Journal article

Resource Planning For Risk Diversification In The Formation Of A Digital Twin Enterprise

digital twin
resource planning
stochastic uncertainty
risk diversification
investment portfolio
digital twin prototypes
digital twin instances
digital twin life cycle

Recently, there has been explosive growth in the development of the digital industry concept. One of the most important elements of this concept is the application of mathematical modeling methods and data mining to create models of production processes and final products, with the aim of making decisions under stochastic uncertainty. In this paper, a method of diversifying the investment portfolio in the formation of a digital twin is proposed, which is aimed at reducing the total risk by distributing existing assets (resources, investments, etc.) between the analog and digital enterprises. Three types of scenario are proposed: pessimistic - when an enterprise - a pessimist - does not show risk exposure under this scenario when forming a digital counterpart; neutral - when an enterprise shows a neutral attitude to risk (indifference); optimistic - enterprise - optimist is exposed to risk, in particular the risk of untapped opportunities. The result is a set of optimal portfolios, each reflecting a particular position of the enterprise on the risks of untapped opportunities and a certain scenario of forming a portfolio structure, taking into account the relationship between the individual components of the risks of untapped opportunities. Highlighted the advantages of technology "digital twins" for business. Digital counterparts use the data obtained from sensors installed on production lines or on the basis of the final product to predict equipment malfunctions, optimize product quality and reduce the negative impact of production processes on the environment.

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Особливості Управління Взаємовідносинами Стейкхолдерів Та Підприємств В Умовах Сталого Інноваційного Розвитку

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Проблеми Розвитку Наукового Потенціалу Молодих Вчених

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Authors: Anastasiia Liezina, Kateryna Andriushchenko, Oksana Kuchai, Helena Petukhova

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