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Artificial intelligence for demand forecasting in manufacturing supply chains: A systematic literature review, research gap analysis, and future directions

Attia Hussien Gomaa1*
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1 Mechanical Engineering Department, Faculty of Engineering, Benha University, Shubra, Cairo , Egypt
SCMR, 026270018 https://doi.org/10.36922/SCMR026270018
Received: 3 July 2026 | Revised: 6 August 2026 | Accepted: 13 August 2026 | Published online: 24 August 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Artificial intelligence (AI) has become a transformative technology for demand forecasting in manufacturing supply chains, improving forecasting accuracy, production planning, inventory optimization, and demand–supply synchronization in increasingly dynamic and data-intensive environments. Despite the rapid advancement of AI-driven forecasting, existing review studies remain fragmented, primarily focusing on forecasting algorithms and predictive performance while providing limited synthesis of methodological developments, implementation challenges, and emerging research gaps. Consequently, a comprehensive understanding of the evolution, adoption, and future trajectory of AI-enabled demand forecasting remains lacking. This work addresses these limitations through a systematic literature review, research gap analysis, and future research agenda on AI applications for demand forecasting in manufacturing supply chains. Following the PRISMA 2020 guidelines, this review systematically analyzes studies published between 2010 and 30 May 2026 to examine the evolution of AI methodologies, identify research trends and implementation barriers, evaluate unresolved theoretical and practical gaps, and propose strategic directions for future research. The findings reveal a clear transition from conventional machine learning to deep learning, reinforcement learning, hybrid intelligence, transformer-based architectures, and foundation AI, reflecting a broader shift from predictive analytics toward intelligent and autonomous decision-making. Although these advances have significantly enhanced forecasting performance and supply chain responsiveness, their industrial adoption remains constrained by challenges related to data quality, interoperability, model explainability, organizational readiness, enterprise system integration, scalability, AI governance, cybersecurity, and cross-organizational collaboration. The review further identifies critical research gaps, including the limited adoption of explainable AI, inadequate integration of foundation models and digital twins, fragmented evaluation frameworks, and insufficient real-world validation. It proposes a future research agenda centered on explainable and trustworthy AI, AI-driven decision intelligence, autonomous forecasting, responsible AI governance, sustainable AI, and autonomous manufacturing systems. This review contributes an integrated synthesis and research roadmap that advances both academic understanding and the practical implementation of intelligent, resilient, and sustainable manufacturing supply chains.

Keywords
Artificial intelligence
Demand forecasting
Manufacturing supply chains
Machine learning
Deep learning
Reinforcement learning
Industry 4.0
Funding
None.
Conflict of interest
The author declares no conflicts of interest.
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