In today’s fast-paced and highly competitive environment, successful organizations use structured experimentation to foster innovation and optimize processes. In this white paper, we explore how JMP’s advanced approach to design of experiments (DOE) and wide range of DOE capabilities can help you gain insights faster, optimize with confidence, and uncover relationships that fuel breakthrough results. We also provide real-world examples of successful DOE implementation and give you the tips, tricks, and best practices you need to succeed.

In this paper, learn more about:

Custom design
With JMP’s Custom Design platform, you can tailor an experiment to meet your specific challenges and resource constraints. Learn how to construct an optimal design that considers your ability to control factors, set constraints, incorporate covariate information, and navigate other experimental conditions and resource limitations. We also cover advanced design types, including supersaturated designs, split-plot structures, and mixture-process variable combinations, plus more about interactive tools that let you preview and evaluate your design in real time.

Augment designs
If your initial design encountered setbacks – perhaps due to overly ambitious factor settings or unexpected constraints that appeared during experimentation – the JMP Augment Design platform allows you to refine and extend your existing experiments by introducing additional runs that optimize the overall design.

Easy DOE
Whether you’re a DOE beginner or a seasoned professional, Easy DOE provides a streamlined and intuitive experience, guiding you through each step with clear, user-friendly instructions. With prebuilt templates for standard factorial and screening designs, intuitive interfaces to reduce complexity, and automated model fitting and reporting for seamless analysis, learn why JMP is the perfect tool for designed experiments.

Definitive screening design
When your primary objective is to screen a large number of factors, definitive screening design (DSD) in JMP offers an efficient and powerful solution. DSDs are designed to minimize the number of experimental runs needed to identify key factors, while also providing the ability to detect curvilinear effects – something that standard screening designs often fail to achieve.

Additional DOE tools in JMP

• Classical designs: JMP offers time-tested methodologies that are widely recognized across various disciplines.
• Design diagnostics: The Design Diagnostics platform offers powerful tools for evaluating and comparing experimental designs, ensuring that the chosen design will meet the study’s objectives and yield reliable results.
• Consumer studies: The JMP Consumer Studies platform offers powerful tools designed to capture and analyze consumer preferences, enabling companies to create products and services that align with consumer demand.
• Special purpose designs: Learn more about the Special Purpose platform in JMP, which includes a wide range of design types to address unique and complex experimental situations.
• Covering array designs: Testing complex engineered systems is usually infeasible, if not impossible. JMP’s covering arrays (CAs) are designs that can be used to effectively test these kinds of systems at minimal cost.
• Sample size explorers: A variety of sample size explorers in JMP help you tailor experiments for optimal precision and power.

Ready to take your experimentation to the next level?

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