Model for assessing the digital maturity of an industrial enterprise

Aim. The work aimed to develop a quantitative model for assessing the digital maturity of an industrial enterprise.
Tasks. The work was performed to define the concept of digital maturity of an industrial enterprise; systematize existing approaches to assessing the digital maturity of an industrial enterprise; conduct a critical analysis of existing approaches in terms of taking into account the requirements of the TOGAF corporate architecture levels, the requirements for the reliability of the assessment results; and propose a quantitative model for assessing the digital maturity of an industrial enterprise.
Methods. The research tools used in this article include methods of system analysis and synthesis, methods of economic analysis for systematization of existing approaches to assessing the degree of digital maturity of an industrial enterprise, as well as methods of mathematical statistics, econometric analysis for developing a multivariate regression model for assessing the digital maturity of an industrial enterprise taking into account the requirements of the process approach.
Results. A classification of models for assessing digital maturity is proposed, which is used to highlight the main approaches to the latter and consider the fields of enterprise activity to assess the level of its digital maturity, assessment scales, methods used in the assessment, and the scope of the models application, as well as the applicability of such models to a comprehensive assessment by levels of the enterprise architecture. A multifactorial regression model for assessing the digital maturity of an enterprise has been developed, which enables to assess the degree of influence of each direction of this assessment on the digital maturity of an enterprise and give recommendations for managing digital maturity, which can be used in the formation of an enterprise digital transformation strategy.
Conclusions. The systematization of approaches to assessing the digital maturity of industrial enterprises revealed that the models currently used are mainly based on expert assessments, which leads to subjectivity. These models use nominal and ordinal scales, which makes it difficult to apply the apparatus of econometric analysis. The models described do not take into account the ratio of variables that determine the digital maturity of an enterprise and the levels of its architecture according to the TOGAF method, which results in a “patchwork” nature of considering the business processes of an industrial enterprise and, as a result, the lack of a comprehensive assessment of digital maturity. According to the authors, the incompleteness and subjectivity of the existing evaluation models, as well as the need for their modernization, are obvious. The authors propose a model for assessing the digital maturity of an industrial enterprise, which enables to solve the above problems. The coefficient of determination of the constructed model (R² = 0.845) indicates that the share of the total scatter relative to the sample average of the integral indicator of digital maturity assessment is 84.5% explained by the constructed regression model. The multiple correlation coefficient (Multiple R = 0.919) indicates the strength of the relationship between the resulting and independent variables. The value of the Fisher criterion (F = 102) indicates the high significance of the constructed multivariate regression model. According to the calculation results, the significant standardized coefficients of the regression equation include Х₁ beta, Х₃ beta, Х₄ beta, Х₅ beta, therefore we can conclude that the variation of Х i has the strongest influence on the variation of the resulting attribute Y, when abstracted from the concomitant influence of variations of other factors included in the equation regression.

1. The secrets to transformation success. McKinsey. URL: https://www.mckinsey.com/featured-insights/Mckinsey-live/webinars/the-secrets-to-transformation-success (accessed on 20.03.2022).

2. Ashkenas R. Change management needs to change. Harvard Business Review. Apr. 16, 2013. URL: https://hbr.org/2013/04/change-management-needs-to-cha (accessed on 20.03.2022).

3. Model for evaluating the digital maturity of the CDTO preparation center. CDTOwiki: Digital transformation knowledge and terms. CDTOwiki. URL: https://cdto.wiki (accessed on 20.03.2022). (In Russ.).

4. Brusakova I.A. Information management in the context of digital transformations. In: System analysis in design and management. Proc. 24th Int. sci. and educ.-pract. conf. St. Petersburg: Politekh-Press; 2020:115-122. (In Russ.). DOI: 10.18720/SPBPU/2/id20-203

5. Digital maturity model: Achieving digital maturity to drive growth. London: Deloitte; 2018. 24 p. URL: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Technology-Media-Telecommunications/deloitte-digital-maturity-model.pdf (accessed on 20.03.2022).

6. The digital maturity model: How digitizing information helps you achieve your goals. Kodak Alaris. URL: https://www.alarisworld.com/en-gb/insights/articles/digital-maturity-model#section%201 (accessed on 20.03.2022).

7. Geissbauer R., Vedso J., Schrauf S. Industry 4.0: Building the digital enterprise. London: PWC; 2016. 36 p. URL: https://www.pwc.com/gx/en/industries/industries-4.0/landingpage/industry-4.0-building-your-digital-enterprise-april-2016.pdf (accessed on 20.03.2022).

8. Leyh С., Bley K., Schäffer T., Bay L. The application of the maturity model SIMMI 4.0 in selected enterprises. In: Proc. 23rd Americas conf. on information systems (AMCIS 2017). (Boston, MA, Aug. 10-12, 2017). Atlanta, GA: Association for Information Systems; 2017: 1-10. URL: https://www.researchgate.net/publication/317505970_The_Application_of_the_Maturity_Model_SIMMI_40_in_Selected_Enterprises (accessed on 20.03.2022).

9. Varnavskiy V., Volkova E.S., Buryakova A.O., Klimova E.A. Model for assessing digital maturity of organizations. Vestnik Universiteta (Gosudarstvennyi universitet upravleniya). 2020;(4):23-32. (In Russ.). DOI: 10.26425/1816-4277-2020-4-23-32

10. Ramos L.F.P., Loures E.F.R., Deschamps F. An analysis of maturity models and current state assessment of organizations for Industry 4.0 implementation. Procedia Manufacturing. 2020;51:1098-1105. DOI: 10.1016/j.promfg.2020.10.154

11. Marchuk A.A. Accounting for qualitative parameters in regression analysis. Russian Assessment Service. URL http://www.rusvs.ru/data/files/articles/dummyvar.pdf (accessed on 20.03.2022). (In Russ.).

12. Borovikov V.P. Popular introduction to Statistica. Moscow: Alpina Business Books; 2003. 321 p. (In Russ.).

13. Borovikov V.P., Ivchenko G.I. Forecasting in the Statistica system in the Windows environment: Fundamentals of theory and intensive practice on the computer. St. Petersburg: Finansy i statistika; 2000. 384 p. (In Russ.).