Utilization of JMP's experimental design features in the manufacturing industry.
For researchers and engineers in the manufacturing industry: How many data points are needed for a t-test? Learning experimental design with the statistical analysis software JMP and techniques for utilizing Bayesian optimization.
JMP is software that not only excels in statistical analysis and graph creation but also has robust features for experimental design. By utilizing experimental design, efficient data collection can be achieved while keeping time and costs down. In this seminar, we will focus on the features of experimental design, specifically the sample size explorer and Bayesian optimization, and present examples of their application in the manufacturing industry in a demonstration format. ▼Details & Registration https://www.jmp.com/ja/events/live-webinars/discovery-seminar-series/2026/10-09
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**Date and Time** October 9, 2026 (Friday) 14:00 - 16:00 **Target Participants** - Those interested in how to utilize JMP in the manufacturing industry - Those interested in the experimental design features of JMP **Overview** First Half: Utilization of t-tests in the manufacturing industry and simulation of required sample sizes T-tests are used for various purposes in the manufacturing industry. In this session, we will explain the overview of t-tests and provide examples of their application in manufacturing, along with analysis examples using JMP. Additionally, we will introduce a method to simulate the relationship between the required sample size for t-tests and statistical power using the Sample Size Explorer. Second Half: Utilizing JMP Pro Bayesian Optimization - Application to Simulation Experimental Data The Bayesian Optimization platform is originally designed for experimental data that includes measurement errors. However, there is also a significant demand to apply it to simulation experimental data that does not contain errors, such as CAE. In this seminar, we will introduce approaches to utilize JMP Pro's Bayesian Optimization for simulation experimental data.
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Free (Pre-registration is required from the page below)
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"Democratization of Data" and Sharing the "Wisdom" of Manufacturing Across the Company - TOTO's New Exploration of "Good Products and Homogeneity" [Challenge] The manufacturing of sanitary ceramics using natural raw materials involves approximately 13% shrinkage during the drying and firing processes. As products become larger and more complex, the challenge is how to maintain "homogeneous" high quality and to convert the "tacit knowledge" of skilled artisans into "explicit knowledge" to pass on to the next generation. For TOTO Ltd., which has a history of over 100 years, this has been a crucial issue at the core of its manufacturing. [Solution] An "exploratory data analysis" using JMP was introduced for the manufacturing data from the advanced Shiga factory. By deepening the high yield achieved through the introduction of the first barcode system in the sanitary ceramics factory, the "good product conditions" were quantified using visual verification with graph builders and techniques such as partitioning and cluster analysis, leading to improvements in direct yield rates and overall yield. Additionally, JMP was adopted for the foundational education of the "in-house study abroad program" over two years, aiming to enhance company-wide data science skills. [Result] Please check the following page for more details! https://www.jmp.com/ja/customer-stories/toto
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The JMP story goes back to 1989 when John Sall decided to combine statistical analysis capabilities with graphical visualizations to animate and visualize data. For more than 35 years now, John Sall has led JMP R&D, making each version of JMP more visual, more interactive, and more practical to help users understand their data. What started as a passion project has grown by leaps and bounds. It’s now a family of statistical software products designed with scientists and engineers in mind and used worldwide in nearly every industry.
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