Naga Bharadwaj Bhavikatta

Intelligent Forecasting: How AI Transforms Supply Chains

Abstract:

Organizations today face significant pressure to accurately predict demand, quickly address disruptions, and make informed, data-driven decisions in an increasingly complex supply chain landscape. Traditional forecasting approaches often struggle with the volatility and complexity of modern supply chain data, creating a strong need for advanced AI-powered solutions that can identify deeper patterns, adapt to uncertainty, and provide reliable predictions at scale.

This talk presents a multi-layered AI architecture designed to tackle these challenges, combining standard LSTM networks, attention-enhanced LSTMs, and a novel Quantum-based Attention LSTM model. By testing these models on diverse real-world supply chain datasets, the presentation demonstrates how quantum-inspired techniques coupled with attention mechanisms can significantly improve forecasting accuracy and interpretability.

Profile:

As a Senior Tech Manager at Oracle USA, I lead AI-powered Oracle product implementations, helping customers unlock tangible benefits through advanced supply chain solutions. With an APICS CSCP certification, I have mentored over 10+ supply chain professionals, guiding them through current industry trends and best practices. My academic foundation includes an M.Tech in Software Engineering from Jawaharlal Nehru Technological University, Anantapur, which strengthens my ability to bridge research and practical applications. As an ASCM CSCP Fellow member, I actively contribute to industry discussions, staying at the forefront of AI-driven innovations in supply chain management. I am a dedicated researcher focused on optimizing supply chain operations using the latest AI and machine learning technologies. My work explores advanced predictive analytics, deep learning models, and AI-driven simulations to enhance efficiency, minimize disruptions, and drive intelligent decision-making in supply chain management.