In this paper, methodology of developing highly reliable system using FRAM (Functional Resonance Analysis Methodology) is described. Increase of IoT or AI applications for the safety critical systems such as self-driving car is changing the system engineering. The main factor of the change is that the system itself is beginning to have capability to adopt to environment change by IoT or machine
learning. In this situation, the need for the new way of system engineering is getting attention,
especially for the system which has great flexibility to cope with the changing environment. FRAM
is the methodology to model how each function is connected and interacting with each other to cope
with the changes.
Confusing package designs or drug names can cause severe medical accidents or incidents. We
applied a data mining technique to incident case data and found drug mixing-up cases caused by
confusing designs or names. In this article, we reviewed studies on indices to measure similarities of label designs of injection ampoule container, PTP package designs for tablets/capsules, look-alikeness and sound-alikeness of drug names.
Management technology research group consists of 124 members, and aims to develop necessary methods
and tools in order to enhance each organization’s business management under a rapidly changing environment.
In this article, the activities implemented by each working group (WG) in our research group are reported.
At first, its purpose and operation system are introduced, and the activity overview of 4 WGs is shown. Then,
each WG leader explains the contents of its activities and the obtained research result during this one year. If
you have any questions or you are interested in our activities, please don’t hesitate to contact us.
This report introduces the 7th research meeting for knowledge sharing of “ Service Excellence Division ” and
“ Production Innovation Division ”. We focus on and discuss current issues of Digital Transformation in Japan.
This proposal describes the checklist for the sustainable prevention of injustice based on the practical sustained quality management system『sustainable QMS』. Organizational intellectual assets base on ISO 9000, ISO 9001 and ISO 9004 are not sufficient for the sustainable prevention of injustice. The supplier’s QMS level, top management initiation in QMS, quality management specialist, and quality officer CQO are essential for the sustainable prevention of injustice. This article propose the checkpoint for the sustainable prevention of injustice based on practical quality management system『the practical sustainable QMS』
In order to manage competence of personnel who contribute to quality assurance，a practical method for competence evaluation which can specifically indicate knowledge and skills is required．Though such a method for competence evaluation has been developed in a clinical laboratory test，effectiveness of this method has not yet analyzed．This method indicates that competence items are factors that affect the variation of test results using cause-and-effect diagrams．The purpose of this study is to verify the effectiveness of the method of developing competence evaluation items through description of cause-and-effect diagrams in clinical laboratory tests．We applied and compared the aforementioned method with that of developing competence evaluation items through description of the operation process．To compare the results，we developed two original indicators ： one expressing the potential competence to be achieved and the other expressing the difference in competence levels among clinical technologists．The findings verified the effectiveness of the method that used cause-and-effect diagrams for enhancing competence levels．It is implied that compared to the method that used description of the operation process，the method of building competence evaluation items using cause-and-effect diagrams is more useful for evaluating the required knowledge for quality assurance of clinical tests．
Behavior observation is effective in understanding latent needs that customers themselves are not aware of. However, in behavior observation, it is necessary to find a need while processing a large amount of information at a time, and its success depends on the skill of the observer. In order to deal with this problem, Sekiya et al. have proposed the limited observation method that makes it easier to find latent needs by narrowing down the behaviors to be observed in advance. In this study, the behaviors to be observed were narrowed down by various methods, and the behaviors were actually observed. Based on the obtained data, the relationships between how to narrow down the behaviors to be observed and the needs obtained were investigated. As a result, it showed the followings: 1)Under the condition that the total observation time is the same, when the number of behaviors to be
observed decrease, the number and concreteness of the newly obtained required qualities related to the focused quality increase; 2) When narrowing down the behaviors to be observed, the behaviors related to the required quality to be obtained should be selected as a set; 3) The relationships between how to narrow down the behaviors to be observed and the obtained required qualities are the same even if the observers are different.
Preferable analysis method has not been established as an analysis for temporal data or for a covariate which influences the response. ANCOVA is a possible candidate, and also there are three alternative candidates. First is regression analysis which uses quantification method of the first type. Second is ANOVA which uses
differences between before-treatment and after-treatment. And the last is usual ANOVA. Power of test of treatment effects as well as the bias of estimation in treatment effects for the four methods are compared, and also were investigated by typical examples and numerical simulations. If it is not to be particular about the theory hard, the use of ANCOVA or usual ANOVA is proposed as follows, under consideration for the size of the correlation in the response and the covariate. When there is the correlation between the response and covariate, ANCOVA is appropriate and usual ANOVA is suitable only when the correlation is small, because it can’t adjust the correlation. When the correlation is relatively high and the treatment effects are about equal to the size of the standard deviation, power of test must meet the practical demands enough.