Abstracts

The Power of Simulation In Design of Experiments (2026-US-30MP-2885) When designing an experiment, you might not be ready to run the experiment and collect the real data for the results. The Simulate Responses feature in JMP allows you to create data to mimic a real-world response, as a stand-in for the data until the experiment has been completed.This presentation discusses the benefits of simulation when creating a designed experiment; it also includes a...

Skill level: beginner

Jacob Rhyne and others

Practical Bayesian Optimization in the Fab (2026-US-30MP-2920) Modern semiconductor experiments are constrained by more than statistics. Lot sizes, review cycles, and operational risk all limit how much experimentation can realistically happen in production. At the same time, practitioners are often asked to choose between two powerful – but imperfect – approaches: design of experiments (DOE), which is efficient but model-dependent, and Bayesian optimization, ...

Skill level: intermediate

Mike Anderson JMP

Testing Complex Systems with Hard‑to‑Change Factors Using Covering Arrays (2026-US-PO-2953) Testing is a critical component of complex systems engineering, yet test engineers are rarely able to evaluate every possible combination of system inputs. Growing system complexity, limited budgets, and tight schedules demand more strategic approaches to test design. The challenge becomes even greater when certain inputs or factors are expensive or time‑consuming to change, forcing engineers to ca...

Skill level: intermediate

Bill Fisher JMP

JMP Bayesian Optimization Case Study with Complex Formulation Constraints (2026-US-PO-2981) Bayesian optimization is a powerful statistical approach that accelerates discovery in molecules, formulations, products, packaging, and processes by efficiently guiding sequential experiments, making it ideal for costly or time-consuming studies. This presentation demonstrates the applications of JMP Bayseian Optimization to optimize product and formulation design. We showcase a complex case study...

Skill level: advanced

Fangyi Luo and others

Experimental Design Training Meets Bayesian Optimization: A PPG Case Study (2026-US-30MP-2944) Balancing multiple performance properties while minimizing experimental effort is a common challenge in industrial R&D. Bayesian optimization provides a powerful framework for adaptive, sequential experimentation in complex, nonlinear response spaces. This presentation evaluates the Bayesian Optimization platform in JMP Pro using a realistic formulation simulation developed for PPG’s Sigma Logi...

Skill level: beginner

David Fenn and others

Defining Optimal Operating Regions in Process Validation (2026-US-30MP-2965) Process validation, such as Quality by Design (QbD) approaches, relies on designed experiments to understand process behavior and define optimal operating regions. These efforts commonly progress from scouting and screening studies to response surface methods, with regulated industries requiring clear definition of Proven Acceptable Ranges (PARs) for Critical Process Parameters (CPPs). At Gore, we ...

Skill level: intermediate

John Szarka W. L. Gore & Associates

Functional Data Meets Mixture DOE in a Food Production Case Study (2026-US-30MP-2974) Food texture plays a critical role in consumer acceptance, requiring careful control of viscosity to achieve the desired sensory experience. At the same time, food and beverage manufacturers must ensure products can be processed efficiently to minimize waste while maintaining quality and affordability. Achieving this balance becomes especially challenging when formulation changes are required.This ...

Skill level: intermediate

Curtis Park HP Hood

Statistically Steeped: A DOE Methodology for Cold Brew Extraction (2026-US-30MP-2911) Have you ever wondered why your home-brewed coffee never tastes as good as the coffee at your favorite coffee shop? Small changes in water temperature, grind size, water-to-coffee ratio, and brew time can have big effects on brew quality, which is why a systematic, scientific process is necessary for achieving the "perfect cup." In this presentation, we apply the design of experiments (DOE) methodo...

Skill level: intermediate

Rebecca Breuer Francis Medical

From OFAT to Mixture DOE: Intuitive Space Filling Designs in JMP (2026-US-PO-2938) One-factor-at-a-time (OFAT) experimentation remains common in the pharmaceutical industry, particularly during early development, despite well‑known efficiency limitations compared with design of experiments (DOE). This poster presents a gene therapy case study illustrating how JMP supported a transition from OFAT to a mixture DOE strategy through visualization‑driven design choices and intuitive e...

Skill level: intermediate

Yang Cao and others

Optimizing Biomanufacturing 1,2,4-butanetriol with JMP Hybrid DOE and SVEM (2026-US-PO-2976) Biomanufacturing 1,2,4-butanetriol (BT) has been of interest due to its utility as a precursor. Efforts to engineer cells to produce BT have had mixed success. In this project, we explored the production of BT with cell-free metabolic engineering, where cell lysates containing each of the enzymes needed to produce BT are mixed along with the precursor xylose as well as multiple cofactors. The desig...

Skill level: advanced

John Davies US Army DEVCOM Chemical Biological Center

Autotuning a PID Controller using Bayesian Optimization and JSL (2026-US-30MP-2947) Proportional-integral-derivative (PID) controllers are ubiquitous in industrial settings, used for such applications as temperature regulation, flow and level control, and motor control. Achieving optimal performance often requires careful manual tuning of the three PID parameters. In this presentation, we demonstrate automated PID tuning using Bayesian optimization, applied to controlling the angu...

Skill level: intermediate

Andrew Michelson and others

Finding the Sweet Spot: Cross-Product Analytical Optimization in Biopharmaceutical Development (2026-EU-30MP-2788) In biopharmaceutical development, a recurring challenge is determining how analytical insights can be leveraged to establish and optimize methods. This case study focuses on a specific chromatographic technique, with the underlying approach having been successfully applied in other areas such as formulation and bioprocess development.To address this task, data from individual design of experiments ...

Jan Spindler

Combating Antimicrobial Resistance Using Functional DOE and the Future of Assay Optimisation (2026-EU-30MP-2843) Antibiotic resistance is one of the greatest health threats facing the world. At the University of Oxford, we are pioneering a novel approach by targeting the RecBCD enzyme – a key regulator of DNA repair in bacteria. Central to this was the development of a robust, high-throughput biochemical assay, which meant navigating a complex 11-factor space.Initially limited by manual pipetting an...

Phil Kay and others

Designing Efficient DOE Strategies for Complex Cell Media Development (2026-EU-30MP-2813) Cell culture media development often involves a daunting challenge: screening across dozens of factors –sometimes 50 or more – while operating under strict experimental resource constraints. Traditional design of experiments (DOE) approaches struggle to scale in such high-dimensional spaces, and fully automated methods can lead to impractical or non-executable designs.In this session, we explo...

Drejc Kopac

Precision by Design: Applying DOE and MSA to Engineer Robust Measurement Systems (2026-EU-30MP-2631) This talk focuses on enhancing precision in metrology experiments through structured methodologies and robust validation of measurement systems. It introduces KLA’s role in the electronics ecosystem, showcasing its core competencies in illumination sources, optics, sensors, mechanics, physical sciences, and image and data processing. A central theme is the strategic use of design of experiments (DO...

John Linden

Applying DOE to Large Language Models (2026-EU-30MP-2761) ChatGPT, and large language models (LLM) in general, have become a growing source of information. The caveat is that their responses need to be assessed before being used, so that the emphasis moves from efforts to generate information to efforts to assess the quality of information. In this talk we move the attention, yet again, to the design of prompts used to learn from LLM platforms. We apply J...

Ron Kenett and others

Head to Head: Optimal DOE vs. Bayesian Learning for LCMS Assay Development (2026-EU-30MP-2771) Developing multiple drugs of abuse testing (DAT) assays in parallel for the cobas® Mass Spectrometry system demands an efficient optimization workflow. This talk details our approach to optimizing the HPLC gradient, a typical challenge with a vast factor space in LCMS method development. We began with a constrained, I-optimal designed experiment of over 80 runs to generate the rich data set needed ...

Sebastian Hoffmeister

Understanding and Optimizing a Photopolymerization Process in Dispersed Media (2026-EU-PO-2810) Calyxia is an industrial scale-up specializing in the design and manufacture of microcapsules that combine biodegradability and high performance. It offers its solutions in three markets: home and beauty, agriculture, and advanced materials. During the microcapsule manufacturing process, controlling the formation of the microcapsule wall is essential to produce a high-quality final p...

Skill level: beginner

Pablo Chourreu Alba De Luna

Modeling Synthetic Chromatograms in JMP 19: Updates and New Applications (2026-EU-PO-2745) Chromatographic techniques such as HPLC, GC, and CGE are essential for analytical workflows across industries. However, optimizing these methods remains challenging due to numerous parameters and the complexity of chromatograms. Traditionally, performance metrics such as resolution or peak-to-valley ratios are extracted and modeled, but linking these metrics back to the full chromatogram is often d...

Skill level: advanced

Marco Kunzelmann

Multiple Trend Optimisation with JMP Functional Data Explorer (2026-EU-30MP-2860) A good small-scale model (SSM) is a process performed at laboratory scale, representing a manufacturing-scale process. Establishing an SSM that is truly representative of manufacturing scale is integral to allowing small-scale process characterisation studies. Previously, the analysis of time-course data was performed with an approach that assessed ±3 SD of the mean manufacturing data, though somet...

Gwenola Ninon

Accelerating Power MOSFET Development Using TCAD Simulations and JMP Bayesian Optimization (2026-EU-PO-2837) In the fast-paced development of new power device technologies, defining and validating robust process windows for key manufacturing modules is critical – but time-consuming. Traditional experimental learning cycles can extend project timelines significantly. This presentation showcases an innovative approach that combines TCAD simulation data with JMP’s cutting-edge Bayesian Optimization plat...

Matteo Patelmo and others

From DOE to Predictive Modeling: Utilizing JMP to Customize Powder Coating Properties (2026-EU-PO-2759) The gel time and cure time are important properties of powder coatings. The rate of the curing reaction inherently affects both of these characteristics. Consequently, it is often challenging to modify one of these properties without compromising the other. The curing rate can be controlled by varying the amount of catalyst used. We found that using two specific catalysts, A and B, produces interes...

Piotr Dudzinski

A Practical Approach to Teaching DOE for Biomanufacturing Using JMP (2026-EU-PO-2865) Design of experiments (DOE) is a cornerstone of process development in the biopharmaceutical industry and a key component of Quality by Design (QbD) principles. However, many professionals entering the biomanufacturing field come from diverse academic backgrounds with limited exposure to structured experimental design. To address this gap, a three-day intensive course was developed at the Bio...

Skill level: beginner

Marcello Fidaleo

Deriving Design Space for Upstream Process Optimization of Biosimilars: A JMP DOE Case Study (2026-EU-PO-2824) This case study applies a Quality by Design (QbD) approach to optimize the upstream process of a biosimilar using design of experiments (DOE) in JMP software. The goal was to derive a robust design space that enhances process understanding and control. Since its goal was to improve efficiency, a streamlined design with fewer than 30 runs was needed to identify optimal parameters while minimizing co...

Vaishali Karle

Bridging Theory to Clicks: Two-Level Designs with Treatment Cardinality Constraints in JMP (2026-EU-30MP-2792) Modern experimental designs often face the so-called treatment cardinality constraint, which is the constraint on the number of included factors in each treatment. Experiments with such constraints are commonly encountered in engineering simulation, AI system tuning, and large-scale system verification. It calls for the development of adequate designs to enable statistical efficiency for modeling ...

Ryan Lekivetz and others

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