Factory Physics provides mathematical model to maximize the throughput（TH） under admitting the existence of operation variability such as delay, failure and set-up, in contrast with Japanese approach tends to strive for Kaizen denying any variability even though sacrificing the TH. This article takas up the two topics form Factory Physics. One is a scientific evaluation method of process performance based on the Little’s law, TH＝WIP（work-in process）/CT（cycle time）, which also gives comprehensive orientation of Kaizen while maintaining the TH. The other is a quantitative evaluation how significantly and qualitatively each variability effects on the CT extension. In addition, mathematical formulation of the pull methods of “Kanban” and the CONWIP（ Constant Work In Process） introduced in Factory Physics are provided and compared. Consequently it is suggested the CONWIP is the best control method by its autonomous WIP adjustment function, and effectively applied even for supply chain situation under the IoT environment.

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　This article is the second report of 3 separate reports on the study group on big data analysis in the quality control field. This article addresses the history of machine learning research, the relationship between multivariate analysis and machine learning and the relationship between statistical quality control and machine learning.

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　A.I.（ Artificial Intelligence） is utilized by collecting data. I examined the situation where machine learning can be used effectively in manufacturing from the viewpoint of quality control. Although machine learning can be applied in many fields in the manufacturing industry, I pay attention to “visual inspection” where workers check parts and finished products visually. 　I’ll give my view regarding the applicable inspection parts, machine learning methods and the tasks to be considered in promoting machine learning.

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　Machine learning including deep learning is now getting core of future essential technology for healthcare providers. As for medical systems manufacturers application of machine learning is expanded to marketing, R&D, manufacturing and after-purchase servicing processes. To consider how to manage machine learning wisely by medical systems manufacturers, some of use cases are reviewed in this article. It could be a good learning opportunity for all manufacturing industries to know how healthcare industry players are willing to use machine learning as a very potential tool.

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　The new government course guidelines have been publicly notified for elementary and junior high schools in March 2017 and for high school in March 2018. Nowadays, the society is significantly changing and the changes are unpredictable. In order to respond to changes in the society, the ability to think how to make our society and life better as well as the ability to make it happen in reality, that is “vital energies”, should be developed. Especially, abilities and skills for collecting and analyzing data, solving problems, and making decisions should be cultivated. In the new government course guidelines, “Utilizing Data” is provided for elementary and junior high schools in one of four areas of mathematics, and “Analyzing Data” and “Statistical Inference” are provided for high school. Moreover, “Information and Data Science” is provided in informatics of high school curriculum. As a result, contents of statistical methods for solving a problem have been built into the new curriculums from elementary to high schools consistently. For maximally bring out the effect of new curriculum, cooperation of industry, government, and academia is indispensable. In order to work together and establish further collaborations for the better future society, the activities of TQE special committee such as “The 7th New Science Technology Education Forum” and Public Comments for the draft of curriculum guidelines as well as the plans for special features throughout this and the next issues are shown in this report.

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　The new national guidelines of school education have been published to make sure education prepare young people for life in the AI era. In this paper we explain the background of reforms of the current guideline and the reason why statistics and programming have become to be in main stream of the whole of school education from elementary to high school in the view point of data science. In “Future Investment Strategy 2017” and “Future Investment Strategy 2018” shown by Japanese Government the technological innovations of the Fourth Industrial Revolution is focused toward to realize “Society 5.0”. For that, it is also pointed out in the future strategy that education to cultivate data analytical skills for students and qualities that are irreplaceable by AIs like problem solving skills is strongly progressed. We show the several features of curriculum in that new guideline and its relationship to the national educational policy which supports so called Cyber-Physical System in Society 5.0.

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　From the late 1970’s to the 1980’s, Japanese products with high quality and high reliability took up a high share in U. S. market. At that time, U. S. faced the crisis of “twin deficits,” enormous trade deficit and fiscal deficit. During the above period, Dr. Juran presented a paper in 1978, NBC broadcasted “If Japan can, why can’t we?” by television in 1980, and Hewlett-Packard Co.（HP） introduced Japanese TQC into the company overall, etc. Thereafter, Mr. Young, a president of HP, was appointed to be a chairperson of President’s Commission on Industrial Competitiveness under the Regan administration. In 1985, he wrote “Young Report” and insisted that industrial international competitiveness mainly on the manufacturing should be strengthened as a pressing issue. “Made in America-Regaining the Productive Edge” by MIT professors, “A SCANS Report for America 2000” by US Department of Labor, “Innovate America（Palmisano Report）” in 2004 by Council on Competitiveness（COC）, and the reinforcement of STEM program under the Obama administration lead to the innovation of U. S. that is using Big Data, IoT, and AI of today. As shown in the above, innovation of industry and education cannot be achieved without any cooperation between industry, government, and academia. Now it’s time to learn how to cooperate altogether, which is the urgent issue for Japan.

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　This article introduces JSQC-Std 41-001 “Guidelines for Quality Management Education and Training” developed by the Japanese Society for Quality Control（JSQC）in 2017. To practice TQM, all persons working in the organization need to address to policy management, daily management and small group improvement activities with understanding fundamental principles of TQM. However, there is no ISO standard or JIS which shows how to develop persons who has abilities necessary for these activities, and therefore, each organization devices its own hierarchy-and-field-wise education and training system and quality management education and training system case-by-case based on the contexts of the organization. This JSQC standard provides guidance to plan, implement, evaluate and improve quality management education and training in order to establish basis for practicing TQM effectively and efficiently.

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