Research

Publications

1. Inventory Productivity and Stock Returns in Manufacturing Networks
   Agrawal, D., Osadchiy, N. (2024)
   Manufacturing & Service Operations Management, 26(2): 573–593
   2025 MSOM iFORM SIG Best Paper AwardBest Presenter Award, 2023 Goizueta Doctoral Research Conference
   PaperDashboard

   Abstract

   Problem definition: We provide a novel, supply network-based perspective on inventory productivity and incentives for its improvement. Methodology/results: Using data from 2003 to 2019, we find that inventory productivity is lower materially and statistically for firms located upstream in the supply network, and higher for high degree and more central firms. Firms with high inventory productivity show high equity valuations and abnormal returns, with both valuations and abnormal returns amplified for upstream, low degree, and peripheral firms. Moreover, the difference in valuations and abnormal returns between best and worst performing firms is greater upstream, suggesting that financial markets offer outsized rewards for improving inventory productivity to upstream firms. Managerial implications: We show that the information about firm’s upstreamness and centrality in the supply network is a valuable predictor of its inventory productivity and financial performance. Our methods for evaluating upstreamness are useful for that purpose. For operations managers and firm executives, our results highlight strong incentives for inventory productivity improvement upstream in the supply network. For investors, we show that supply network position data can sharpen inventory-based arbitrage opportunities.

Under Review

1. Value Capture in Supply Networks
   (with Gad Allon, Nikolay Osadchiy)
   SSRNDashboard

   Abstract

   The value chain smile is a popular concept stating that upstream and downstream activities create more value than midstream activities and, therefore, firms should move toward an end of the value chain. Prior studies test this proposition using upstreamness measures anchored to final demand and find inconsistent evidence about the existence and morphology of the smile. We develop a local value chain curve anchored on a focal firm that maps its margin relative to those of its direct and indirect suppliers and customers. We find that firms with higher R&D and SG&A intensities are more likely to occupy the top of their local value chains. Transitions into the inverted-U shape are associated with persistently high R&D intensity and increased SG&A intensity, without systematic changes in the firm’s upstreamness position. These findings help reconcile previous mixed evidence on the value-chain smile and identify firm capabilities associated with superior relative margin capture in local supply networks.

2. Using MELD 3.0 for Predicting Emergent and Non-Emergent TIPS Outcomes
   (with Justin-James Chua, Mustafa Akan, Tulio L. Correa, Andy Wu, Kiran M. Bambha, Scott W. Biggins, Paula M. Novelli)

Work in Progress

1. Transparency Without Parity
   (with Satyam H Verma, Amin Hosseininasab, Sridhar Tayur)
   Preparing for submission to Management Science
2. Empirical Analysis of Geographical Diversification in Supply Chain Network
   (with Nikolay Osadchiy)
   Abstract

   We investigate the operational efficiency and resiliency in the global supply network using a geospatial analysis approach. Traditionally, diversification is considered a tool for risk mitigation. However, we find that diversification not only mitigates risk but also enhances operational efficiency. We find a U-shaped relationship between diversification (both upstream and downstream) and resiliency, and an inverted U-shaped relationship between customer diversification and inventory turnover. For managers, our results suggest optimal diversification for dual benefits.

3. Trade Credit or Bank Credit: A Supply Network Perspective
   (with Shengxing Zhang)
4. Inventory Management and Service Network Optimization Using Downstream Data and AI Support Agent
   (with Ramnath K. Chellappa)

Book Chapter and Case Studies

In Pochiraju, B. and Seshadri, S. (eds.), Essentials of Business Analytics, Springer, 2019.

1. Probability & Statistics
   Taori, P., Mamidipudi, S., Agrawal, D.
   pp. 945–963
2. Case Study: InfoMedia Solutions
   Agrawal, D., Mamidipudi, S., Padmanabhan, S.
   pp. 873–887
3. Case Study: AAA Airline
   Agrawal, D., Kollipara, H.S.S., Mamidipudi, S.
   pp. 863–871
4. Case Study: Ideal Insurance
   Agrawal, D., Mamidipudi, S.
   pp. 847–862

Presentations

Honors and Awards

• 2026Industry Studies Association Dissertation Award, Finalist
• 2025MSOM iFORM SIG Best Paper Award, Winner
• 2023Best Presenter, Goizueta Doctoral Research Conference
• 2023Digital Dissertation Scholarship, Emory University
• 2022–2023Laney Travel Grants
• 2021Sheth Fellowship
• 2019–2024Goizueta Doctoral Student Scholarship
• 2014–2015NSE–NYU Stern Research Grant
• 2007Gnosis Award, first rank in Information Systems

Industry Experience

• WNS Global Services, Bengaluru, India2011–2013
  Deputy Manager, Research and Analytics (General Insurance)
  Fraud analytics using machine learning and network analysis; telematics proof of concept
• DenuoSource India, Hyderabad, India2011
  Associate Business Analyst, Business Intelligence and Analytics (Retail and CPG)
  Business intelligence reporting and campaign analysis
• Kirtane Pandit Consulting, India2007
  Management Consultant
  Market research and new product launch (automobile industry)