Deep Learning Approach for Supply-chain Network Analysis
| Project/Area Number |
26330344
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Web informatics, Service informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MORI Junichiro 東京大学, 政策ビジョン研究センター, 准教授 (30508924)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 取引ネットワーク / 機械学習 / ネットワーク分析 / 深層学習 / サプライチェーン |
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| Outline of Final Research Achievements |
Aiming at supporting business partner recommendations and designing sustainable supply-chain networks, we propose the method to analyze customer-supplier networks using a machine learning approach. In particular, we extracted latent features from the structure of large and heterogeneous customer-supplier networks using representation learning. And we obtained the learning model which generalizes the structure of the customer-supplier networks. We also developed the business partner recommendation system based on the model. Finally, our results showed the important latent features for predicting potential business partners from a customer-supplier network. Those features can be also utilized for designing resilient supply-chain networks.
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Report
(4 results)
Research Products
(9 results)
