управление цепочками поставок,
анализ данных,
машинное обучение,
прогнозирование спроса,
управление запасами,
удовлетворенность клиентов,
дефицит,
данные в реальном времени,
прогнозная аналитика
Abstract
Relevance of the topic. The need for expertise in data analysis and machine demand forecasting and forecasting in the management of probability chains. Target. analysis of variance, analysis, data analysis and machine forecasting, demand forecasting and planning, probability chain management. Methodology. Scoping case studies and research papers to explore the use of this method in finding chain-of-reference efficiency, stock utilization, shortage reductions, and customer characteristics. Results and samples. Several problems and limitations were identified, including quality issues and the need for qualified personnel. Suggestions have been made to overcome the problems, including improving the quality of data and investing in staff training and maintenance. The study also explores future research directions in data science research in the areas of time impact and predictive analytics. The results of this study have important implications for probability and observation chain managers in terms of the use cases, challenges, and expected outcomes of research on data analysis and machine learning methods in demand forecasting and planning. Application area. The sphere of chain management at the mesolevel.
