Vladivostok, Vladivostok, Russian Federation
A comprehensive overview of data analytics and machine learning techniques that perform demand forecasting and planning is presented. The author has reviewed scholarly articles, conference papers and books in English published between 2001 and 2023. By judgment conversion, the logical underpinnings of the importance of these approaches in optimizing inventory levels and reducing shortages, as well as advantages and problems associated with the implementation of the methods, with their potential in decision-making processes are highlighted. Both traditional statistical methods: time series analysis, moving averages - and advanced methods: regression analysis, neural networks, ensemble models - are considered. A comparative analysis of the strengths, limitations and application features of the best practices has been made. Recommendations are given to improve supply chain management and increasing customer satisfaction.
data analytics, demand forecasting, optimizing inventory levels, reducing shortages, customer satisfaction, statistical methods, advanced methods, supply chain management, machine learning models
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