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

Manufacturing & Service Operations Management · 2026-03-26

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.

Human-Algorithm Collaboration in Gig Work: The Role of Experience, Skill Level, and Task Complexity

Information Systems Research · 2026-02-09 · 1 citations

In this paper, we contribute to recent studies on human-algorithm collaboration by examining how experience, skill level, workload, and task complexity shape the impact of an algorithm-enabled decision-support tool for gig workers. We leverage a large-scale randomized field experiment on the Instacart platform from June 2022 to September 2022. The algorithm-enabled technology aims to revolutionize item picking by helping shoppers locate and collect items more efficiently, reducing picking time while maintaining service quality, as reflected by refund rates. We find that the technology complements experience: rather than diminishing the value of experience, it yields larger improvements for more experienced shoppers. We also find that it substitutes for skill levels by helping lower-skilled workers bridge the performance gap with higher-skilled peers, but lower-skilled workers need experience to fully benefit from the tool. Finally, treatment effects vary with workload and task complexity, clarifying when algorithmic guidance is most valuable. For policymakers, our findings suggest a simple rule: give workers some baseline experience before introducing AI tools, using a staggered rollout with basic training. We also show that these tools can make service more consistent by closing the gap between high performers and lower performers, reducing performance dispersion, and helping standardize quality.

Development Operations Management: From Frictions to Theory

SSRN Electronic Journal · 2026-01-01

Value of Silence: Determinants and Consequences of Greenhushing

SSRN Electronic Journal · 2026-01-01

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

UNC Libraries · 2026-04-09

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.

When Where Watt: Harnessing the Value of Time and Location for Renewable Electricity Generation

SSRN Electronic Journal · 2025-01-01

Robust Contracting in Supply Chains Under Incomplete Demand Information

SSRN Electronic Journal · 2025-01-01

The Invisible Backbone: How Supply Chains Bring AI to Life

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

From Information Aggregators to Transaction Facilitators: An Empirical Investigation of the Impact of the Platform Payment Integration

SSRN Electronic Journal · 2025-01-01

Residential Battery Storage—Reshaping the Way We Do Electricity

Operations Research · 2025-10-09

Household investments in renewable energy technologies like rooftop solar and battery storage can be driven by motivations beyond mere financial returns. In “Residential Battery Storage— Reshaping the Way We Do Electricity,” Christian Kaps and Serguei Netessine develop a structural estimation model that separates observed electricity demand from underlying consumption preferences, enabling the estimation of a nonfinancial utility that households derive from using more self-generated solar power rather than grid-procured electricity. The authors call this utility nonmarket valuation; provide evidence that it is driven by sustainability and autarky desires; and link it to the early adoption of residential storage. Applying their model to a novel data set of German households with solar and storage installations, they find that the median household has a nonmarket valuation of 0.29 euros per kilowatt-hour. Additionally, the authors demonstrate that residential storage can have unexpected effects. They show a “rebound effect,” whereby households with storage increase their overall electricity consumption by 4%. Counterintuitively, under Germany’s observed grid mix, residential storage on average increases carbon emissions. However, they also find that demand from the grid decreases by 38% for the average household equipped with solar and storage.