Economic and Social Research

Экономические и социально-гуманитарные исследования

2409-1073 3033-5442

122978

10.24151/2409-1073-2023-3-115-126

HMWMTX

Экономика инновационного развития: теория и практика

Economics of Innovation-Driven Growth: Theory and Practice

Экономика инновационного развития: теория и практика

Overview of application frameworks for using data analytics and machine learning in demand forecasting

Обзор прикладных основ использования аналитики данных и машинного обучения в прогнозировании спроса

https://orcid.org/0000-0002-3235-6429

Рогулин

Родион Сергеевич

Rogulin

Rodion Sergeevich

rafassiaofusa@mail.ru

кандидат экономических наук;

candidate of economic sciences;

Владивостокский государственный университет экономики и сервиса Vladivostok State University of Economics and Service

20 09 2023

115—126

29 05 2023

https://htlaw.ru/en/nauka/article/122978/view

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.

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.

аналитика данных прогнозирование спроса оптимизация уровней запасов сокращение дефицита удовлетворенность клиентов статистические методы передовые методы управление цепочками поставок модели машинного обучения

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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