Abstract
The increasing complexity of modern production processes necessitates the use of artificial intelligence technologies that use formalized knowledge to develop effective management tools. It is proposed to use the well-known Balance Score Card System to describe production processes based on the concepts of risks. The key performance indicators of the Balanced Scorecard are organized in a tree graph. The root of the tree graph is an event that causes a security violation. The rest of the graph vertices are unfavourable events. The branches of the tree describe the causal mechanisms between these events. A mathematical model is proposed to describe action and reaction in the form of a matrix. Its cells are functions that describe interactions at the nodes of the tree. If the result of the interaction contributes to the occurrence of an event that should be considered as unfavourable, then the sign of the event is positive. If the interaction led to the elimination of the adverse event, then the event result is zero. It is assumed that interactions can backfire and facilitate processes to prevent adverse events. The probability of realization of each combination depends on the result of interactions at the vertices of the graph, which are described by status functions. Status functions can be viewed as an extended analogy to probabilities. To develop a mathematical model of the dynamics of production processes, improve the quality and safety of food products, a system of linear differential equations of Kolmogorov-Chapman is used. The described results are obtained by solving linear differential equations. They allow you to develop scenarios and predict the dynamics of production processes when solving automation problems to improve the quality and safety of the food industry.
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This research was partially supported by the Russian Fund of Basic Research (grant No. 20-010-00465).
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Veshneva, I., Bolshakov, A., Fedorova, A. (2021). Analysis of the Competitiveness Risks of Food Production Enterprises Using Mathematical Modelling Methods. In: Kravets, A.G., Shcherbakov, M., Parygin, D., Groumpos, P.P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2021. Communications in Computer and Information Science, vol 1448. Springer, Cham. https://doi.org/10.1007/978-3-030-87034-8_18
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