Citation:

Naveen Saikrishna Puppala, 2026. AI-Driven Demand Forecasting Platforms for Global Supply Chains,  Volume 6 Issue 3: 138-145.

Abstract:

As the world's supply chains have become complex and volatile, there has been a need for powerful and responsive forecasting. In many modern markets, product demand is influenced by nonlinear interactions and exogenous factors, for which traditional demand-forecasting techniques may perform poorly. Recent advances in artificial intelligence (AI) have enabled data-driven platforms to learn from large volumes of heterogeneous data. This paper summarizes the relevant literature, empirical studies and methodological innovations of AI-based demand-forecasting platforms. It compares the accuracy and calibration of statistical, machine-learning, and deep-learning models and the results reported show that global and probabilistic models have the highest accuracy and calibration. Hierarchical reconciliation also helps to maintain coherence of the multilevel forecast. Emerging challenges and research gaps with regard to interpretability, privacy and sustainability are identified. The paper concludes with the directions for the next generation forecasting systems, such as causal reasoning, federated learning, and energy-efficient AI.

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Keywords:

Artificial Intelligence, Demand Forecasting, Global Supply Chain, Machine Learning, Deep Learning, Temporal Fusion Transformer, Forecast Reconciliation, Probabilistic Forecasting, Federated Learning, Sustainable AI