Subject. This article examines the methodological aspects of developing management decisions regarding personnel remuneration in the context of business environment uncertainty. Objectives. The article aims to develop methodological principles for managing the employee remuneration system using a scenario-based approach. Methods. For the study, I used scenario analysis tools. Results. Based on the study's findings, the article presents proposals for implementing a scenario-based approach to managing the employee remuneration system and determines projected values for the motor vehicle production index, the unemployment rate, and the average wage growth rate in the automotive industry. The article also proposes specific measures regarding personnel remuneration for the baseline, optimistic, and pessimistic scenarios of the automotive industry's development. Conclusions. Applying a scenario-based approach to managing the staff remuneration system helps account for the impact of external business environment factors. In contrast to the traditional forecasting approach based on expert assessments, the scenario-based approach employed in this study is notable for its greater degree of formalization, relying on quantitatively measurable indicative indicators of industry development. This helps improve the flexibility and adaptability of the reward system to ongoing changes.
Keywords: reward, scenario analysis, scenario forecasting, automotive industry
References:
Maksyutina E.V., Golovkin A.V. [Russian automobile industry: Development problems in the "crumbling world"]. Regional'nyi ekonomicheskii zhurnal, 2020, no. 1, pp. 71–83. (In Russ.) EDN: XUFTFR
Feofanov K.A. [Scenario approach to forecasting]. Sotsiologicheskie issledovaniya, 2008, no. 5, pp. 67–73. (In Russ.) EDN: IQFTPL
Simachev Yu.V., Kuzyk M.G., Butov A.M. [The Russian auto industry: Key features, the effect of coronacrisis, prospects]. Vserossiiskii ekonomicheskii zhurnal, 2021, no. 8, pp. 8–37. (In Russ.) DOI: 10.30680/ECO0131-7652-2021-8-8-37
Zagidullin A.R. [Assessment of the level of economic security of automotive enterprises]. Progressivnaya ekonomika, 2025, no. 5, pp. 237–250. (In Russ.) DOI: 10.54861/27131211_2025_5_237 EDN: TLPSET
Kapelyushnikov R.I. [The anatomy of the corona crisis through the lens of the labor market adjustment]. Voprosy Ekonomiki, 2022, no. 2, pp. 33–68. (In Russ.) DOI: 10.32609/0042-8736-2022-2-33-68 EDN: ZAPVNU
Roshchin S.Yu., Sidorova A.S., Solntsev S.A. [Wage structure and HR management in Russian companies]. Rossiiskii zhurnal menedzhmenta, 2018, vol. 16, no. 4, pp. 481–498. (In Russ.) DOI: 10.21638/spbu18.2018.401
Vakulenko E.S., Gurvich E.T. [Real wage flexibility in Russia: Comparative analysis]. Zhurnal Novoi ekonomicheskoi assotsiatsii, 2016, no. 3, pp. 67–92. (In Russ.) DOI: 10.31737/2221-2264-2016-31-3-3 EDN: WRKYXD
Milyakin S.R., Skubachevskaya N.D., Valeev S.V. [The automotive market in the new conditions: Current state and outlook]. Problemy prognozirovaniya, 2024, no. 2, pp. 138–151. (In Russ.) DOI: 10.47711/0868-6351-203-138-151 EDN: CHBJPD
P'yankova S.G., Ergunova O.T. [Human resources basic competences transformation in the age of digitization]. Ars Administrandi (Iskusstvo upravleniya), 2025, vol. 17, no. 2, pp. 267–294. (In Russ.) DOI: 10.17072/2218-9173-2025-2-267-294 EDN: LMNRIE
Platonova A.I., Popov V.S. [Comparison of accuracy of time series forecasting models: ARIMA, Prophet, LSTM and GRU]. Sovremennye innovatsii, sistemy i tekhnologii, 2025, vol. 5, no. 2, pp. 3061–3070. (In Russ.) DOI: 10.47813/2782-2818-2025-5-2-3061-3070 EDN: LDSMFM
Tsypin A.P. [Studying the dynamics of per capita income using statistical methods: Time series analysis]. Analiz. Audit, 2025, vol. 12, no. 1, pp. 46–61. (In Russ.) DOI: 10.26794/2408-9303-2025-12-1-46-61 EDN: ZSSEDQ