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The analytical method "Causal Exploration" visualizes the relationship between causes and effects in data. In the manufacturing industry, it is used for analyzing the causes of equipment failures, while in the marketing sector, it helps uncover causal relationships from survey results, creating new insights in various scenarios. If you want to learn the basics or wish to implement it in your work or research, please join us! ■ Recommended for: - Production and quality management personnel who want to investigate the causes of equipment failures. - Those in the marketing industry who want to visualize the relationships between survey and purchasing data. - Data analysts and researchers who want to uncover causal relationships from big data. ■ What you will learn in this seminar: (1) Basic knowledge of Causal Exploration "Causal Exploration" visualizes the invisible relationships between causes and effects from big data. We will first explain the fundamentals of data analysis methods that do not rely on human experience or intuition. (2) Case studies utilizing Causal Exploration We will introduce actual case studies where Causal Exploration has been implemented in business. (3) Steps to implementation What kind of data and how much is needed to identify causes with Causal Exploration? We will explain the analysis methods, how to interpret the results, and the steps for implementation towards practical use.

If you are looking to improve maintenance efficiency and reduce costs through predictive maintenance using the latest AI technology, please join us! (Preparing to file a patent for our unique algorithm) ■ Recommended for: - Those who want to implement AI predictive maintenance tools to reduce maintenance costs - Those who are already practicing predictive maintenance but want to enhance efficiency using AI - Those looking for predictive maintenance technology that can visualize and notify the causes of failures from predictions ■ Program: - Market trends in predictive maintenance - Benefits and considerations of predictive maintenance - New predictive maintenance processes using AI - Cause analysis of failures using causal exploration - Future outlook for predictive maintenance ■ Participation Benefits: All participants who complete the post-viewing survey will receive an early gift of a free trial version of the AI predictive maintenance tool! We will provide an early gift of the free trial version (30 days) of "NTech Predict [Ver2.0.0]" scheduled for release in September! Please feel free to join us! *The licensing format may differ from the production environment.

If you want to successfully implement a PoC using data and connect it to the practical application of your project, please join us! ▼Recommended for: - Field personnel who have struggled with PoCs or are about to start one - Members of planning/development/technical/research departments involved in the development of new services or products - DX personnel and their managers who want to utilize data ▼Overview In today's rapidly evolving manufacturing industry, the importance of PoCs (Proof of Concept) to verify the feasibility and utility of projects is increasing. However, many of you may have experienced: - "The expected results were not achieved" - "The costs became too high" - "We tried it, but it did not lead to full-scale operation" This seminar will explain the basics of PoCs and key points to avoid failure. We will provide methods for successfully implementing PoCs, including what to set in advance and evaluation metrics based on actual failure cases. We will also introduce successful PoC examples using data analysis and the process using the AI prediction and causal exploration tool 'NTech Predict'. As the first step towards realizing your project through PoCs, we encourage you to participate!