About

Gad Allon is the Jeffrey A. Keswin Professor and Professor of Operations, Information and Decisions at the University of Pennsylvania. He is also the director of the Management and Technology Program at Wharton. His academic background includes a PhD in Management Science from Columbia Business School and Bachelor and Master degrees from the Israeli Institute of Technology. His research interests encompass operations management, service operations, and operations strategy, with a focus on models of information sharing among firms and customers in service and retail settings, as well as competition models in the service industry. Professor Allon has published in leading journals such as Management Science, Manufacturing and Service Operations Management, and Operations Research. He has received recognition for his research, including the 2011 Wickham Skinner Early-Career Research Award from the Production and Operations Management Society. In addition to his research, he is an award-winning educator, teaching courses on scaling operations and operations strategy, and has been an innovative leader in educational technology initiatives, including co-founding ForClass, a platform aimed at increasing student engagement and accountability. He regularly consults firms on service and operations strategy and writes a weekly newsletter on related topics.

Research topics

• Computer Science
• Business
• Marketing
• Economics
• Operations research
• World Wide Web
• Microeconomics
• Econometrics
• Computer Security
• Telecommunications
• Data science
• Process management
• Knowledge management
• Psychology
• Operations management
• Labour economics
• Computer network

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

• Leveraging Consensus Effect to Optimize Ranking in Online Discussion Boards

  Manufacturing & Service Operations Management · 2025-10-03

  article1st authorCorresponding

  Problem definition: Online discussion platforms (often referred to as discussion boards) are designed for facilitating remote discussions between users. To stimulate engagement (e.g., participation in the discussion), these platforms offer arriving users a ranked list of existing discussion comments. In this paper, we formalize the level of consensus in the discussion and study its impact on engagement and how it could be leveraged by ranking algorithms to increase engagement along the discussion path. Methodology/results: We collaborate with a leading online discussion board for education settings. Analyzing data from online discussions, we identify the level of consensus in the discussion as a new engagement driver. The presence of the consensus effect suggests that ranking algorithms should consider not only comments that would induce engagement in the present period but also ones that would maximize future engagement by managing the desired level of consensus. Based on this insight, we propose a new dynamic model for ranking optimization and a class of intuitive algorithms that, among other factors, account for the level of consensus when prescribing rankings that maximize engagement using a limited lookahead. In a randomized experiment consisting of eight discussion groups in an education setting, our proposed algorithm outperformed the approach used in current practice (that does not actively manage the level of consensus). Managerial implications: Our study proposes consensus as an essential factor in user engagement and in the design of user interface in online platforms and demonstrates the performance improvement that is achievable by leveraging it in the design of ranking algorithms in discussion boards. In doing so, our study suggests that online platforms may often benefit from rankings that build debate rather than an “echo chamber” of consensus. History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0451 .

  PublisherDOI

• AI in Supply Chain Management: Disruptions and Challenges

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

  book-chapter1st authorCorresponding
  PublisherDOI

• Introduction to Special Section on Data-Driven Research Challenge

  Manufacturing & Service Operations Management · 2024-01-01 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Introduction to Special Section on Data-Driven Research Challenge

  Manufacturing & Service Operations Management · 2023-05-01

  article1st authorCorresponding
  PublisherDOI

• Machine Learning and Prediction Errors in Causal Inference

  SSRN Electronic Journal · 2023-01-01 · 4 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Managing Multihoming Workers in the Gig Economy

  SSRN Electronic Journal · 2023-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Measuring Strategic Behavior by Gig Economy Workers: Multihoming and Repositioning

  SSRN Electronic Journal · 2023-01-01 · 13 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• The Impact of Behavioral and Economic Drivers on Gig Economy Workers

  Manufacturing & Service Operations Management · 2023 · 98 citations

  1st authorCorresponding
  • Economics
  • Microeconomics
  • Labour economics

  Problem definition: Gig economy companies benefit from labor flexibility by hiring independent workers in response to real-time demand. However, workers’ flexibility in their work schedule poses a great challenge in terms of planning and committing to a service capacity. Understanding what motivates gig economy workers is thus of great importance. In collaboration with a ride-hailing platform, we study how on-demand workers make labor decisions; specifically, whether to work and work duration. Our model revisits competing theories of labor supply regarding the impact of financial incentives and behavioral motives on labor decisions. We are interested in both improving how to predict the behavior of flexible workers and understanding how to design better incentives. Methodology/results: Using a large comprehensive data set, we develop an econometric model to analyze workers’ labor decisions and responses to incentives while accounting for sample selection and endogeneity. We find that financial incentives have a significant positive influence on the decision to work and on the work duration—confirming the positive income elasticity posited by the standard income effect. We also find support for a behavioral theory as workers exhibit income-targeting behavior (working less when reaching an income goal) and inertia (working more after working for a longer period). Managerial implications: We demonstrate via numerical experiments that incentive optimization based on our insights can increase service capacity by 22% without incurring additional cost, or maintain the same capacity at a 30% lower cost. Ignoring behavioral factors could lead to understaffing by 10%–17% below the optimal capacity level. Lastly, our insights inform the design of platform strategy to manage flexible workers amidst an intensified competition among gig platforms. Funding: This study was supported by The Jay H. Baker Retailing Center, The William and Phyllis Mack Institute for Innovation Management, The Wharton Risk Management and Decision Processes Center, and The Fishman-Davidson Center for Service and Operations Management. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1191 .

  PublisherDOI

Frequent coauthors

• Achal Bassamboo

  30 shared

• Awi Federgruen

  12 shared

• Jan A. Van Mieghem

  9 shared

• Wichinpong Park Sinchaisri

  7 shared

• Dennis Zhang

  Washington University in St. Louis

  6 shared

• Qiuping Yu

  Georgetown University

  6 shared

• Eren B. Çil

  University of Oregon

  5 shared

• Kimon Drakopoulos

  University of Southern California

  4 shared

Labs

• Operations, Information and Decisions DepartmentPI

Awards & honors

• Wickham Skinner Early-Career Research Award (2011)
• Media coverage - Kellogg Insights
• Ideas for Leaders Finalist, Service Science Student Paper Co…
• Second Prize, CMU YinZOR Best Student Poster (2022)