Тег: inventory management

The impact of modern ERP systems on supply chain management

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Библиографическое описание статьи для цитирования:

. The impact of modern ERP systems on supply chain management//Наука онлайн: Международный научный электронный журнал. - 2025. - №4. - https://nauka-online.com/ru/publications/economy/2025/4/19-7/

Аннотация: (English) Amid accelerating global digitalization of business processes, the integration of ERP systems into supply chain management has become a key factor in determining the competitiveness of modern enterprises. The aim of this article is to provide a multifaceted analysis of the transformational impact of enterprise resource planning (ERP) platforms on logistics operations, with a focus on identifying and describing mechanisms that enhance the efficiency of material flows. Ongoing academic debates reveal significant contradictions regarding the optimal level of ERP system customization, the role of the human factor in implementation, and the potential for integration with Industry 4.0 technologies. Based on a structured review of relevant publications, it is observed that the comprehensive integration of ERP platforms into supply chain architecture leads not only to quantitative improvements in operational performance—such as inventory reduction and enhanced forecasting accuracy—but also to a qualitative transformation of managerial paradigms. In this context, the author outlines a perspective on the development of workforce competencies within the digital ecosystem. The content presented in this article is valuable for logistics managers, system integrators, and the academic community engaged in research on the digital transformation of business processes.

Application of demand forecasting models in inventory management

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Библиографическое описание статьи для цитирования:

. Application of demand forecasting models in inventory management//Наука онлайн: Международный научный электронный журнал. - 2025. - №3. - https://nauka-online.com/ru/publications/economy/2025/3/08-20/

Аннотация: (English) The study focuses on improving inventory management efficiency through accurate demand forecasting. It provides an overview of key statistical methods (ARIMA, exponential smoothing) and modern machine learning techniques (decision trees, neural networks), along with their hybrid versions. The impact of forecast accuracy on critical logistics aspects—inventory levels, safety stock, turnover rates, and shortage risks—is examined. Successful implementation practices in leading companies are analyzed, highlighting the economic benefits achieved. The author's contribution includes recommendations on selecting and adapting models to the specifics of logistics processes, as well as proposals for integrating demand forecasting with other elements of supply chain management.

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