DEFINING CALCULATION HORIZON FOR INNOVATION PROJECT MODELLING
DOI:
https://doi.org/10.14529/em160406Keywords:
data mining, production function calculation horizon, innovation activityAbstract
The article is devoted to the problems of data mining. An algorithm for the simulation of industrial enterprises
production has been developed. A modified production function of Cobb-Douglas taking into account
the high material consumption in industrial production and autonomous technical progress, Hicksneutral,
was taken as a model. Modelling is carried out by a programming language “R” adjusted for the effects
of multicollinearity factors through the mechanism of ridge regression.
The article suggests the author's method of estimation of the innovative activity of industrial enterprise
in the implementation of the investment project on the basis of the calculation of the integral dynamics of indicators
of production elasticity obtained in the simulation. It is justified that the proposed method takes into
account the specific features of the innovative project and its autonomous impact on the final results of the
enterprise industrial production.
It solved the problem of determining the methodological horizon for simulation operations of an industrial
enterprise in the evaluation of its innovative activity as a result of the innovative project implementation
which is associated with the moment of transition of the net cash flow from the project results into a positive
zone.
The developed method was tested on the data of the joint-stock company “CPRP” in implementing the
“Vysota 239” innovative project.
References
Han Jiawei, Micheline Kamber, Jian Pei. Data
mining: concepts and techniques. – 3 ed. – Morgan
Kaufmann, 2012. – 1 р.
Larose Daniel T., Chantal D. Larose. Discovering
knowledge in data: an introduction to data
mining. – 2 ed. – John Wiley & Sons, 2014. – 23 р.
Барсегян, А.А. Анализ данных и процессов:
учебное пособие / А.А. Барсегян, М.С. Куприянов,
И.И. Холод и др. – 3-е изд., перераб. и доп. – СПб.:
БХВ-Петербург, 2009. – 91 с.
Cross Industry Standard Process for Data
Mining, CRISP-DM 1.0 Step-by-step data mining
guide (2000). – https://the-modelingagency.
com/crisp-dm.pdf. – 10 p.
Мохов, В.Г. Корпоративный форсайт и
оценка инновационной активности промышленно-
го предприятия / В.Г. Мохов, К.С. Стаханов //
Вестник ЮУрГУ. Серия «Экономика и менедж-
мент». – 2015. – Т. 9, № 3. – С. 61–67.