In this webinar, we explore how simulation-driven RAM analysis enables engineers to better understand system behavior, quantify availability risks, and make informed design and operational decisions earlier in the lifecycle.
Overview
The rapid growth of AI workloads and large-scale data storage is placing unprecedented demands on data center infrastructure. Ensuring high levels of reliability, availability, and maintainability (RAM) is therefore more critical than ever, but validating availability in complex, interconnected systems is far from straightforward. Modern data centers consist of multiple redundant subsystems, dependencies between power and cooling architectures, maintenance strategies, and operational constraints that make traditional spreadsheet-based approaches insufficient for accurate availability prediction.
In this webinar, we explore how simulation-driven RAM analysis enables engineers to better understand system behaviour, quantify availability risks, and make informed design and operational decisions earlier in the lifecycle.
Key Takeaways
Using ReliaSoft's BlockSim and Asset Performance Framework, you will learn how to:
- Model complex data center architectures using reliability block diagrams
- Evaluate the impact of redundancy strategies on system availability
- Identify critical components driving downtime risk
- Forecast availability under different maintenance scenarios
- Support evidence-based decisions to improve uptime and resilience
- Deploy this RAM model to continuously provide insights with newly collected field data
Speakers
Chris is an experienced application engineer with a demonstrated history in reliability engineering and Computational Fluid Dynamics (CFD) CAE field. Chris has a Bachelor of Science degree in mechanical engineering with with 10+ years of professional experience including 3 years of experience in reliability engineering. My current role as an application engineer at HBK is supporting ReliaSoft desktop products, including Weibull++, BlockSim and XFMEA.
Adi helps business leaders prevent costly operational disruptions by delivering advisory services and software solutions that enable continuous improvement in asset reliability, uptime, and lifecycle cost. Over his 13-year career, he has partnered with more than 60 asset-intensive organisations, including Fortune 100s, to transform how asset performance is managed. Adi specialises in advanced analytics, operationalising digital twins, and large-scale asset data management to deliver the impact required for Industry 4.0. His recent focus includes AI infrastructure (data centres and semiconductor manufacturing), autonomous transport reliability, and improving the profitability of aftermarket services for equipment manufacturers. He holds an engineering degree from the University of Waterloo. Outside of work, Adi enjoys rolling up his sleeves renovating his century-old (1904) home in Toronto.