Directory of Researchers

WATANABE Ruriko
Graduate School of System Informatics / Department of System Science
Assistant Professor
Management Engineering
Last Updated :2022/09/16

Researcher Profile and Settings

Affiliation

  • <Faculty / Graduate School / Others>

    Graduate School of System Informatics / Department of System Science

Teaching

Research Activities

Research Areas

  • Informatics / Web and service informatics
  • Social infrastructure (civil Engineering, architecture, disaster prevention) / Social systems engineering
  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering) / Control and systems engineering

Published Papers

  • Ruriko Watanabe, Nobutada Fujii, Daisuke Kokuryo, Toshiya Kaihara, Kyohei Irie, Kenji Yanagita, Kenichi Harada

    Springer International Publishing, 2021, Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, 224 - 231

    In book

  • Ruriko Watanabe, Nobutada Fujii, Daisuke Kokuryo, Toshiya Kaihara, Yoichi Abe

    This study was conducted to devise a method for supporting consulting service companies in their response to client demands irrespective of the expertise of consultants. With emphasis on revitalization of small and medium-sized enterprises, the importance of support systems for consulting services to serve them is increasing. Those systems must support solutions to difficulties that must be addressed by enterprises. Consulting companies can respond to widely various management consultations. Nevertheless, because the consultation contents are highly specialized, service proposals and problem detection depend on the experience and intuition of the consultant. Often, stable service cannot be provided. A support system must provide stable services independent of the ability of consultants. In this study, analyzing customer information describing the contents of consultation with client companies is the first step in constructing a support system that can predict future problems. Text data such as a consultant’s visit history, consultation contents by e-mail, and contents of call centers are used for analyses because the contents can explain current problems. They might also indicate future problems. This report describes a method to analyze text data using text mining. The target problem is fraud, which includes uncertainty: cases in which it is not clear whether a fraud problem has occurred with the company. To address uncertainty, a method of using logistic regression models is proposed to represent inferred values as probabilities, rather than as binary discriminated data, because the possibility exists that some misidentified companies might have some difficulty. As described herein, computer experiments are conducted to verify the effectiveness of the proposed method and to compare consultants’ forecasted and achieved results. Results of a verification experiment are presented in the following. First, the proposed method is applicable to problems including uncertainties. Secondly, the possibility exists of discovering companies with a fraud problem of which they are unaware.

    Lead, Fuji Technology Press Ltd., 05 Sep. 2020, International Journal of Automation Technology, 14 (5), 779 - 790, English

    [Refereed]

    Scientific journal

  • Ruriko WATANABE, Nobutada FUJII, Daisuke KOKURYO, Toshiya KAIHARA, Yoichi ABE

    Technical University of Kosice, Faculty of Electrical Engineering and Informatics, 30 Jun. 2020, Acta Electrotechnica et Informatica, 20 (2), 3 - 10

    Scientific journal

  • Ruriko Watanabe, Nobutada Fujii, Daisuke Kokuryo, Toshiya Kaihara, Yoichi Abe, Ryoko Santo

    This study aims to build a support method for consulting service companies allowing them to respond to client demands regardless of the expertise of the consultants. With an emphasis on the revitalization of small and medium-sized enterprises, the importance of support systems for consulting services for small and medium-sized enterprises, which support solving problems that are difficult to deal with by an enterprise, is increasing. Consulting companies can respond to a wide range of management consultations; however, because the contents of a consultation are widely and highly specialized, a service proposal and the problem detection depend on the experience and intuition of the consultant, and thus a stable service may occasionally not be provided. Therefore, a support system for providing stable services independent of the ability of consultants is desired. In this research, as the first step in constructing a support system, an analysis of customer information describing the content of a consultation with the client companies is conducted to predict the occurrence of future problems. Text data such as the consultant’s visitation history, consultation content by e-mail, and call center content are used in the analysis because the contents explain not only the current problems but also possibly contain future problems. This paper describes a method for analyzing the text data by employing text mining. In the proposed method, by combining a correspondence analysis with a DEA (Data Envelopment Analysis) discriminant analysis, words that are strongly related to problem detection are extracted from a large number of words obtained from text data, and variables of the DEA discriminant analysis are reduced and analyzed. The proposed method focuses on a cancellation of contract problems. The cancellation problem does not include uncertainty; it is clearly known whether the contract of the consulting service is being updated or cancelled. In this study, computer experiments were conducted to verify the effectiveness of the proposed method through a comparison with an existing method. The results of the verification experiment are as follows. First, there is a possibility of discovering new factors that cannot be determined from the intuition and experience of the consultant regarding the target problem. Second, through a comparison with the existing method, the effectiveness of the proposed method was confirmed.

    Lead, Fuji Technology Press Ltd., 05 Jul. 2018, International Journal of Automation Technology, 12 (4), 482 - 491

    [Refereed]

    Scientific journal

  • Ruriko Watanabe, Nobutada Fujii, Daisuke Kokuryo, Toshiya Kaihara, Yoshinori Onishi, Yoichi Abe, Ryoko Santo

    This study aims to build a supporting method for consulting service companies so that the companies can respond to client's demand regardless of the expertise of consultants. Occurrence of future problems in client companies is predicted by using text mining with data taken from a consulting company correspondence analysis and DEA discriminant analysis are employed. Computer experiments are conducted to verify the effectiveness of the proposed method.

    Elsevier B.V., 2018, Procedia CIRP, 67, 569 - 573, English

    [Refereed]

    International conference proceedings

  • Xinyue Wang, Nobuteda Fujii, Ruriko Watanabe, Kokuryo Daisuke, Toshiya Kaihara

    IEEE, Dec. 2021, 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)

    International conference proceedings

Association Memberships

  • 精密工学会

  • システム制御情報学会