Researcher's Profile

  • Project Professor
  • Naoto IMURA
  • Progressive Logistic Science
E-mail
nimurag.ecc.u-tokyo.ac.jp
Tel
03-5452-5225
FAX
03-5452-5225
URL

Biography

March 1980 Faculty of Agriculture, The University of Tokyo (UTokyo)
April 1980 R&D, Ajinomoto General Foods, Inc.
March 1992 Ph.D. UTokyo
July 2002 Director Worldwide Technology, R&D Center of Excellence, Kraft Foods, UK
July 2004 Director, Logistics, Ajinomoto General Foods, Inc.
June 2011 Executive Officer, R&D, Ajinomoto General Foods, Inc.
June 2015 Managing Executive Officer, Ajinomoto General Foods, inc.
June 2017 Audit & Advisory Board Member, Ajinomoto AGF, Inc.
July 2019 Project Professor, RCAST, UTokyo

Research Interests

Logistics is a major social issue
The shortage of labor is a critical issue in all industries in Japan due to decreasing population and changes in the social environment, such as strengthening compliance and work style reform.  It is even more serious in the logistics industry which is more labor-intensive than other industries. The issue may have a major impact on the development of economy and industry for the future.     

Paradigm shift is required in logistics industry
Logistics has been optimized based on human intuition and experiences. The method is no longer effective in the current social situation and application of emerging technologies, such as AI and IoT, is required to solve the issues and optimize logistics.    

Need more science literacy in the industry
However, there are few people who can use the technologies in the industry, and more education is expected in universities about the application of the technologies to the logistics industry.     

Mission of Advanced Logistics Science Laboratory
In this laboratory, Advanced Logistics Science, we aim to develop scientific knowledge of students who can solve issues in logistics by the emerging technologies and build supply chain based on science. We also study solutions to various issues in logistics using the emerging technologies and various mathematical methods.

Keywords

logistics, supply chain, mathematical model, data analysis, optimization, AI, IoT

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