SAS Institute Japan Company Overview

We will introduce how to utilize JMP to solve challenges faced when conducting data analysis on the manufacturing floor, structured in two parts. - There is data, but I don't know how to analyze it. - I am struggling with how to leverage the analysis results in business. Part One: Understanding and Utilizing Defect Counts and Occurrence Data We will touch on the basic concepts when dealing with count data such as defect counts and trouble occurrences, and introduce the following points: - Understanding the occurrence situation - How to capture variability and changes using control charts - Monitoring methods when there are many count data Part Two: Utilizing JMP Dashboard Builder for Engineers Using process data from the workflow, we will explain how to consolidate multiple reports into one window while being mindful of actual business usage scenarios. You can create dashboards that require no coding and always access the latest data, which can be used for information sharing with related departments and reporting to superiors. ▼ Details & Registration https://www.jmp.com/ja/events/seminars/discovery-seminar-series/2026/06-03

This seminar is aimed at engineers primarily in the manufacturing industry, focusing on data utilization, and will explain practical approaches to factor exploration and predictive model building. 1. What to do before the model: Key points for data preparation and trial & error using JMP We will introduce the necessary preprocessing and feature creation required before analysis. - Understanding the contents of the data (visualization and summary statistics) - Dealing with missing values and outliers - Creating variables to enhance model accuracy (feature engineering) - Tips for efficiently progressing the creation of analysis data 2. How to proceed with factor exploration and predictive model building without making it a black box Based on the data prepared in the first half, we will explain the process of factor exploration and predictive model building. - Visualization of factors using correlation analysis, regression models, and decision trees - Model evaluation using training and validation data - Comparison and selection of multiple machine learning models (e.g., neural networks, random forests, etc.) - Intuitive understanding of important factors and prediction results ▼ Details & Registration https://www.jmp.com/ja/events/seminars/non-series/2026/06-19-factor

We will introduce the features and applications of "Bayesian Optimization." In product design and development, it is essential to efficiently find optimal conditions within a limited number of trials. Design of Experiments (DOE) is one effective method, but it requires conducting multiple experiments in advance, making it challenging to explore optimal conditions while minimizing the number of experiments, especially in cases with complex nonlinear relationships. In this seminar, we will explain the basic concepts and advantages of "Bayesian Optimization," which allows us to approach optimal conditions with fewer experiments. Furthermore, we will introduce a method that uses "Bayesian Optimization" to sequentially propose the next experimental conditions to test, considering multiple characteristics and constraints while utilizing profiles, thus advancing optimization efficiently with concrete examples. Main content: - What can be done with Design of Experiments (DOE) and its limitations - Overview of Bayesian Optimization and its benefits - Features of "Bayesian Optimization" in JMP Pro ▼ Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/05-28-pro-bayes