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Volume 44 (2014)
- Issue 4 Pages 362-
- Issue 3 Pages 270-
- Issue 2 Pages 166-
- Issue 1 Pages 5-
Volume 44, Issue 3
Displaying 1-9 of 9 articles from this issue
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Features
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Keiichiro NAKAGAWA, Takashi NAMATAMEArticle type: Features 〔A Forward of Feature Articles : Quality Control in Big Data Generation〕
2014 Volume 44 Issue 3 Pages 270-275
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSRecently, "bigdata" becomes very popular word in business. This word expresses not only the change of technological perspective, but also the paradigm shift of change of viewpoint for business. Moreover, "business analytics" faces on new challenge age over essentially different from existing business intelligence or data analysis. In this article, we explain the recent trend of bigdata and business analytics. First, we discuss the characteristics of bigdata from IT and analytical perspective. Next, we explain the changes of bigdata utilization. Then some typical characteristics of business analytics are shown, i.e. some topics which is overcome the existing analytical method like business intelligence. Finally, the expectations of future direction of bigdata and business analytics are pointed out.View full abstractDownload PDF (738K) -
Hironobu KAWAMURAArticle type: Features 〔A Forward of Feature Articles : Quality Control in Big Data Generation〕
2014 Volume 44 Issue 3 Pages 276-279
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSAlthough the word of big data is very popular in Japan nowadays, it is not introduced many examples about manufacturing processes relevant to big data. This paper reports several examples of big data on process control for semiconductor manufacturing. Specifically, the number of the measurement point on a wafer, the sensors of semiconductor equipments, QWACS (Quality early Warning Alarm Control System) for real-time statistical process control are introduced, comparing the past with the present. Moreover, QWACS has been developed by NEC Corporation. Furthermore, the examples are discussed from the viewpoints of volume, variety, velocity and veracity which the feature of big data has.View full abstractDownload PDF (512K) -
Shozo ISHIBASHIArticle type: Features 〔A Forward of Feature Articles : Quality Control in Big Data Generation〕
2014 Volume 44 Issue 3 Pages 280-285
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSThe problem obtained from the action of the BIG DATA in our plant cultivation experiment could measure quantity of photosynthesis that I could not measure conventionally. Therefore, based on quantity of photosynthesis measurement procedure studying in cooperation with Ehime University, I suggest the action of BIG DATA.View full abstractDownload PDF (11583K) -
Mutsumi YOSHINOArticle type: Features 〔A Forward of Feature Articles : Quality Control in Big Data Generation〕
2014 Volume 44 Issue 3 Pages 286-293
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSRecently, it is required to treat the big-data also in the manufacturing industry. They are laboratory data, inspection data, diagnosis data, and so on. But, unfortunately, they are not able to compute with conventional multivariate analysis. The causes are an increase of power of a test and the curse of dimensionality. Then, the new statistical method called "data-driven analysis" is applied as solution. On the other hand, conventional SQC method corresponds to "event-driven analysis". The difference between both is a difference of the conditional probability used as a criterion of judgment. Event-driven analysis uses P(O | T), and data-driven analysis uses P(T | O), where T is theory and O is observation. In in-house statistics education, the educational content that makes this difference clear was desired. This report reviews how this subject was tackled by several examples in DENSO.View full abstractDownload PDF (1007K) -
Kazunori YAMAUGHIArticle type: Features 〔A Forward of Feature Articles : Quality Control in Big Data Generation〕
2014 Volume 44 Issue 3 Pages 294-297
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSIn knowledge-based society, it is shared understanding throughout the world that "Statistical Thinking" and "Competency of Statistical Analysis" are substantial skills for detecting and solving new issues. Building an educational system which aims to foster these abilities is internationally proceeding. There are no departments and colleges named "Statistics" in Japan, but demands for statistics and data science education are increasing. Reinforcing statistical education for data science people is one of the most pressing issues for universities in Japan. In this paper, we introduce activities of Center for statistics and information and College of Business in Rikkyo University. One of our trials is to introduce action learning methods and combine leadership developing program.View full abstractDownload PDF (527K)
Serial
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Masaaki KANEKOArticle type: Serial [Understand the Specialty Skills for the Small and Medium-Sized Companies to Survive this Age of Change]
2014 Volume 44 Issue 3 Pages 298-301
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSAt the end of this serial, 6 key concepts of designing and implement the quality management system (QMS) for the sustained success in the targeted business area is explained. Also, the 4 steps for designing QMS based on the 6 key concepts are presented as a guide to introducing new quality management model in this age of change.View full abstractDownload PDF (865K) -
Yuichiro KATOArticle type: Serial [The New Thinking Method and Organization Management to Improve Values of Business]
2014 Volume 44 Issue 3 Pages 302-306
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSThere is a need for companies to enhance the skill to create new goal. This article introduces a new QC story to create new goal. This is called "ideal-seeking QC Story". This methodology was born through the NIT Innovation Research Association, which is co-sponsored by the Union of Japanese Scientists and Engineers and Nagoya Institute of Technology. Among the companies that participated in this research association, this article introduces a case of B2C companies.View full abstractDownload PDF (5637K)
Research Papers
Survey and Field Study
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Takeshi OKITA, Takeshi NAKAJOArticle type: Survey and Field Study
2014 Volume 44 Issue 3 Pages 329-340
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSMany studies have been implemented on success factors and effective tools for new product development from the viewpoint of market research and product planning, design and development, or sales and after-sales service. New product development, however, cannot be fruitful without close collaboration between elements no matter how individual elements mature. This study focused on information sharing, and then investigated many companies by mail and identified the relationships between information sharing and outcomes of new product development as well as the relationships of organizations/processes for new product development, company-wide quality management activities and information sharing. As the result, it was found that degree of information sharing between departments has close relationships with outcomes of new product development, and it is essential to promote sharing of the following information because it has close relationships with the outcomes: 1) company goals, 2) know-how about quality assurance and improvement activities, 3) know-how about past failures, 4) information on product overview, quality objectives and development plan/schedule, 5) information on product design, 6) information on production/supply processes, 7) information on customers and sales methods, and 8) information on bottle-neck technologies and issues that occurred during development. In addition, it was also found that in order to promote sharing of the information, overall level of organization/process for new product development, especially consistency of process, as well as overall level of company-wide quality management activities should be enhanced.View full abstractDownload PDF (1339K)
Applied Research
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Masanobu HIGASHIDE, Ken NISHINA, Hironobu KAWAMURAArticle type: Applied Research
2014 Volume 44 Issue 3 Pages 341-350
Published: July 15, 2014
Released on J-STAGE: June 05, 2017
JOURNAL RESTRICTED ACCESSIn semiconductor manufacturing processes, pattern of variation with-in wafer could reflect more serious problems than its amount of variation. This pattern of variation caused by several sources, such as adhesion to processing equipment of by-product, main parts deterioration, equipment maintenance of by-product removal and parts replacement, etc., has a peculiar repetition. In order to monitor this pattern of variation, a conventional R chart, which is used for monitoring the amount of variation, is not a suitable method because R chart does not provide any information of the variation pattern. In this case, we can apply a multivariate control chart which considers measurement positions as variables. In this paper, T^2-Q control chart proposed by Jackson and Mudholkar (1979) is applied to monitor the pattern of variation. A proposal and discussion are given as follows, 1) How to divide principal components consisting of T^2 and Q statistics. 2) Application of contribution plot 3) Significance of variation pattern monitoring This proposal is a method that can convert a viewpoint from monitoring an amount of variation by the conventional control charts into monitoring a pattern of variation.View full abstractDownload PDF (1125K)
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