains, and measuring systematic risk with relation to supply chain structure. In current work on this topic, I am studying supply chain structure using network models applied to public data (with Nikolay Osadchiy and Maximiliano Udenio).

Monthly Sales for U.S. Retail, Wholesale trade, and Manufacturing sectors (detrended, deseasonalized, and price-adjusted) compared to returns on the value weighted market index, Figure 1 in this paper.

Retail and Supply Chain Operations: My work in this area includes econometric analysis and theoretical modeling of operational problems faced by firms in retailing and supply chains. These problems include periodic inventory routing in a supermarket chain, assortment optimization (locational choice, stockout-based substitution), sourcing from multiple suppliers, the effect of online marketplaces on sourcing, competition between stores when consumers learn about service levels from repeated interactions in previous visits or social information, price experiments, estimating judgmental uncertainty, etc. Some of this work has been done in collaboration with different companies; for example, we designed an algorithm to solve the periodic inventory routing problem for the distribution network of a retail chain and implemented it at Albert Heijn in the Netherlands (a network consisting of about 1,200 stores, regional distribution centers, and a national distribution center), and we conducted a pilot on assortment optimization and inventory planning for new and used textbooks at the Cornell Store. In one field study bridging inventory management and behavioral operations (see picture below), we found that retail store managers bypassed an automated ordering system and made manual ordering decisions that were superior and that allowed us to improve the automated system.

Weekly Seasonality Pattern of Sales, Automated Store Orders (ASO) Inventory Replenishment System, and Actual Orders in a Retail Store for a Slow-Moving Body Lotion SKU. This picture is made using one year of shipments and sales transaction data, and the ASO orders are generated through a sample path simulation of the replenishment algorithm. Case Pack Size = 6 units, Average Weekly Sales = 2.78 units. [Source: Figure 1(a) in this paper]

Food waste: Food waste is a complex topic where operations management theory can have enormous impact because of the supply chain data gathered from new traceability technologies, new advancements in algorithms, design of incentives and consumer nudges, and business model innovation. My work in this area, with Arzum Akkas, seeks to understand the drivers of expiration of shelf-stable packaged food products and to map out a research agenda on food waste in operations management. We have also written a case study that I use in the MBA classroom.

Root Causes of Different Types of Food Waste, Figure 2 in this paper.

Marketplace Operations: Since 2018, I have been collaborating with IndiaMart, which is an online B2B marketplace in India, to develop machine learning algorithms for matching buyers and sellers on the platform and to set up ML Operations to deploy those algorithms, continuously monitor their performance, and conduct experiments. My research work in this area, based on data from IndiaMart and done in collaboration with Amber Xiaoyan Liu and Allu Rakesh, focuses on algorithm design and empirical analysis for marketplaces. Specifically, Amber and I developed a method to generate personalized ranked recommendations by addressing the problem of class imbalance in historical data and implemented this method at our research collaborator. A version of this algorithm has been successfully deployed on the platform and is used to conduct more than one million B2B transactions every week. This work builds on my previous research on choice estimation and assortment optimization problems in retailing.

Blockchain design for supply chain applications: The success of blockchain adoption in supply chains depends critically on the design of this technology, the role of certification agencies, and the existing market structure. My work in this area includes a managerial article on blockchain design, an article on combining blockchain with IoT, a book chapter, and a research paper, with Yao Cui and Jingchen Liu, studying blockchain governance with respect to horizontal and vertical information sharing induced by the distributed ledger in a supply network. This research has benefitted from my collaboration with DiBiZ, a blockchain technology company focused on sustainable palm oil. DiBiz has developed a solution for tracing palm oil transactions through the stages of its complex supply chain starting from palm fruit to dealers to oil mills to derivative products to CPG manufacturers and finally to retail. The company is addressing challenges such as how to increase sustainable palm oil production and how to get fair prices to the farmers of sustainable palm.

Master of Science in Business Analytics (MSBA)

I am directing the MS in Business Analytics degree program at Cornell, which is a part-time online degree program designed for working professionals in business analytics. The first cohort of this program graduates in August 2023 after a 4-semester, 16-month curriculum. The second cohort begins their academic journey in May 2023. See below for news and media coverage of this program.

News and Media Links

Last revised: May 2023