Daicel Corporation

Marina Takahashi

Hiroshi Okamoto

Shunji Mitsuyoshi

Masahiro Haitsuka

Shuji Shinohara

Fumihiro Miyoshi

Hidetoshi Kozono

A regression model reflecting causality between variation of an explanatory variable and variation of a response variable is constructed. A prediction apparatus predicts characteristic values of a product by using process data obtained from a production facility. The prediction apparatus includes a process data acquisition unit that reads the process data from a storage device that stores the process data obtained from the production facility, and a prediction model generation unit that generates a prediction model on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data. The first process data and the second process data or the value corresponding to the second process data are included in the read process data. The first process data is used as a predetermined explanatory variable. The second process data or the value corresponding to the second process data is used as response variable. The prediction model has learned features of the process data obtained from the production facility. The prediction model generation unit generates the prediction model and determines a positive/negative variation direction of the response variable in accordance with a positive/negative variation direction of the predetermined explanatory variable.

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Tokyo, Japan

Marina Takahashi