Application of digital twin technology in commercial networks

The competitive advantage of digital commerce entities is based on real-time market analysis and predictive modeling using mathematical algorithms and basic indicators such as demand, supply, costs and risks. The concept of digitalization is based on the dominance of digital ecosystems and on the widespread introduction of artificial intelligence systems based on computer programs that implement such algorithms. Serious competition in the field of trade has led to the process of consolidating the business into a retail network. In addition, managers’ responsibility for the quality of management decisions is growing. From an economist’s point of view, a digital double doesn’t make sense without a mathematical model. With the usual digitization of data processing, the digitization effect is limited to the usual delayed reporting. Only a mathematically sound result using the theory of optimal solutions will allow management to achieve the greatest benefits based on a set of economic criteria. This is due to the fact that the transition to a system of leading economic indicators is possible only through the use of scientifically based predictive mathematical models. The results obtained can be incorporated into the algorithmic basis of the digital platforms of the trading network ecosystem. In the presented work, several options for planning commercial activities are considered and algorithms are obtained that are convenient for implementation on servers for managing the activities of a retail network. The aim of the research is to develop a mathematical model focused on its use in managing the activities of a retail network in combination with information cyberspace. In the course of the study, stochastic modeling and dynamic programming methods were used, as well as mathematical optimization methods. Economic indicators were selected as the performance criteria.

Keywords: digital twin; intelligent supply chains; digital logistics; retail network management; algorithmization

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Authors