About

Gérard Cachon is the Fred R. Sullivan Professor of Operations, Information and Decisions at the Wharton School of the University of Pennsylvania. His research focuses on supply chain management, operation strategy, and pricing, with particular attention to how technology transforms competitive dynamics and enables novel operational strategies. He has contributed significantly to the understanding of operations management through his authorship of textbooks such as 'Operations Management' and 'Matching Supply with Demand,' which are used in undergraduate, MBA, and executive education courses worldwide. Professor Cachon is recognized as an INFORMS Fellow and has served as a Fellow and former President of the Manufacturing and Service Operations Management Society. He has also held the position of Editor-in-Chief for leading journals including Management Science and Manufacturing & Service Operations Management. His research articles have been published in prominent journals such as Management Science, Marketing Science, Operations Research, and the Quarterly Journal of Economics, among others. His work often explores practical issues such as supply chain strategies, pricing control, online service platform regulation, free shipping policies, and inventory management, providing data-driven insights and analytical models to improve business performance.

Research topics

• Computer Science
• Political Science
• Sociology
• Economics
• Industrial organization
• Microeconomics
• Marketing
• Business
• Finance
• Public administration
• World Wide Web
• Demography
• Law
• Demographic economics

Selected publications

• OM Forum—Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community

  UNC Libraries · 2026-04-09

  articleOpen access

  Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human–machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI’s promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human–AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.

  PublisherDOI

• OM Forum—Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community

  Manufacturing & Service Operations Management · 2026-03-26

  article

  Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human–machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI’s promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human–AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.

  PublisherDOI

• The Current and Future Impact of AI on Supply Chains

  Springer series in supply chain management · 2025-12-10 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• The Allure of Free Shipping: How to Choose the Best Policy for Online Retail

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Rented Today, Bought Tomorrow: Buyout Pricing in the Circular Economy

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• The Human-AI Contracting Paradox

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen accessSenior author
  PublisherDOI

• Pricing Control and Regulation on Online Service Platforms

  Management Science · 2025-08-18 · 7 citations

  article1st authorCorresponding

  An open debate in platform design is who should control pricing: the platform (centralized pricing) or its service providers (decentralized pricing). We show that a key trade-off is between regulating competition and enabling price tailoring. Centralized pricing allows the platform to manage competition, but it faces information asymmetry as it cannot observe agent costs. Decentralized pricing lets agents adjust prices to their costs, but without oversight, competition can become too strong (prices too low) or too weak (prices too high). For commission-based platforms, either form of price control can prevail depending on market conditions, implying that neither dominates. However, a relatively simple tweak—adopting an affine fee structure based on posted prices or quantities served—allows the platform to decentralize pricing control without sacrificing optimality. This flexibility further supports agent classification as independent contractors, offering platforms a valuable strategic option for how to structure their workforce. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Funding: Supporting grants have been provided by the Mack Institute for Innovation Management and the Ripple University Blockchain Research Initiative. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04078 .

  PublisherDOI

• The Enigma of Ticket Exchanges (and Other Reselling Markets)

  SSRN Electronic Journal · 2024-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• The Fast and Affordable Delivery Problem

  SSRN Electronic Journal · 2024 · 1 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  PublisherDOI

• Democracy on the Line: Polling Place Closures in Georgia and the Wait Time to Vote

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Pnina Feldman

  Boston University

  14 shared

• Martin A. Lariviere

  Northwestern University

  8 shared

• Marcelo Olivares

  Complex Engineering System Institute

  8 shared

• Serguei Netessine

  7 shared

• Christian Terwiesch

  University of Pennsylvania

  6 shared

• Robert Swinney

  5 shared

• Santiago Gallino

  University of Pennsylvania

  4 shared

• Ruben Lobel

  3 shared

Awards & honors

• INFORMS Fellow, 2015
• Manufacturing and Service Operations Management Society Fell…
• Penn Fellow, 2011
• M&SOM Best Paper Award Finalist 2010 for "In Search of the B…
• MSOM Society Service award, 2009