2020, Vol. 7, No. 1. - go to content...

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DOI: 10.15862/17INOR120 (https://doi.org/10.15862/17INOR120)

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Guseva E.N., Chusavitina G.N., Povitukhin S.A. Using simulation methods to manage the business continuity of a leasing company. Russian journal of resources, conservation and recycling. 2020; 7(1). Available at: https://resources.today/PDF/17INOR120.pdf (in Russian). DOI: 10.15862/17INOR120

Using simulation methods to manage the business continuity of a leasing company

Guseva Elena Nikolaevna
Nosov Magnitogorsk state technical university, Magnitogorsk, Russia
E-mail: kellymy7@rambler.ru

Chusavitina Galina Nikolaevna
Nosov Magnitogorsk state technical university, Magnitogorsk, Russia
E-mail: gala_m27@mail.ru

Povitukhin Sergey Alekseevich
Moscow state university of technologies and management named after K.G. Razumovsky (First Cossack university)
Moscow, Russia
E-mail: serge2410@yandex.ru

Abstract. In the context of digitalization of all areas of modern organizations, an important role is played by ensuring the continuity of business processes and crisis management. The article discusses the application of the concept of discrete event modeling to study the continuity of business processes of an auto-leasing enterprise, the characteristics of which change randomly. The built simulation model of critical business processes of an auto-leasing organization is described. Simulation experiments using various sets of destructive factors in combination with measures that reduce the vulnerability of the system and prevent threats to the continuity of business processes are described based on these experiments, key indicators of business process efficiency are determined, and critical infrastructure elements are indicated. Simulation parameters are analyzed based on data from simulation experiment reports obtained in the Arena program of Rockwell Software.

Keywords: leasing; information technology; business continuity; business continuity management; modeling; simulation model; Arena simulation environment; business continuity risk modeling

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