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Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks

Author

Listed:
  • Aleksey I. Shinkevich

    (Logistics and Management Department, Kazan National Research Technological University, 420015 Kazan, Russia)

  • Irina G. Ershova

    (Department of Finance and Credit, Southwest State University, 305040 Kursk, Russia)

  • Farida F. Galimulina

    (Logistics and Management Department, Kazan National Research Technological University, 420015 Kazan, Russia)

Abstract

Approaches presented today in the scientific literature suggest that there are no methodological solutions based on the training of artificial neural networks to predict the direction of industrial development, taking into account a set of factors—innovation, environmental friendliness, modernization and production growth. The aim of the study is to develop a predictive model of performance management of innovative industrial systems by building neural networks. The research methods were correlation analysis, training of neural networks (species—regression), extrapolation, and exponential smoothing. As a result of the research, the estimation efficiency technique of an innovative industrial system in a complex considering the criteria of technical modernization, development, innovative activity, and ecologization is developed; the prognostic neural network models allow to optimize the contribution of signs to the formation of target (set) values of indicators of efficiency for macro and micro-industrial systems that will allow to level a growth trajectory of industrial systems; the priority directions of their development are offered. The following conclusions: the efficiency of industrial systems is determined by the volume of sales of goods, innovative products and waste recycling, which allows to save resources; the results of forecasting depend significantly on the DataSet formulated. Although multilayer neural networks independently select important features, it is advisable to conduct a correlation analysis beforehand, which will provide a higher probability of building a high-quality predictive model. The novelty of the research lies in the development and testing of a unique methodology to assess the effectiveness of industrial systems: it is based on a multidimensional system approach (takes into account factors of innovation, environmental friendliness, modernization and production growth); it combines a number of methodological tools (correlation, ranking and weighting); it expands the method of effectiveness assessment in terms of the composition of variables (previously presented approaches are limited to the aspects considered).

Suggested Citation

  • Aleksey I. Shinkevich & Irina G. Ershova & Farida F. Galimulina, 2022. "Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks," Mathematics, MDPI, vol. 11(1), pages 1-25, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:164-:d:1018301
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    References listed on IDEAS

    as
    1. Daniel Ramos & Pedro Faria & Zita Vale & João Mourinho & Regina Correia, 2020. "Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning," Energies, MDPI, vol. 13(18), pages 1-18, September.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Cooke, Philip & Gomez Uranga, Mikel & Etxebarria, Goio, 1997. "Regional innovation systems: Institutional and organisational dimensions," Research Policy, Elsevier, vol. 26(4-5), pages 475-491, December.
    4. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    5. Alexey I. Shinkevich & Tatiana V. Malysheva & Yulia V. Vertakova & Vladimir A. Plotnikov, 2021. "Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
    6. Shouheng Tuo & Tianrui Chen & Hong He & Zengyu Feng & Yanling Zhu & Fan Liu & Chao Li, 2021. "A Regional Industrial Economic Forecasting Model Based on a Deep Convolutional Neural Network and Big Data," Sustainability, MDPI, vol. 13(22), pages 1-11, November.
    7. Chen Wenjing & Man Fai Leung, 2022. "BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, August.
    8. Desamparados Blazquez & Josep Domenech & Jose-Maria Garcia-Alvarez-Coque, 2018. "Assessing Technology Platforms for Sustainability with Web Data Mining Techniques," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    9. Feng-Jyh Lin & Yi-Hsin Lin, 2012. "The determinants of successful R&D consortia: government strategy for the servitization of manufacturing," Service Business, Springer;Pan-Pacific Business Association, vol. 6(4), pages 489-502, December.
    10. Wee Chin Wong & Ewan Chee & Jiali Li & Xiaonan Wang, 2018. "Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing," Mathematics, MDPI, vol. 6(11), pages 1-20, November.
    11. Tatiana V. Malysheva & Alexey I. Shinkevich & Guzyal M. Kharisova & Yuliya V. Nuretdinova & Oleg R. Khasyanov & Ildar G. Nuretdinov & Natalia A. Zaitseva & Svetlana S. Kudryavtseva, 2016. "The Sustainable Development of Competitive Enterprises through the Implementation of Innovative Development Strategy," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 185-191.
    12. Aleksey I. Shinkevich & Irina G. Ershova & Farida F. Galimulina & Alla A. Yarlychenko, 2021. "Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction," Mathematics, MDPI, vol. 9(23), pages 1-18, November.
    13. Asheim, Bjorn T & Isaksen, Arne, 2002. "Regional Innovation Systems: The Integration of Local 'Sticky' and Global 'Ubiquitous' Knowledge," The Journal of Technology Transfer, Springer, vol. 27(1), pages 77-86, January.
    14. Palm, Alvar, 2022. "Innovation systems for technology diffusion: An analytical framework and two case studies," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    15. Daitian Li & Zheng Liang & Fredrik Tell & Lan Xue, 2021. "Sectoral systems of innovation in the era of the fourth industrial revolution: an introduction to the special section [The magnitude of innovation by demand in a sectoral system: the role of indust," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(1), pages 123-135.
    16. Bergek, Anna & Jacobsson, Staffan & Carlsson, Bo & Lindmark, Sven & Rickne, Annika, 2008. "Analyzing the functional dynamics of technological innovation systems: A scheme of analysis," Research Policy, Elsevier, vol. 37(3), pages 407-429, April.
    17. Kao Ge & Jian-Qiang Zhao & Yan-Yong Zhao, 2022. "GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm," Mathematics, MDPI, vol. 10(7), pages 1-13, April.
    18. Yuanyuan Luo & Da Ren, 2021. "Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitmen," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
    19. Ortt, J. Roland & Kamp, Linda M., 2022. "A technological innovation system framework to formulate niche introduction strategies for companies prior to large-scale diffusion," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
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