Today’s engineering challenges demand more than localized solutions – they require scalable, flexible tools that integrate data science techniques into the fabric of data and analytic workflows. The JMP family of products enables engineers to move beyond routine problem solving by providing powerful platforms for statistical analysis, process optimization, and data visualization. This plenary session explores how JMP supports engineering data science, with a focus on tackling complex, large-scale problems.

We highlight tools for data access, process screening, environmental monitoring, by-group analysis, model and response screening, and the integration of JMP Live to demonstrate how JMP adapts to diverse needs. Case studies and live demonstrations showcase how these capabilities fit into real engineering environments, enhancing decision making and accelerating innovation.

This session is also a chance to discuss the evolving relationship between engineers and data science. By rethinking workflows and harnessing new statistical techniques, we can address foundational challenges in manufacturing, product design, and quality engineering. Together, we examine the role of software in fostering collaboration, managing complexity, and building a resilient analytics ecosystem for the next generation of engineering data scientists.