Artem A. Madykh, Oleksiy O. Okhten, Alla F. Dasiv, 80_02
Wednesday, 06 December 2017 00:28 | Author: Покотиленко Р.В. | 
Artem A. Madykh
candidate of economic sciences
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it ;
Oleksiy O. Okhten
candidate of economic sciences
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it ;
Alla F. Dasiv
candidate of economic sciences
Institute of the Economy of Industry of the NAS of Ukraine
03057, Ukraine, Kiev, Zhelyabova str., 2
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it
Analysis of the world experience of economic and mathematical modeling of smart enterprises
Section: Problems of strategy development, financial and economic regulation in industry
Ekon. promisl. 2017, 80(4): 19-46
Language: English
Abstract | Full text (PDF)
DOI: https://doi.org/10.15407/econindustry2017.04.019
References
1. Analysis of World Experience in the Development of Industry and Approaches to the Digital Transformation of the Industry of the Member States of the Eurasian Economic Union: Informational and Analytical Report (2017). Moscow: Eurasian Economic Commission [in Russian].
2. Byelyakov, K.I. (2008). Informati-zation in Ukraine: Problems of organiza-tional, Legal and Scientific Support: mono-graph. Кyiv: KVIC [in Ukrainian].
3. Vasilenko, V. (2013). Technological Modes in the Context of the Aspiration of Economic Systems to the Ideal. Sotsial'no-ekonomichni problemy i derzhava, 1, Vol. 8, pp. 65-72 [in Russian].
4. Voronin, A.V. (2013). Modeling of Technical Systems. Tomsk: Tomsk Poly-technic University [in Russian].
5. Glazev, S.Yu. (2012). The Modern Theory of Long Waves in the Development of the Economy. Ekonomicheskaya nauka sovremennoy Rossii, 2 (57), pp. 8-27 [in Russian].
6. Gromova, T. Will the world survive the fourth industrial revolution? Delovaya stolitsa. Retrieved from http://fastsalttimes.com/sections/obzor/522.html [Accessed 07 Oct. 2017] [in Russian].
7. "Digital Dividends" World Devel-opment Report (2016). Washington: Interna-tional Bank for Reconstruction and Devel-opment / World Bank [in Russian].
8. Dyuzhev, D.V. (2008). The Specifics of Information Society Formation in Ukraine: History and Modernity. Nauka. Relihiya. Suspil'stvo, 2, pp. 130-135 [in Russian].
9. The Law of Ukraine "On the Basic Principles of the Development of the Infor-mation Society in Ukraine for 2007-2015" (2007). Uryadovyy kur"yer, 28 [in Ukrainian].
10. Information Technologies in Ukraine: The Colossus on Clay Feet. Retrieved from https://dou.ua/lenta/articles/it-in-ukraine/ [Accessed 07 Oct. 2017] [in Russian].
11. Information Technologies as the Stimulus for the Development of Ukrainian Society. Retrieved from http://data.ngorg. od.ua/ru/informacionnye-tehnologii-kak-stimul-razvitiya-ukrainskogo-obshchestva [Accessed 11 Sept. 2017] [in Russian].
12. Kaplan, A.V., Kaplan, V.E., Mas-chenko, M.V., & Ovechkina, E.V. (2007). The Solution of Optimization Tasks in the Economy. Moscow: Fenix [in Russian].
13. Kupriyanovskiy, V.P., Namiot, D.E., & Sinyagov, S.A. (2016). Cyber-Physical Systems as the Basis of the Digital Economy. International Journal of Open Information Technologies, 2, Vol. 4, pp. 18-25 [in Russian].
14. Kupriyanovskiy, V.P., Sinyagov, S.A., Namiot, D.E., Utkin, N.A., Nikolaev, D.E., & Dobryinin, A.P. (2017). Transformation of Industry in the Digital Economy – Design and Manufacturing. International Journal of Open Information Technologies, 1, Vol. 5, pp. 50-70 [in Russian].
15. Paklin, N.B., & Oreshkov, V.I. (2009). Business Intelligence: from Data to Knowledge. St. Petersburg: Peter [in Russian].
16. The Portrait of a Ukrainian IT professional. Retrieved from https://dou.ua/ lenta/articles/portret-ukrainskogo-it-specialista/ [Accessed 19 Sept. 2017] [in Russian].
17. "Industry 4.0": Creation of a Digital Enterprise. The Main Results of the Research in the Metallurgical Industry/ PwC. Retrieved from https://www.pwc.ru/ru/ mining-and-metals/publications/assets/industry-4-metals-key-findings_rus.pdf [Accessed 28 Sept. 2017] [in Russian]
18. Distributed data processing using Hadoop. IBM, 2017 [in Russian].
19. Romanchuk, Ya. Third industrial revolution: essence, influence, consequences. Retrieved from http://liberty-belarus.info/o-kapitalizme/kapitalizm-dlya-lyuboznatelnykh/ item/848-tretya-promyshlennaya-revolyutsiya-sut-vliyanie-posledstviya#l2 [Accessed 25 Aug. 2017] [in Russian].
20. State, Problems and Prospects of the Development of the Information Society in the CIS. (2012).Commonwealth of Inde-pendent States. Executive committee. Moscow [in Russian].
21. Trofimova, E. (2016). Cyber Vulnerability of the Smart Manufacturing. Сontrol Еngineering Rossiya, 1 (61), pp. 34-36 [in Russian].
22. Chekletsov, V. (2015). The Fourth Revolution: Internet of Things. Ekspert, January, pp. 42-44. Retrieved from http://www.ncca.ru/file?Files&141 [Accessed 08 Sept. 2017]. [in Russian].
23. Sheer, A.-V. What’s Behind the Term "Industry 4.0". Retrieved from http://www.i-love-bpm.ru/s%D1%81heer/chto-skryvaetsya-za-terminom-industriya-40 [Accessed 08 Sept. 2017]. [in Russian].
24. Atzori, L., Lera, A., & Morabito, G. (2010). The internet of things: a survey. Computer Networks. Режим доступа: https://www.researchgate.net/profile/Luigi_Atzori2/publication/222571757_The_Internet_of_Things_A_Survey/links/546b36df0cf2f5eb180914e5/The-Internet-of-Things-A-Survey.pdf [Acces¬sed 28 Sept. 2017].
25. Auschitzky, E., Hammer, M., & Rajagopaul, A. How big data can impro-ve manufacturing. McKinsey. Режим доступа: http://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-can-improve-manufacturing [Accessed 28 Sept. 2017].
26. Brynjolfsson, E., & McElheran, K. Data in Action: Data-Driven Decision Making in U.S. Manufacturing. Режим доступа: http://www.economics.cornell.edu/sites/default/files/files/events/Brynjolfsson_McElheran_AEA_2016.pdf [Acces¬sed 25 Aug. 2017].
27. Dymola for physical modelling and simulation using Modelica. Claytex. Режим доступа: http://www.claytex.com/ products/dymola/ [Acces¬sed 28 Sept. 2017].
28. Fishwick, P. (2007). Handbook of Dynamic System Modeling (Chapman & Hall/CRC Computer and Information Sci-ence Series). New York: Chapman and Hall/CRC.
29. Geisberger, E., & Broy, M. (2012). Agenda CPS: Integrierte For schungs agenda Cyber-Physical Systems (acatech STUDIE) (German Edition). New York: Springer-Verlag, 297 p.
30.Gröger, C., Niedermann, F., & Mitschang, B. (2012, July) Data Mining-driven Manufacturing Process Optimization. Proceedings of the World Congress on En-gineering (Vol. III, pp. 1475-1481). London: WCE.
31.Hermann, M., Pentek, T., & Otto, B. (2016). Design Principles for Industrie 4.0 Scenarios. 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928-3937.
32. Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014). Toward Scalable Systems for Big Data Analytics: a Technology Tutorial. IEEE Access, 2. pp. 652-687.
33. Industrie 4.0 – What is it? Germany Trade & Invest. Режим доступа: https://industrie4.0.gtai.de/INDUSTRIE40/ Navigation/EN/Topics/Industrie-40/what-is-it.html [Acces¬sed 08 Sept. 2017].
34. Information Resources Manage-ment Association. The Internet of Things: Breakthroughs in Research and Practice (2017). Information Resources Management Association. Hershey: IGI Global.
35.Innovationen für die Produktion von morgen. Режим доступа: https://www.bmbf.de/pub/Industrie_4.0.pdf [Accessed 08 Sept. 2017].
36. Jeschke, S., Brecher, C., Song, H., & Rawat, D. (2017). Industrial Internet of Things. Cybermanufacturing Systems. Her-ausgeber: Springer International Publishing Switzerland.
37. Kim, K., Jung, J.-K., & Choi, J.-Y. (2016). Impact of the Smart City Industry on the Korean National Economy: Input-Output Analysis. Sustainability, 8 (7). pp. 649-678.
38. Lasi, H., Kemper, H.-G., Fettke, P., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engi-neering, 4 (6), pp. 239-242.
39. Lee, J., Lapira, E., Bagheri, B., & Kao, H. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1. pp. 38-41.
40. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. New York: McKinsey Global Institute.
41. Nedelcu, B. (2013). About Big Data and its Challenges and Benefits in Manufacturing. Database Systems Journal, 4, Issue 3, pp. 10-19.
42. Networked Readiness Index (2015). Режим доступа: http://reports.weforum.org/ global-information-technology-report-2015/ economies/#indexId=NRI&economy=UKR [Accessed 25 Aug. 2017].
43. Nonaka, Y. Suginishi, Y., Lengyel, A., & Katsumura, Y. (2015, August) The S-Model: A digital manufacturing system combined with autonomous statistical analysis and autonomous discrete-event simulation for smart manufacturing. IEEE International Conference on Automation Science and Engineering (CASE) (pp. 1006-1011). Gothenburg: IEEE.
44. Reimann, M., & Ruckriegel, C. (2017). Road2CPS Priorities and Recom-mendations for Research and Innovation in Cyber-Physical Systems. Stuttgart: Steinbeis-Editions.
45. Saraee, M. How can companies start implementing the Smart Industry concept? Режим доступа: https://www.smart-industry.nl/site/assets/files/2158/how_can_ companies_start_implementing_the_smart_ industry_concept.pdf [Acces¬sed 08 Sept. 2017].
46. Smart Enterprise demo for manu-facturing. Pharaos Navigator. Режим до-ступа: https://enterprise.win2biz.com/static/ content/en/525/Explaining-Enterprise-Model. html [Accessed 08 Sept. 2017].
47. The 2016 IMD World Com-petitiveness Scoreboard. Режим доступа: http://www.imd.org/globalassets/wcc/docs/scoreboard-2016.pdf [Acces¬sed 08 Sept. 2017].
48. Yin, S., & Kaynak, O. (2015). Big Data for Modern Industry: Challenges and Trends. Proceedings of the IEEE, 2 Vol. 103, pp. 143-146.
49. Zaitsev, D.A. (2012). Universal Petri Net. Cybernetics and Systems Analysis, Vol. 48, Issue 4, pp. 498-511.
50. Zhou, Z., Xie, S., & Chen, D. (2012). Fundamentals of Digital Manufac-turing Science. London: Springer-Verlag London Limited.
candidate of economic sciences
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it ;
Oleksiy O. Okhten
candidate of economic sciences
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it ;
Alla F. Dasiv
candidate of economic sciences
Institute of the Economy of Industry of the NAS of Ukraine
03057, Ukraine, Kiev, Zhelyabova str., 2
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it
Analysis of the world experience of economic and mathematical modeling of smart enterprises
Section: Problems of strategy development, financial and economic regulation in industry
Ekon. promisl. 2017, 80(4): 19-46
Language: English
Abstract | Full text (PDF)
DOI: https://doi.org/10.15407/econindustry2017.04.019
Abstract: The paper shows the inevitability of technological mode shift driven by the Industry 4.0, which implies the ubiquitous implementation of information technology, total automation of various processes and creation of cyber-physical systems with artificial intelligence. This requires a complete restructuring of manufacturing systems and production relations, especially in the economies of those countries that want to take a decent place in the new international division of labour of the digital future.
An analysis of the world experience of such changes connected with smart industrialization, digital transformations of the economy, the emergence of the industrial Internet of Things and big data processing made it possible to draw the conclusion that it is necessary to apply economic and mathematical methods to justify the expediency of such transformations: economic validity, as well as physical viability of newly created systems. The use of the apparatus of economic and mathematical modeling allows studying properties of the smart system that is being designed, evaluating its effectiveness and risks, anticipating the emergence of problems and errors – without the risk of incurring significant losses which is inevitable when making direct changes in the object of research.
Therefore, the purpose of this paper is to study the world experience in the economic and mathematical modeling of smart enterprises and to substantiate its use in the conditions of Ukraine.
The review of publications, reflecting the aspects of economic and mathematical modeling in these areas, allowed to conclude that the methodical and methodological apparatus for modeling these processes is unsystematic and inefficient, as well as to formulate recommendations on the economic and mathematical modeling of smart enterprises in Ukraine. In order to take into account the specific features of Ukraine's technological and institutional development, a number of economic and mathematical modeling tools based on the use of production functions, models of inter-branch balance, network optimization models and simulation models based on stochastic dependencies were offered to support the creation of smart enterprises.
Keywords: Industry 4.0, digital technologies, smart enterprises, big data, economic and mathematical modeling.
JEL code: С00; С60; С67; С69; О12; О14.
An analysis of the world experience of such changes connected with smart industrialization, digital transformations of the economy, the emergence of the industrial Internet of Things and big data processing made it possible to draw the conclusion that it is necessary to apply economic and mathematical methods to justify the expediency of such transformations: economic validity, as well as physical viability of newly created systems. The use of the apparatus of economic and mathematical modeling allows studying properties of the smart system that is being designed, evaluating its effectiveness and risks, anticipating the emergence of problems and errors – without the risk of incurring significant losses which is inevitable when making direct changes in the object of research.
Therefore, the purpose of this paper is to study the world experience in the economic and mathematical modeling of smart enterprises and to substantiate its use in the conditions of Ukraine.
The review of publications, reflecting the aspects of economic and mathematical modeling in these areas, allowed to conclude that the methodical and methodological apparatus for modeling these processes is unsystematic and inefficient, as well as to formulate recommendations on the economic and mathematical modeling of smart enterprises in Ukraine. In order to take into account the specific features of Ukraine's technological and institutional development, a number of economic and mathematical modeling tools based on the use of production functions, models of inter-branch balance, network optimization models and simulation models based on stochastic dependencies were offered to support the creation of smart enterprises.
Keywords: Industry 4.0, digital technologies, smart enterprises, big data, economic and mathematical modeling.
JEL code: С00; С60; С67; С69; О12; О14.
References
1. Analysis of World Experience in the Development of Industry and Approaches to the Digital Transformation of the Industry of the Member States of the Eurasian Economic Union: Informational and Analytical Report (2017). Moscow: Eurasian Economic Commission [in Russian].
2. Byelyakov, K.I. (2008). Informati-zation in Ukraine: Problems of organiza-tional, Legal and Scientific Support: mono-graph. Кyiv: KVIC [in Ukrainian].
3. Vasilenko, V. (2013). Technological Modes in the Context of the Aspiration of Economic Systems to the Ideal. Sotsial'no-ekonomichni problemy i derzhava, 1, Vol. 8, pp. 65-72 [in Russian].
4. Voronin, A.V. (2013). Modeling of Technical Systems. Tomsk: Tomsk Poly-technic University [in Russian].
5. Glazev, S.Yu. (2012). The Modern Theory of Long Waves in the Development of the Economy. Ekonomicheskaya nauka sovremennoy Rossii, 2 (57), pp. 8-27 [in Russian].
6. Gromova, T. Will the world survive the fourth industrial revolution? Delovaya stolitsa. Retrieved from http://fastsalttimes.com/sections/obzor/522.html [Accessed 07 Oct. 2017] [in Russian].
7. "Digital Dividends" World Devel-opment Report (2016). Washington: Interna-tional Bank for Reconstruction and Devel-opment / World Bank [in Russian].
8. Dyuzhev, D.V. (2008). The Specifics of Information Society Formation in Ukraine: History and Modernity. Nauka. Relihiya. Suspil'stvo, 2, pp. 130-135 [in Russian].
9. The Law of Ukraine "On the Basic Principles of the Development of the Infor-mation Society in Ukraine for 2007-2015" (2007). Uryadovyy kur"yer, 28 [in Ukrainian].
10. Information Technologies in Ukraine: The Colossus on Clay Feet. Retrieved from https://dou.ua/lenta/articles/it-in-ukraine/ [Accessed 07 Oct. 2017] [in Russian].
11. Information Technologies as the Stimulus for the Development of Ukrainian Society. Retrieved from http://data.ngorg. od.ua/ru/informacionnye-tehnologii-kak-stimul-razvitiya-ukrainskogo-obshchestva [Accessed 11 Sept. 2017] [in Russian].
12. Kaplan, A.V., Kaplan, V.E., Mas-chenko, M.V., & Ovechkina, E.V. (2007). The Solution of Optimization Tasks in the Economy. Moscow: Fenix [in Russian].
13. Kupriyanovskiy, V.P., Namiot, D.E., & Sinyagov, S.A. (2016). Cyber-Physical Systems as the Basis of the Digital Economy. International Journal of Open Information Technologies, 2, Vol. 4, pp. 18-25 [in Russian].
14. Kupriyanovskiy, V.P., Sinyagov, S.A., Namiot, D.E., Utkin, N.A., Nikolaev, D.E., & Dobryinin, A.P. (2017). Transformation of Industry in the Digital Economy – Design and Manufacturing. International Journal of Open Information Technologies, 1, Vol. 5, pp. 50-70 [in Russian].
15. Paklin, N.B., & Oreshkov, V.I. (2009). Business Intelligence: from Data to Knowledge. St. Petersburg: Peter [in Russian].
16. The Portrait of a Ukrainian IT professional. Retrieved from https://dou.ua/ lenta/articles/portret-ukrainskogo-it-specialista/ [Accessed 19 Sept. 2017] [in Russian].
17. "Industry 4.0": Creation of a Digital Enterprise. The Main Results of the Research in the Metallurgical Industry/ PwC. Retrieved from https://www.pwc.ru/ru/ mining-and-metals/publications/assets/industry-4-metals-key-findings_rus.pdf [Accessed 28 Sept. 2017] [in Russian]
18. Distributed data processing using Hadoop. IBM, 2017 [in Russian].
19. Romanchuk, Ya. Third industrial revolution: essence, influence, consequences. Retrieved from http://liberty-belarus.info/o-kapitalizme/kapitalizm-dlya-lyuboznatelnykh/ item/848-tretya-promyshlennaya-revolyutsiya-sut-vliyanie-posledstviya#l2 [Accessed 25 Aug. 2017] [in Russian].
20. State, Problems and Prospects of the Development of the Information Society in the CIS. (2012).Commonwealth of Inde-pendent States. Executive committee. Moscow [in Russian].
21. Trofimova, E. (2016). Cyber Vulnerability of the Smart Manufacturing. Сontrol Еngineering Rossiya, 1 (61), pp. 34-36 [in Russian].
22. Chekletsov, V. (2015). The Fourth Revolution: Internet of Things. Ekspert, January, pp. 42-44. Retrieved from http://www.ncca.ru/file?Files&141 [Accessed 08 Sept. 2017]. [in Russian].
23. Sheer, A.-V. What’s Behind the Term "Industry 4.0". Retrieved from http://www.i-love-bpm.ru/s%D1%81heer/chto-skryvaetsya-za-terminom-industriya-40 [Accessed 08 Sept. 2017]. [in Russian].
24. Atzori, L., Lera, A., & Morabito, G. (2010). The internet of things: a survey. Computer Networks. Режим доступа: https://www.researchgate.net/profile/Luigi_Atzori2/publication/222571757_The_Internet_of_Things_A_Survey/links/546b36df0cf2f5eb180914e5/The-Internet-of-Things-A-Survey.pdf [Acces¬sed 28 Sept. 2017].
25. Auschitzky, E., Hammer, M., & Rajagopaul, A. How big data can impro-ve manufacturing. McKinsey. Режим доступа: http://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-can-improve-manufacturing [Accessed 28 Sept. 2017].
26. Brynjolfsson, E., & McElheran, K. Data in Action: Data-Driven Decision Making in U.S. Manufacturing. Режим доступа: http://www.economics.cornell.edu/sites/default/files/files/events/Brynjolfsson_McElheran_AEA_2016.pdf [Acces¬sed 25 Aug. 2017].
27. Dymola for physical modelling and simulation using Modelica. Claytex. Режим доступа: http://www.claytex.com/ products/dymola/ [Acces¬sed 28 Sept. 2017].
28. Fishwick, P. (2007). Handbook of Dynamic System Modeling (Chapman & Hall/CRC Computer and Information Sci-ence Series). New York: Chapman and Hall/CRC.
29. Geisberger, E., & Broy, M. (2012). Agenda CPS: Integrierte For schungs agenda Cyber-Physical Systems (acatech STUDIE) (German Edition). New York: Springer-Verlag, 297 p.
30.Gröger, C., Niedermann, F., & Mitschang, B. (2012, July) Data Mining-driven Manufacturing Process Optimization. Proceedings of the World Congress on En-gineering (Vol. III, pp. 1475-1481). London: WCE.
31.Hermann, M., Pentek, T., & Otto, B. (2016). Design Principles for Industrie 4.0 Scenarios. 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928-3937.
32. Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014). Toward Scalable Systems for Big Data Analytics: a Technology Tutorial. IEEE Access, 2. pp. 652-687.
33. Industrie 4.0 – What is it? Germany Trade & Invest. Режим доступа: https://industrie4.0.gtai.de/INDUSTRIE40/ Navigation/EN/Topics/Industrie-40/what-is-it.html [Acces¬sed 08 Sept. 2017].
34. Information Resources Manage-ment Association. The Internet of Things: Breakthroughs in Research and Practice (2017). Information Resources Management Association. Hershey: IGI Global.
35.Innovationen für die Produktion von morgen. Режим доступа: https://www.bmbf.de/pub/Industrie_4.0.pdf [Accessed 08 Sept. 2017].
36. Jeschke, S., Brecher, C., Song, H., & Rawat, D. (2017). Industrial Internet of Things. Cybermanufacturing Systems. Her-ausgeber: Springer International Publishing Switzerland.
37. Kim, K., Jung, J.-K., & Choi, J.-Y. (2016). Impact of the Smart City Industry on the Korean National Economy: Input-Output Analysis. Sustainability, 8 (7). pp. 649-678.
38. Lasi, H., Kemper, H.-G., Fettke, P., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engi-neering, 4 (6), pp. 239-242.
39. Lee, J., Lapira, E., Bagheri, B., & Kao, H. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1. pp. 38-41.
40. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. New York: McKinsey Global Institute.
41. Nedelcu, B. (2013). About Big Data and its Challenges and Benefits in Manufacturing. Database Systems Journal, 4, Issue 3, pp. 10-19.
42. Networked Readiness Index (2015). Режим доступа: http://reports.weforum.org/ global-information-technology-report-2015/ economies/#indexId=NRI&economy=UKR [Accessed 25 Aug. 2017].
43. Nonaka, Y. Suginishi, Y., Lengyel, A., & Katsumura, Y. (2015, August) The S-Model: A digital manufacturing system combined with autonomous statistical analysis and autonomous discrete-event simulation for smart manufacturing. IEEE International Conference on Automation Science and Engineering (CASE) (pp. 1006-1011). Gothenburg: IEEE.
44. Reimann, M., & Ruckriegel, C. (2017). Road2CPS Priorities and Recom-mendations for Research and Innovation in Cyber-Physical Systems. Stuttgart: Steinbeis-Editions.
45. Saraee, M. How can companies start implementing the Smart Industry concept? Режим доступа: https://www.smart-industry.nl/site/assets/files/2158/how_can_ companies_start_implementing_the_smart_ industry_concept.pdf [Acces¬sed 08 Sept. 2017].
46. Smart Enterprise demo for manu-facturing. Pharaos Navigator. Режим до-ступа: https://enterprise.win2biz.com/static/ content/en/525/Explaining-Enterprise-Model. html [Accessed 08 Sept. 2017].
47. The 2016 IMD World Com-petitiveness Scoreboard. Режим доступа: http://www.imd.org/globalassets/wcc/docs/scoreboard-2016.pdf [Acces¬sed 08 Sept. 2017].
48. Yin, S., & Kaynak, O. (2015). Big Data for Modern Industry: Challenges and Trends. Proceedings of the IEEE, 2 Vol. 103, pp. 143-146.
49. Zaitsev, D.A. (2012). Universal Petri Net. Cybernetics and Systems Analysis, Vol. 48, Issue 4, pp. 498-511.
50. Zhou, Z., Xie, S., & Chen, D. (2012). Fundamentals of Digital Manufac-turing Science. London: Springer-Verlag London Limited.
Last Updated (Thursday, 07 December 2017 13:53)
