• International Journal of Technology (IJTech)
  • Vol 12, No 7 (2021)

Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

Title: Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation
Aleksandr Babkin, Vladimir Glukhov, Elena Shkarupeta, Natalija Kharitonova , Hanon Barabaner

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Cite this article as:
Babkin, A., Glukhov, V., Shkarupeta, E., Kharitonova , N., Barabaner, H., 2021. Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation. International Journal of Technology. Volume 12(7), pp. 1397-1406

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Aleksandr Babkin Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaia Street, 29, Saint Petersburg, 195251, Russia
Vladimir Glukhov Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaia Street, 29, Saint Petersburg, 195251, Russia
Elena Shkarupeta Voronezh State Technical University, 20-letiia Oktiabria Street, 84, Voronezh, 394071, Russia
Natalija Kharitonova Financial University under the Government of the Russian Federation, Leningradskiy Avenue, 49, Moscow, 125167, Russia
Hanon Barabaner Academic Society of Estonia, 10412 Tallinn, Estonia
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Abstract
Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

The essence of the transformation processes currently taking place in all sectors of the economy, including industry, is increasingly described by the ecosystem approach. Industrial ecosystems have proven to be the models with the highest production efficiency. The effective management of industrial ecosystems is possible only if the mechanisms underlying their dynamics are understood. Achieving a high level of maturity for industrial ecosystems in the framework of digital technology implementation is of particular relevance, and this research aims to develop and test a methodology for assessing the maturity level of such industrial ecosystems by relying on principal component analysis and hierarchical agglomerative clustering. A mature industrial ecosystem gets rapidly integrated into the new technological paradigm and global value chains, is able to compete in global markets in the long term, and has increased potential for industrialization and digital transformation. The proposed methodology is based on the environmental, social, and corporate governance (ESG) methodology for determining the levels of interests in the area of sustainable development of an industrial ecosystem. The proposed approach singles out a fourth assessment projection in addition to ESG, namely the digital maturity. All four maturity assessment projections are proven to be positively and significantly correlated. An industrial ecosystem maturity assessment scale has been developed, including six levels (very high; advanced; basic; elementary; zero; and minus one). The methodology has been tested on the national industrial ecosystem and the metallurgy and mining industrial ecosystem of Russia. The results show that the maturity level of the Russian industrial ecosystem as of mid-2021 is "basic" with the prospect of transition to "advanced." The Russian metallurgy and mining industrial ecosystem maturity level is “advanced.” The key directions for increasing the maturity levels of Russian industrial ecosystems based on innovations in various industrial aspects are proposed.

Digital maturity; Digital transformation; Ecosystem; Industrial ecosystem; Maturity assessment methodology

Introduction

    Global transformations affecting all industries and activities are now becoming evident, manifesting themselves in the intensification of complex and volatile processes. This state of external and internal environments is characterized by a whole set of economic, production, social, technological, digital, ecological, and other transformations (Held and McGrew, 2000; Berawi, 2020; Rudskaya et al., 2020; Kusrini et al., 2020). The observed global transformations are largely due to the intensive implementation of digital technologies, fundamentally changing both the quality of life and the system of socioeconomic relations (Fukuda, 2020; Shkarupeta et al., 2020; Shkarupeta et al., 2021). In the near future, Russia is expected to see more active use of the "digital twin" technology and explosive growth in the metallurgy and mining industry (PwC, 2020)

    The object of this research is an industrial ecosystem as a complex system of economic agents acting on the basis of autonomy and interconnectedness that are distinguished by their activities and features of functioning, whose goal is the creation of industrial products and/or services that are based on the principle of emergence. The research objectives are as follows: to systematize the existing research on assessing the maturity level of industrial ecosystems; to develop and test a methodology for assessing the maturity level of an industrial ecosystem; to identify the main directions of increasing the maturity level of an industrial ecosystem in the framework of the implementation of digital technologies.

Conclusion

    Upon identifying the problem of achieving maturity by Russian industrial ecosystems, a methodology for assessing their maturity has been developed as a continuation of the existing research in this area. The proposed methodology is distinguished by considering the implementation of digital technologies. The methodology has been tested on the Russian industrial ecosystem as well as on the Russian metallurgy and mining industrial ecosystem. The results showed that the national industrial ecosystem is at a basic level, whereas the metallurgy and mining industrial ecosystem is at an advanced level of maturity. The main directions for increasing the maturity level of industrial ecosystems have been systematized. Further research should focus on updating the maturity assessment of the industrial ecosystem for other subindustries and on developing an optimization model, including the one based on machine learning algorithms.

Acknowledgement

    The research was carried out within the framework of the project No. 20-010-00942A of the Russian Foundation for Basic Research.

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