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Authors

Maxim Korzin

Abstract

This article presents a comprehensive mathematical model describing the process of implementing innovative technologies in the shipbuilding industry. While diffusion models are widely studied, there is a lack of deterministic dynamic models that integrate financial, technical, and human capital factors specifically for capital-intensive industries like shipbuilding. The model focuses on the key parameters determining the speed and success of technology diffusion: economic efficiency, investment level, adaptation costs, and personnel qualifications. Based on the apparatus of differential equations and methods of multi-criteria optimization, a system has been constructed that allows for a quantitative assessment of the impact of control actions (e.g., the volume of state subsidies or the intensity of retraining programs) on the pace of technological modernization of a shipbuilding enterprise. The stability of the model is analyzed, and critical conditions under which the implementation becomes self-sustaining are determined. The main contributions include: the derivation of an analytical critical success condition; a numerical demonstration of scenarios leading to success, stagnation, or failure; practical recommendations for structuring investments. The results can be used to formulate technological development strategies for shipbuilding holdings and to substantiate state support programs for the industry.

Keywords:
mathematical modeling, system dynamics, shipbuilding, diffusion of innovations, technological modernization, optimal control, investment efficiency, human capital

Article Details

References

[1]Rogers, E.M. Diffusion of Innovations, 5th ed.; Free Press: New York, NY, USA, 2003; 576p.

[2]Bass, F.M. A New Product Growth for Model Consumer Durables. Management Science 1969, 15(5), 215–227.

[3]International Maritime Organization (IMO). Fourth IMO Greenhouse Gas Study 2020; IMO Publishing: London, UK, 2021. Available online: https://www.imo.org/en/OurWork/Environment/Pages/Fourth-IMO-Greenhouse-Gas-Study-2020.aspx (accessed on 23/11/2025).

[4]Dubrovskiy, A.V.; Lavrov, A.S. Mathematical Models for Managing Innovation Projects in High-Tech Industries. Herald of the Bauman Moscow State Technical University, Series “Instrument Engineering” 2018, 6, 120–135. (In Russian)

[5]Kuznetsov, Yu.A.; Petrov, A.M. System Analysis and Modeling of Industrial Enterprise Modernization Processes; Politekhnika Publishing: St. Petersburg, Russia, 2019; 288p. (In Russian)

[6]Sterman, J.D. Business Dynamics: Systems Thinking and Modeling for a Complex World; Irwin McGraw-Hill: Boston, MA, USA, 2000; 982p.

[7]Solow, R.M. Technical Change and the Aggregate Production Function. The Review of Economics and Statistics 1957, 39(3), 312–320.

[8]Pontryagin, L.S.; Boltyanskii, V.G.; Gamkrelidze, R.V.; Mishchenko, E.F. The Mathematical Theory of Optimal Processes; John Wiley & Sons: New York, NY, USA, 1962; 360p.

[9]Smirnov, I.P.; Kozlov, D.Yu. Methods for Assessing the Economic Efficiency of Investments in Digitalization of Shipbuilding Enterprises. Economics and Management in Mechanical Engineering 2021, 63(4), 45–52. (In Russian)

[10]Federal Target Program “Development of Shipbuilding and Equipment for the Development of Offshore Fields for 2021-2035”. Ministry of Industry and Trade of the Russian Federation: Moscow, Russia, 2020. (In Russian)

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