Transformation of industrial business models under the influence of digital twins and autonomous systems and its implications for the structure of added value and income distribution

Abstract

The article is devoted to the analysis of the transformation of industrial business models under the influence of digital twins and autonomous systems and to the assessment of the consequences of this transformation for the structure of added value and the distribution of income between labor and capital. The aim of the study is to quantitatively assess the impact of the implementation of digital twins on operational efficiency, cost structure, risk profile, and intra-corporate income inequality, as well as to compare the efficiency of point-based, systemic, and ecosystem digitalization projects. The empirical base consists of the financial and operational reporting of the 500 largest industrial companies in the world for 2018–2024, as well as data from analytical agencies and consulting reports; Difference-in-Differences, multifactor regression, value chain analysis based on the Input-Output approach, clustering of digitalization strategies, and Monte Carlo simulation modeling methods are applied. It is shown that a high level of digital twin integration is accompanied by more than a twofold increase in revenue per employee, a noticeable improvement in gross and operating margins, a reduction in deviations of actual costs from planned ones, and an acceleration in inventory turnover, while simultaneously increasing R&D and IT capital expenditures. A structural transformation of added value has been established: the share of material production decreases, while software and service-analytical functions become the main sources of income, forming more stable, recurrent revenue streams. At the same time, the share of labor in revenue decreases while the share of capital grows, internal wage differentiation and polarization between high- and low-skilled workers intensify, which is reflected in the growth of the Gini coefficient and the wage polarization index. Ex-post analysis of investment projects shows that ecosystem solutions based on digital twins provide a multiple increase in NPV and IRR and a shorter payback period compared to point automation, but require a significantly higher entry threshold, which strengthens market concentration around technological leaders. It is concluded that the spread of digital twins leads to the dematerialization of value sources, the enhancement of the role of intangible and data-oriented assets, and necessitates the adaptation of corporate reporting, investment models, and social policy to mitigate the risks of increasing inequality and monopolization.