The ability to accurately perform capacity management and planning on today’s interconnected mainframe is a growing challenge for many. Less predictable workloads aligned with business-focused SLAs increase complexity and risk in ensuring performance and future growth is understood and costed. This session explores some of the key challenges faced by performance and capacity planners using IBM Z Performance and Capacity Analytics. By using SMF and other data sources, we look at addressing much needed tasks around identifying current and future application and subsystem problems, MSU consumption management, forecasting of resource utilization, and predictive what-if simulation.