[1M4-J-13] AI application: industries and markets

[1M4-J-13-01] The local transactional structure based estimation of inter-firm relationships which mediates industrial modules

Kohei Kanda¹, 〇Hajime Sasaki², Hiroko Yamano², Ichiro Sakata¹ (1. Dept. of Technology Management for Innovation, Graduate School of Engineering, The University of Tokyo, 2. Policy Alternatives Research Institute, The University of Tokyo)

Keywords:Social System, Machine Learning Applications

Inter-firm relationships formed by business transactions are the resource of companies. Especially, the relationship which enables companies to produce radical and flexible innovation is important in adjusting recent rapidly changing business market. First, we identied relationships mediating industrial modules which give companies nonredundant knowledge and information through structural analysis of large transactional network. Next, we predicted these relationships by machine learning model using the feature quantities designed from the standpoint of local companies in the network. There are three suggestions in our research. First, industries which become the basis of other industries tend to have module mediating relationships. Second, the transactions with companies which have rarer attributes are likely to mediate modules. Third, the transactions between sales-oriented buyer and procurement-oriented supplier have tendency to mediate modules. These estimations are expected to be useful to choose business partners in seeking nonredundant information or opportunities to access to new market.