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@article{181942,
        author = {Krishna Kumar Tiwari and Ashitosh. S. Wagh and Sumedha Chakma and Rajlaxmi Roy Urbi and Protiksha Sarker and Sahir Ahamad},
        title = {AI-Driven Demand Forecasting in Hyper- Dynamic Supply Chain Markets},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {249-264},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181942},
        abstract = {This conceptual paper intends to examine the revolutionary role of artificial intelligence (AI) in demand forecasting in hyper-dynamic supply chain markets through a synthesis of recent academic literature. In environments characterised by unpredictable disruptions, volatile demand, and rapid change, traditional demand forecasting techniques often fall short. AI techniques not only provide the highest accuracy through state-of-the-art machine learning and deep learning algorithms but also allow for real-time flexibility and can fuse data from various sources for improved forecasting ability and enhanced operational efficiency. This paper will discuss the characteristics of hyper-dynamic supply chains, study the evolution and possible applications of AI in demand forecasting, and address the far-reaching consequences ofAI pertaining to supply chain resilience. It will also propose a novel conceptual framework based on dynamic capabilities and organizational information processing theories to depict how AI engenders agility and adaptability. This study delineates key advantages arising from applications of AI, including enhanced inventory management, optimization of logistics, and mitigation of risks.},
        keywords = {Artificial Intelligence, Demand Forecasting, Supply Chain Management, Hyper-Dynamic Markets, Supply Chain Resilience, Machine Learning, Deep Learning.},
        month = {July},
        }