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Selection of an Inventory Management Strategy in a Low-Demand Environment
P. 347–356.
The research into various sources showed that, despite the results achieved, a low-demand environment and short time series are currently often neglected. To improve the reliability and validity of forecast estimates based on a short time series with low demand, it is necessary to create calculation models using all available quantitative and qualitative information. In this paper, we propose an algorithm that includes the systematisation of statistical data in the form of a time series, statistical and analytical models, expert evaluation of forecast consistency, analysis of the results in order to form versions of a combined model for assessing the predicted parameters of stock consumption, making decisions on choosing one of the inventory management strategies for a short time series with low demand, and the proposed approach is tested. Further research of low demand should include a number of directions, in particular, the development of combined forecasting methods, which include, in addition to quantitative and qualitative methods, the application of decision-making methods.
Сергеев И. В., Domnina S., Elyashevich I., В кн.: Логистика быстрого реагирования в цепях поставок.: М., СПб., Краснодар: Лань, 2026. Гл. 3.3 С. 155–296.
В учебнике системно изложены теоретические, методические и практические аспекты логистики быстрого реагирования (ЛБР) в современной экономике. Показано, что принятие быстрых и гибких решений в логистике и управлении цепями поставок (УЦП) дает возможность бизнесу оперативно реагировать на все возрастающие риски в турбулентной экономической среде. Рассмотрены тенденции развития логистики и УЦП в цифровой экономике в разрезе использования ...
Added: July 18, 2026
Karpukhin I., Savchenko A., , in: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI-26: AAAI Technical Track on Planning, Routing, and Scheduling; AAAI Technical Track on Reasoning under Uncertainty; AAAI Technical Track on Search and Optimization. Main Track, volume 40 no. 43.: American Association for Artificial Intelligence (AAAI) Press, 2026. P. 22536–22544.
Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on a horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of ...
Added: June 17, 2026
Switzerland: Springer Publishing Company, 2026.
In this volume of “Lecture Notes in Networks and Systems” we are pleased to present the proceedings of the 25th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication” (RelStat-2025), which took place in hybrid form in Riga, Latvia on October 16–18, 2025. This event belongs to a con ference series started in ...
Added: May 25, 2026
Karacharovskiy V., Ларина У. С., Резмерица А. А., Социологические исследования 2026 № 7 С. 60–75
Based on the neural network approach, the decomposition of the index of social mood in Russia for the period 2004-2024 was carried out and their dynamics for this period was modeled. The following issues are discussed: (a) the nature of the social mood' shocks in Russia, based on their connection with the concepts of politics ...
Added: May 14, 2026
Ankudinov I., Социология: методология, методы, математическое моделирование 2025 № 61 С. 165–203
The changing political mood of Russians is a constant subject of interest for sociological agencies. With the development of the Internet, conventional questionnaire research began to be supplemented by online surveys and, despite some skepticism, by social media mining. This article attempts to adjust an accidental web-sample so as to bring its estimates closer to ...
Added: April 22, 2026
Nartova-Bochaver S. K., Stakina Y., Тренина М. Е. et al., Клиническая и специальная психология 2026 Т. 15 № 1 С. 166–181
Context and relevance. Eco-anxiety is the anxiety arising in connection with real and possible natural changes and disasters. Eco-anxiety is a significant destabilizer of human activity and therefore needs to be monitored or intervened, which requires a tool to assess its severity. Objective. The present study is aimed at adapting the Hogg Eco-Anxiety Scale (HEAS) ...
Added: April 18, 2026
Hushchyn M., Arzymatov K., Derkach D., Machine Learning 2026 Vol. 115 Article 56
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear ...
Added: March 6, 2026
Miske O., Abatayo A. L., Daley M. et al., Nature 2026 Vol. 652 P. 126–134
Published claims should be reproducible, yielding the same result when applying the same analysis to the same data. ...
Added: January 19, 2026
Ivanov S., Borisov V., Ali S. et al., , in: 2025 IEEE XVII International Scientific and Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE).: IEEE, 2025. Ch. 127 P. 1–7.
This paper investigates the problem of detecting slow refrigerant leaks in a data center cooling system using a graph neural network. The study addresses the challenge of early fault identification, proposing a method for constructing a topological graph based on the engineering diagram, the physical layout, and the cause-and-effect relationships in the cooling system. This ...
Added: December 19, 2025
Kopnova E., Журавлева К. А., Коряков И. В. et al., Журнал Белорусского государственного университета. Экономика 2025 № 1 С. 36–46
he article examines the prospects for economic cooperation between Russia and Belarus within the framework of the EAEU, SCO and BRICS integration associations, with an emphasis on the impact of sanctions and their consequences for the economic growth of the countries. The study includes the use of econometric modeling methods to evaluate the forecast of ...
Added: December 11, 2025
Gusev D., Sviridova O. A., Shidlovskii I. et al., Journal of Applied and Industrial Mathematics (перевод журналов "Сибирский журнал индустриальной математики" и "Дискретный анализ и исследование операций") 2025 Vol. 19 No. 1 P. 40–50
In today’s inventory management, there is a need to optimize new models in order to select the best solution to ensure order fulfillment. These models must take into account various uncertainties, as well as the concept of time value of money. At the same time, it is important to note that in practice, appropriate solutions ...
Added: December 11, 2025
Shidlovskii I., Менеджмент и бизнес-администрирование 2025 № 4 С. 92–104
The research is dedicated to an in-depth development of special modifications of EOQ (Economic Order Quantity) inventory management models that allow for both delays in revenue receipt and cargo capacity constraints of vehicles. The primary focus is on the analysis of supply strategies involving several vehicles when discounts are available, the magnitude of which depends ...
Added: December 11, 2025
Shidlovskii I., Транспорт: наука, техника, управление 2025 № 11 С. 66–70
This study is devoted to developing methods for analyzing supply chain efficiency within a modified EOQ model. The model incorporates a priori allowable delays in revenue receipt, the structure of working capital, and the limited cargo capacity of transport vehicles. The aim of the work is to determine the necessary and sufficient conditions for ensuring ...
Added: December 11, 2025
Shidlovskii I., Gusev D., Свиридова О. А. et al., Вестник Российского экономического университета им. Г.В. Плеханова 2026 Т. 23 № 1 С. 13–28
The article proposes a modified algorithm for multicriteria optimization of reserve management in conditions of uncertainty, based on the use of standardized data to represent indicators of particular criteria. This approach is aimed at eliminating the phenomenon of inadequate selection, in which criteria with smaller orders of magnitude have virtually no effect on the final ...
Added: December 11, 2025
Akhmedjanova D., Окунола А. О., Психологическая наука и образование 2025 Т. 30 № 5 С. 91–104
Context and relevance. Metacognition is the control over one’s thoughts. There is a lack of a reliable survey measuring metacognition of schoolchildren in Russia. Objective. The goal of this article is to report on the adaption of the metacognition survey on a sample of Russian adolescents. Hypothesis. The adaptation of the metacognition scale from the ...
Added: November 7, 2025
Kychkin A., Chernitsin I., Vikentyeva O., , in: 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2025. P. 987–991.
Industry 4.0 concept focuses on sustainability problem that requires to control air emissions, especially for harmful substances like H2S, and reduction their impact on nature by using environmental monitoring and sources identification systems. This task requires solving inverse problem of dispersion models, which should establish complex mathematical dependences between the sensor data, the location and ...
Added: November 4, 2025
Алескеров Ф. Т., Lola I. S., Asoskov D. et al., Вопросы экономики 2025 № 11 С. 143–157
The LC-curve method, a new approach to time series analysis, was applied to the composite Business Uncertainty Index (BUI), based on the results of regular Rosstat business surveys, which made it possible to analyze uncertainty trajectories across Russia's enlarged industries and sub-sectors using two index specifications: ex-ante (forecast) and ex-post (actual). The results of the ...
Added: October 13, 2025
Smirnov S. V., Вопросы экономики 2025 № 10 С. 131–154
The paper summarizes machine-learning (ML) methods most relevant to macroeconomics and assesses their performance in forecasting and nowcasting key macro indicators. Despite rapid methodological progress and a surge of publications over the past 25 years, gains in forecast accuracy with traditional statistical (economic, financial, and survey) data remain modest. ML models often outperform naïve and ...
Added: October 12, 2025
Kychkin A., Chernitsin I., Vikentyeva O., , in: 2025 International Russian Automation Conference (RusAutoCon).: IEEE, 2025. P. 1046–1050.
This paper presents system architecture of the automated AI-driven predictive assessment of the ecological state of the air in sanitary protection zones around industrial facilities. The developed solution implements automated pre-processing of data presented in the form of time series using machine learning methods and the construction of short-term forecasts of pollutant concentrations for the ...
Added: October 6, 2025