Владивосток, Приморский край, Россия
В работе обсуждаются возможные преимущества объединения методов анализа данных и машинного обучения для прогнозирования спроса и планирования в управлении цепями поставок. Работа включает в себя анализ тематических исследований и документов, в которых эти методы были успешно интегрированы для улучшения эффективности управления цепями поставок, и описывается их влияние на уровень запасов, дефицит и удовлетворенность клиентов. В работе также обсуждаются проблемы и ограничения использования этих методов, включая вопросы качества данных и потребность в квалифицированных сотрудниках, а также предлагаются стратегии для преодоления этих проблем. Исследование также рассматривает будущие направления исследований в области прогнозирования и планирования спроса, включая интеграцию данных в режиме реального времени и использование прогнозной аналитики. Результаты работы обобщаются и приводятся выводы для практики и будущих исследований. В целом, интеграция методов анализа данных и машинного обучения может значительно улучшить прогнозирование спроса и планирование в управлении цепями поставок, однако это требует тщательного анализа качества данных, обучения персонала и технологической инфраструктуры.
анализ данных, машинное обучение, прогнозирование спроса, планирование, цепочка поставок.
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