International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 6
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Mitigating Cognitive Load in Supply Chain Decision-Making: An AI-Driven Framework for Enhanced Operational Efficiency

Author(s) Jochebed Akoto Opoku, Omotola Ogunsola, Ishmael Adikorley
Country Ghana
Abstract Humans face a lot of cognitive hinderances that slow down the operations and effectiveness in decision making in the scope of supply chain. Supply chain management systems have now become more intricate and data intensive. Existing systems usually overlook users' cognitive limitations, that results in information overload and decision fatigue despite advancements in digitalization and analytics. For cognitive load in supply chain to reduce significantly in decision-making, this study recommends an AI-driven framework that enhances human cognition instead of replacing it. The emphasis is on increasing operational efficiency, fostering human and AI collaboration, and developing supply chain sustainability. As companies grow, there is an increasing need to incorporate AI technologies that improve decision-making and operational resilience while assuring sustainable practices. The framework consists of layered components that filter information, provide contextual support, provide actionable recommendations, and incorporate human feedback. The design is based on Cognitive Load Theory and human-centered AI design. The study uses theoretical example applications in disruption management and demand forecasting to show how the framework might improve operational efficiency, minimize cognitive strain, and improve decision quality. The findings contribute to the AI-human collaboration and suggest new approaches to building intelligent decision-support systems that are both technologically advanced and cognitively aware.
Keywords cognitive load, supply chain management, artificial intelligence, decision support systems, cognitive augmentation
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-03
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.61809
Short DOI https://doi.org/hbdr67
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