SAS Institute Japan Company Profile

Although quality data and process data are accumulated, there are challenges such as "missing signs of abnormalities" and "being unable to identify the cause when problems occur." In the seminar, we will cover practical methods for quality management and cause analysis, introducing a data-driven approach to problem-solving. We will explain analytical approaches that can be utilized in manufacturing sites, from quality monitoring to cause exploration when problems arise, including demonstrations. 【Target Audience】 Quality management personnel in manufacturing Production technology and process improvement personnel Those who want to utilize process data and quality data Those who feel challenges in cause analysis when problems occur 【Overview】 1) Taking Quality Management to the Next Step - Utilizing JMP Beyond Control Charts Using quality management tasks as a theme, we will cover the flow from control charts and process capability analysis using JMP to cause exploration, as well as methods for sharing analysis results. 2) Data-Driven Cause Analysis and Quality Improvement An analytical approach to efficiently narrow down the causes of problems using process data and quality data. ▼ Details & Registration https://www.jmp.com/ja/events/seminars/discovery-seminar-series/2026/10-30

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 minimizing time and costs. In this seminar, we will focus on the features of experimental design, specifically the sample size explorer and Bayesian optimization, and demonstrate examples of their application in the manufacturing industry. 【Overview】 First half: Utilization of t-tests in the manufacturing industry and simulation of required data numbers We will explain the overview and examples of t-tests, which are used for various purposes in the manufacturing industry, and present analysis examples using JMP. Additionally, we will introduce a method to simulate the relationship between the required data numbers for t-tests and statistical power using the sample size explorer. Second half: How to utilize JMP Pro Bayesian optimization – Application to simulation experimental data The Bayesian optimization platform is originally designed for experimental data that includes measurement errors, but there is also a strong demand to apply it to simulation experimental data that does not contain errors, such as CAE. In this seminar, we will introduce approaches for utilizing Bayesian optimization with simulation experimental data.

Starting from November 2025, "JMP 19" has become available. Those who hold a license can upgrade to the latest version. For information on new features, please visit the following site: https://www.jmp.com/ja/software/new-release/new-in-jmp