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TRUVACE RECORD VERSION
record: TRV-2026-0739
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-12T14:26:07.528303Z
status: published
lens: g_space
sector: science
headline: Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation
dek: This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM).Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas.These areas include but are not limited to demand forecasting, inventory management, supply chain de…
gain_title: Generative AI applied to supply chain and operations management can enhance decision-making and optimize processes across areas like demand forecasting and inventory management to improve efficiency, accuracy, and resilience.
problem_title: (none)
trace_subject: (none)
gain_reading: Generative AI applied to supply chain and operations management can enhance decision-making and optimize processes across areas like demand forecasting and inventory management to improve efficiency, accuracy, and resilience.
gain_evidence: deliver improved efficiency, accuracy, resilience, and overall effectiveness | decision-making enhancement, process optimisation, investment prioritisation, and skills development | can impact 13 distinct SCOM decision-making areas
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers developed a capability-based framework to analyze where artificial intelligence and generative AI fit into supply chain and operations management. Using capabilities like learning, perception, prediction, interaction, adaptation and reasoning, they mapped applications across 13 decision areas including demand forecasting, inventory management, supply chain design and risk management.

The framework matters because it translates general AI capabilities into specific operational use cases that managers can evaluate for efficiency, accuracy and resilience improvements. As of the January 2024 publication, the contribution remains a conceptual guide for implementation and skills development, with actual performance gains and adoption barriers still to be validated in practice.
limitation: 
tag: Evidence-backed gain
key_points: Analyzes AI and Generative AI through resource-based view using capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning. | Maps capabilities to 13 SCOM decision areas including demand forecasting, inventory management, supply chain design, and risk management. | Proposes practical framework for practitioners to evaluate processes and prioritize investments and skills development.
rundown: The study uses the resource-based view to organize AI capabilities and examines how they apply to SCOM. It lists 13 decision-making areas and positions the framework as guidance for managers to assess operational processes.

By publication date January 31 2024, the work presents a conceptual framework and potential applications rather than measured deployment results, focusing on where AI and GAI can be applied for process optimisation and investment prioritisation.
sources:
- peer_reviewed | International Journal of Production Research | https://doi.org/10.1080/00207543.2024.2309309 | 2024-01-31
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