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Subject(s)
Management sciences, decision sciences and quantitative methods
Keyword(s)
learning and earning, dynamic pricing, advertising, inventory management
ISSN (Online)
1526–5498
ISSN (Print)
1523-4614
Subject(s)
Management sciences, decision sciences and quantitative methods
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
machine-learning, rational inattention, human-machine collaboration, cognitive effort
JEL Code(s)
L23, M11, O33, O31, D24
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, decisionmaking, 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.
© 2026, INFORMS
ISSN (Online)
1526–5498
ISSN (Print)
1523-4614
Subject(s)
Human resources management/organizational behavior
Keyword(s)
playification, leadership development, creativity, collaboration, hybrid work, virtual teams, psychological safety, innovation, experiential learning, organizational adaptability
Subject(s)
Technology, R&D management
Keyword(s)
paradox, dysfunctional dynamics, vicious cycle, goals, longitudinal qualitative, process model
Subject(s)
Diversity and inclusion; Human resources management/organizational behavior
Keyword(s)
gender, backlash, negotiation, bargaining, gender gap
ISSN (Online)
1939-2222
ISSN (Print)
0096-3445
Subject(s)
Health and environment; Management sciences, decision sciences and quantitative methods
Keyword(s)
vaccination campaign, fractional-dose vaccines, epidemiology, optimal Control
JEL Code(s)
L18; C51; C54; C61; C63
ISSN (Online)
1526–5498
ISSN (Print)
1523-4614
Subject(s)
Economics, politics and business environment
Keyword(s)
price dispersion, stability, price competition, consideration sets
JEL Code(s)
D43 L11
We study the pricing of homogeneous products sold to customers who consider different sets of suppliers. We identify prices that are stable in the sense that no firm wishes to undercut a rival or to raise its price when rivals are able to respond by offering special deals. We derive predictionsforstable and disperse prices acrossseveral price-consideration specifications, and we contrast the implications with those of conventional approaches.
[This paper supersedes working papers Stable Price Dispersion (2024) and A Theory of Stable Price Dispersion (2019).]
© 1999-2026 John Wiley & Sons, Inc
© 1999-2026 John Wiley & Sons, Inc
Subject(s)
Marketing; Strategy and general management
Keyword(s)
centralization, decentralization, deglobalization, organizational structure
JEL Code(s)
F23, L22, M16, M31
ISSN (Online)
1758-6763
ISSN (Print)
0265-1335