SAS Institute Japan List of News Articles

This session is aimed at engineers in the manufacturing industry, focusing on data utilization, and will explain practical approaches to factor exploration and predictive model building using JMP products. Analysis does not yield optimal results in one go; it is important to enhance accuracy through repeated trial and error in data preparation and model building. In particular, the quality of data preparation, such as preprocessing and feature creation, significantly influences the results, and JMP allows for efficient progress while visualizing the process. Using publicly available data for model building, we will introduce a series of processes from preparation, including visualization, preprocessing, and feature creation, to factor exploration through correlation and regression analysis, predictive model building (such as neural networks and random forests), and extraction of important factors, all through trial and error. Program: 1) What to do before the model: Key points of data preparation and trial & error using JMP 2) How to proceed with factor exploration and predictive model building without making it a black box ▼Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/12-15-factor-exploration

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 factor 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 during problem occurrences, with demonstrations included. 【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 factor analysis when problems occur 【Overview】 1) Taking Quality Management to the Next Step - Utilizing JMP Beyond Control Charts Using quality management tasks as a subject, 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 Factor 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

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 on the manufacturing floor, from quality monitoring to cause exploration during problem occurrences, with demonstrations included. 【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 during problem occurrences 【Overview】 1) Taking Quality Management to the Next Step - Utilizing JMP Beyond Control Charts Using quality management tasks as a subject, 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/2026/12-09