USING PREDICTIVE ANALYTICS FOR MANAGING SUPPLY AND DEMAND IN SMALL AND MEDIUM-SIZED ENTERPRISES
DOI:
https://doi.org/10.46827/ejefr.v9i3.2006Słowa kluczowe:
predictive analytics, demand forecasting, machine learning, small and medium-sized enterprisesAbstrakt
The article discusses the use of predictive analytics to manage supply and demand in small and medium-sized enterprises. The methods of machine learning, time series analysis and regression modeling are being investigated, which make it possible to predict changes in market demand, optimize the supply chain and increase the efficiency of production processes. Modern tools such as programming languages and machine learning libraries, data visualization tools and cloud services that provide computing power for processing large amounts of data are analyzed. Special attention is paid to the advantages of these methods, such as reducing operating costs, automating management decisions, and improving forecast accuracy. Obstacles to their implementation are being explored, such as limited resources, difficulties integrating predictive models into business processes, and data security issues.
JEL: C45, C53, L25, L86
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Bibliografia
Adewusi AO, Komolafe AM, Ejairu E, Aderotoye IA, Abiona OO, Oyeniran OC, 2024. The role of predictive analytics in optimizing supply chain resilience: a review of techniques and case studies. International Journal of Management & Entrepreneurship Research 6(3): 815–837. doi: 10.51594/ijmer.v6i3.938
Blazhkovskii A, Korostin O, Tretiakov I, Larin M, 2025. Using artificial intelligence to improve customer interaction efficiency in logistics. Competitiveness in the Global World: Economics, Science, Technology 2: 189–194
Bradač Hojnik B, Huđek I, 2023. Small and medium-sized enterprises in the digital age: understanding characteristics and essential demands. Information 14(11): 606. doi: 10.3390/info14110606
Maroufkhani P, Iranmanesh M, Ghobakhloo M, 2023. Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems 123(1): 278–301. doi: 10.1108/imds-11-2021-0695
Nurdinova K, 2024. Integration of artificial intelligence into accounting as a tool for optimization and risk management. Bulletin of Science and Practice 10(12): 405–410. doi: 10.33619/2414-2948/109/52
Opoku E, Okafor M, Williams M, Aribigbola A, 2024. Enhancing small and medium-sized businesses through digitalization. World Journal of Advanced Research and Reviews 23(2). doi: 10.30574/wjarr.2024.23.2.2313
Ponomarev E, 2024. Practical application of AI and NLP in Android mobile applications. Naukosphere 12(1): 22–27
Shokirov K, 2024. Application of artificial intelligence in warehouse operations management. Professional Bulletin: Economics and Management 4/2024: 29–33
