Владивосток, Приморский край, Россия
Представлен всесторонний обзор методов анализа данных и машинного обучения, осуществляющих прогнозирование и планирование спроса. Проанализированы статьи, материалы конференций и книги на английском языке, изданные с 2001 по 2023 г. Путем преобразования суждений выделены логические основы значения этих подходов для оптимизации уровней запасов и сокращения дефицита, а также преимущества и проблемы, связанные с внедрением методов, с их потенциалом в процессах принятия решений. Рассмотрены и традиционные статистические методы: анализ временных рядов, скользящие средние, - и передовые методы: регрессионный анализ, нейронные сети, ансамблевые модели. Сделан сравнительный анализ сильных сторон, ограничений и особенностей приложения передовых методов. Даны рекомендации по совершенствованию управления цепочками поставок и повышению удовлетворенности клиентов.
аналитика данных, прогнозирование спроса, оптимизация уровней запасов, сокращение дефицита, удовлетворенность клиентов, статистические методы, передовые методы, управление цепочками поставок, модели машинного обучения
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