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Malarvizhi Sankaranarayanasamy
Malarvizhi Sankaranarayanasamy

Malarvizhi Sankaranarayanasamy

Hitachi America R&D
Bio

Dr. Malarvizhi(Malar) Sankaranarayanasamy is a Senior Researcher at Hitachi America, bringing over a decade of experience in industrial systems engineering. Her expertise focuses on developing solutions for various sectors including manufacturing, supply chain, mobility, oil and gas, and data centers. Her work primarily involves prototype development and AI/ML applications, where she leads digital transformation projects that have impacted over 2,000 remote assets. She specializes in creating scalable frameworks for real-time predictive control, risk assessment in distributed networks, and digital twin technologies using reinforcement and imitation learning. Through collaboration with cross-functional and multi-organizational teams, she has successfully demonstrated new product introduction (NPI) feasibility and scaling through strategic partnerships and analytics-driven solutions that enhance operational efficiency.
Her expertise spans process optimization, machine learning strategies, and policy development for industrial automation. As an active member of the Society of Manufacturing Engineers (SME) and The Association for the Advancement of Artificial Intelligence (AAAI), she has contributed patents and publications in asset management, data center solutions, and mobility systems while mentoring SMEs to adopt next-generation technologies.


Capacity Reimagined: A Hybrid Analytics Portfolio for Datacenter Supply Chain & Operational Growth

This talk addresses the challenges of scaling data center operations amidst volatile supply chains by introducing a portfolio-based analytics framework. Combining traditional methods like risk analytics and hazard function models with advanced AI techniques, such as large language models and probabilistic graphical models, the framework enhances supply chain resilience. It evaluates survival probabilities for critical equipment with lead times exceeding 120 weeks, pinpointing risks from material shortages and logistics constraints. Insights enable targeted risk mitigation, dynamic resource allocation, and robust capacity management. A colocation case study highlights reduced bottlenecks and improved supply-demand alignment. Attendees will learn to bridge traditional forecasting with AI-driven strategies, ensuring scalable, resilient operations. Beyond colocation centers, these techniques empower organizations to detect disruptions, optimize supplier collaboration, and accelerate capacity expansion while minimizing delays and service-level violations. This session provides a practical roadmap for adopting an integrated, forward-looking supply chain strategy.

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