Використання технологій машинного навчання для оптимізації логістики

Abstract

UA: В статті досліджується важливість впровадження машинного навчання в логістику. Аналізуються недоліки традиційних методів та обґрунтовується застосування машинного навчання для підвищення гнучкості і швидкості логістики. Зроблено акцент на комплексному використанні та конкретних рекомендаціях для успішного впровадження. Результати корисні фахівцям в галузі логістики, що прагнуть підвищити ефективність процесів через інноваційні методи машинного навчання.

EN : This article explores the significance and potential implementation of machine learning technologies in logistics to optimize supply chain processes. The authors analyze the challenges of traditional logistics methods and advocate for the application of machine learning to enhance flexibility and responsiveness to market changes. Emphasizing the unresolved aspects of comprehensive machine learning utilization in logistics, the article provides a specific set of recommendations for successful technology implementation. The research discusses the importance of the role played by machine learning in addressing issues related to transportation logistics. The complexities of efficient route planning, influenced by factors such as traffic, weather conditions, and communication, are highlighted. To tackle these challenges, the authors propose the use of modern technologies and tools, including transportation resource management systems, route planning and warehouse management software, transport and cargo monitoring systems, and demand analytics and forecasting systems. The efficiency of logistics systems in business operations, achieved through the integration of modern technologies, is examined by prominent scholars such as Chornopyska, N., Stasiuk, K, Jin, X., Zhong, M., Quan, X., and others. The authors of this work further investigate issues related to the application of information technologies in logistic system management, analyzing obstacles like high costs, insufficient staff qualifications, and system integration challenges. The article concludes with recommendations and strategies to overcome these challenges and successfully implement information technologies in the logistics system of enterprises. In summary, this research delves into the integration of machine learning technologies in logistics, providing a comprehensive analysis of the current state of the field. The authors contribute valuable insights, addressing challenges and proposing practical recommendations for professionals in the logistics industry seeking to enhance the efficiency of their processes through innovative machine learning methods.