Mr. Shiva Kumar Ramavath

AI-Powered Retail Intelligence: Redefining Supply Chain Optimization, Anomaly Detection, and Model Deployment in the Data-Driven Era

Abstract:

This presentation explores the transformative potential of artificial intelligence (AI) in reshaping retail supply chains, anomaly detection, and model deployment. By leveraging vast datasets, AI enhances forecasting accuracy, inventory management, logistics routing, and warehouse automation. The integration of AI drives operational efficiency, cost optimization, and customer satisfaction by enabling predictive analytics and intelligent decision-making. Emphasis is placed on AI-powered anomaly detection to preempt fraud, supplier disruptions, and systemic inefficiencies, ensuring business continuity and risk mitigation. The presentation also addresses the challenges and strategies associated with deploying machine learning models at scale, including the use of MLOps, hybrid cloud-edge deployment, and real-time feedback loops for continuous improvement. With case studies demonstrating real-world impact, such as retail loss prevention and automated dynamic pricing, the session highlights the strategic importance of ethical AI, governance, and digital twins. Ultimately, it presents a forward-looking vision of AI as a critical driver of innovation and competitiveness in the retail industry.

Profile:

Shiva Kumar Ramavath is an AI Machine Learning Engineer working at Albertsons with over a decade of experience in Data Science and Machine Learning specializes in Retail Operations and Supply Chain Domain. His expertise includes building advanced machine learning models, such as a Phantom Inventory Reduction Model that contributed to $120 million in additional sales, Sales Forecasting Model to improve demand planning and inventory management, and Anomaly Detection Model for identifying irregularities in PI. Through his work, he continues to drive innovation in data-driven decision-making and business transformation. Academically, Shiva is pursuing a Ph.D. in Artificial Intelligence at the University of the Cumberlands, where his research focuses on the development of domain-specific Large Language Models (LLMs) and Small Language Models (SLMs) for specialized applications. He holds an MS in Data Science from the University of North Texas and a bachelor’s in computer science from JNTU University.