In the paper an approach to assessing the conformity of industrial production, individual enterprises in particular and the economy as a whole has been developed in accordance with the smart industrialization criteria, as well as the level of smartization of advanced countries’ economies and Ukrainian economy has been assessed using this approach.Currently, there are no approaches that allow a fairly objective assessment of the smart industrialization level of a country's economy or individual enterprises. Given that the introduction of smart technologies (big data, the Internet of things, smart sensors, etc.) is accompanied by an increase in investments in computer software and databases (software and DB), these investments or the costs of software and DB, used in manufacturing, which will directly correlate with the complexity of the tasks, solved by cybernetic systems, should become the base for evaluating the level of smartness. The paper shows the feasibility of using the ratio of software and DB costs to costs of machinery and equipment (“smartization” of capital) and the ratio of software and DB costs to the value added (“smart intensity” of manufacturing). These proportions have been used as the basis of the required composite indicator of industrial enterprises’ compliance with the smart industrialization criteria. The obtained indicator has a number of advantages: it’s simple to calculate; the obtained estimates are stable and reliable; it doesn’t require the initial data to be comparable; it can be analyzed over time; it’s invariant to the scale of manufacturing and can be calculated both for individual enterprises and for the industry or economy as a whole.The indicator was tested on data from Australia, Germany, the Czech Republic and Ukraine, while modeling for Ukraine was complicated by the lack of official statistics on the value of software and DB for the manufacturing industry. The analysis of the modeling results led to the conclusion that Ukraine lags behind the advanced economies in terms of smart industrialization: investments in machinery and equipment are not accompanied by investments in the creation of the software environment of the smart industry. The created indicators can be used to substantiate managerial decisions at both the micro and macro levels, in particular - to determine directions of investments, compare with advanced economies and companies, and also to assess the effectiveness of government policies in the smart industrialization.

assessment; smart industrialization; processing industry; smart enterprises; economic and mathematical modeling

Доклад о мировом развитии «Цифровые дивиденды». Washington: Международный банк реконструкции и развития. Всемирный банк, 2016. 58 с.

Зоркальцев В. И. Индексы цен и инфляционные процессы. Новосибирск: Наука, Сибирская издательская фирма РАН, 1996. 279 с.

Капітальні інвестиції за видами активів за 2010-2017 роки. Капітальні інвестиції (щоквартальні показники) за січень-грудень 2018 року. Укрстат, 2019. URL: http://www.ukrstat.gov.ua/.

Мадих А. А., Охтень О. О., Дасів А .Ф. Моделювання фактору цифровізації виробництва в процесі становлення смарт-промисловості (на прикладі переробної промисловості Німеччини): науково-аналітична доповідь. НАН України, Ін-т економіки пром-сті. К., 2018. 41 c.

Мадых А. А., Охтень А. А. Моделирование трансформации влияния производственных факторов на экономику в процессе становления смарт-промышленности. Экономика промышленности. 2018. № 4 (84). С. 26-41. doi: http://doi.org/10.15407/econindustry2018.04.026.

Річна інформація емітента цінних паперів ПАТ «НКМЗ» за 2016 рік. НКМЗ, 2019. URL: http://www.nkmz.com/fileadmin/services/actioners/godovoy_otchet_2016.pdf

Річна інформація емітента цінних паперів ПрАТ «НКМЗ» за 2017 рік. НКМЗ, 2019. URL: http://nkmz.com/fileadmin/services/actioners/godovoy_otchet_2017.pdf

Суслов И. П. Основы теории достоверности статистических показателей. ИЭОПП СО АН СССР. Новосибирск: Наука. Сиб. отд-е, 1979. 304 с.

Brynjolfsson E., McElheran K. Data in Action: Data-Driven Decision Making in U.S. Manufacturing. 2016. URL: http://www.economics.cornell.edu/sites/default/files/files/events/Brynjolfsson_McElheran_AEA_2016.pdf.

Digitale Wirtschaft und Gesellschaft / Bundesministerium für Bildung und Forschung, 2018. URL: https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html.

Douglas P. The Cobb-Douglas Production Function Once Again: Its History, Its Testing, and Some New Empirical Values. The Journal of Political Economy. 1976. 5 (84). pp. 903-916. doi: http://dx.doi.org/10.1086/260489.

Fixed assets by activity and by asset, ISIC rev4 / OECD, 2018. URL: https://stats.oecd.org/Index.aspx?DataSetCode=SNA_TABLE9A.

Griliches Z., Mairesse J. Production Functions: The Search for Identification. Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (Econometric Society Monographs). Cambridge: Cambridge University Press, 1999. pp. 169-203.

Industrie 4.0. Germany Trade & Invest, 2018. URL: https://www.gtai.de/GTAI/Navigation/EN/Invest/Industries/Industrie-4-0/Industrie-4-0/industrie-4-0-what-is-it.html.

Kim K., Jung J.-K., Choi J.-Y. Impact of the Smart City Industry on the Korean National Economy: Input-Output Analysis. Sustainability. 2016. 8 (7). pp. 649-678.

Madykh A.A., Okhten O.O., Dasiv A.F. Analysis of the world experience of economic and mathematical modeling of smart enterprises. Economy of Industry. 2017. № 4 (80). pp. 19-46. doi: http://doi.org/10.15407/econindustry2017.04.019.

Solow R. M. Technical Change and the Aggregate Production Function. The Review of Economics and Statistics. 1957. Vol. 39, No. 3. pp. 312-320. doi: http://dx.doi.org/10.2307/1926047.

Value added and its components by activity, ISIC rev 4. OECD, 2018. URL: https://stats.oecd.org/Index.aspx?DataSetCode=SNA_TABLE6A.