Improving the efficiency of functional reserve management amidst digital transformation
Makhosheva S.A.1
, Galachieva S.V.2, Kushkhov A.P.3, Illizarov D.L.
1 Институт информатики и проблем регионального управления - филиал ФГБНУ «Федеральный научный центр «Кабардино-Балкарский научный центр Российской академии наук»
2 Северо-Кавказский горно-металлургический институт (государственный технологический университет)
3 Кабардино-Балкарский государственный университет им. Х.М. Бербекова
Download PDF | Downloads: 9
Journal paper
Journal of Central Asia Economy (РИНЦ, ВАК)
опубликовать статью | оформить подписку
Volume 8, Number 4 (October-December 2024)
Indexed in Russian Science Citation Index: https://elibrary.ru/item.asp?id=80548947
Abstract:
Modern industrial companies face the need to optimize resource management in the context of digital transformation and fierce market competition. The article discusses key areas for improving efficiency in the use of functional reserves, including the introduction of digital technologies, predictive analytics, and automated management systems. Special attention is paid to the use of artificial intelligence, digital twins, and inventory management platforms to minimize costs and increase production flexibility. Based on research data and practical cases, a comparative analysis of the effectiveness of various strategic resource management tools is conducted. The results show that the integration of intelligent systems into production and logistics processes can significantly reduce costs and improve forecasting accuracy. The article also discusses promising areas for further development of inventory management strategies to adapt to changing market conditions. The findings and recommendations can be used by companies to develop more effective resource management strategies.
Keywords: functional reserves, digital transformation, predictive analytics, automated management system, digital twin, resource optimization
JEL-classification: L80, O14, P23
References:
Ansoff I. (2009). Strategicheskoe upravlenie [Strategic management] M.: Ekonomika. (in Russian).
Bratarchuk T.V., Gladyshev A.G., Lukichev K.E., Danilkevich M.A., Komov V.E. (2024). Razrabotka i vnedrenie tsifrovyh dvoynikov dlya optimizatsii i ustoychivogo razvitiya ugolnoy promyshlennosti Rossii [Development and implementation of digital twins for optimization and sustainable development of the coal industry in Russia]. Ugol. (11(1186)). 108-116. (in Russian). doi: 10.18796/0041-5790-2024-11-108-116.
Dynamics 365 Supply Chain Management OverviewMicrosoft. Retrieved February 26, 2025, from https://dynamics.microsoft.com/en-us/supply-chain-management/
Gurtuev A., Derkach E., Makhosheva S., Ivanov Z. (2020). Bayesian approach to investment in innovation projects with the presence of fake innovators Heliyon. 6 (11). e05603. doi: 10.1016/j.heliyon.2020.e05603.
Industry 4.0 and Smart ManufacturingSiemens. Retrieved February 26, 2025, from https://www.siemens.com/global/en/industries/digital-enterprise.html
Ivanov G.N., Polotskiy Yu.I. (2011). Protsessnyy podkhod v upravlenii kachestvom [The process approach in quality management] (in Russian).
Johnson C., Lee H. (2022). Supply Chain 4.0: The Role of AI and Predictive Analytics Hoboken, New Jersey: Wiley.
Johnson K., Smith M. (2022). Advanced Simulation Strategies New York: Springer.
King U., Klipland D. (2012). Strategicheskoe planirovanie i khozyaystvennaya politika [Strategic planning and economic policy] M.: YuNITI. (in Russian).
Lamben Zh.-Zh. (2012). Strategicheskiy marketing [Strategic marketing] SPb.: Piter. (in Russian).
Lee I. (2023). Digital Supply Chain Transformation: A Framework for the Future United Kingdom: Routledge.
Lee J., Zhang T. (2023). Urban Planning and Digital Twins Cambridge: Cambridge University Press.
Oracle Demand Management Cloud: AI-Powered Supply Chain OptimizationOracle. Retrieved February 25, 2025, from https://www.oracle.com/supply-chain/demand-management/
Planning Analytics: AI-Driven Forecasting and Decision MakingIbm. Retrieved February 25, 2025, from https://www.ibm.com/planning-analytics
SAP Integrated Business Planning for Supply ChainSap. Retrieved February 24, 2025, from https://www.sap.com/products/scm/ibp.html
Simcenter: Digital Twins for Industrial Process OptimizationSiemens. Retrieved February 25, 2025, from https://www.plm.automation.siemens.com/global/en/products/simcenter/
The Role of Digital Twins in Industrial OptimizationGeneral Electric. Retrieved February 26, 2025, from https://www.ge.com/digital/applications/digital-twin
Top Supply Chain Technology Trends for 2024Gartner. Retrieved February 26, 2025, from https://www.gartner.com/en/insights/supply-chain
Страница обновлена: 11.04.2025 в 03:48:18