USING ARTIFICIAL INTELLIGENCE IN THE LOGISTICS INDUSTRY

Abstract

The article is devoted to the topic of using artificial intelligence (AI) in the logistics industry. It has been studied that artificial intelligence opens up new opportunities for optimizing business processes, increasing efficiency and adapting to changes in market conditions. AI contributes to the automation of tasks, analyzing large data sets in real time and forecasting future needs. Thanks to the implementation of such technologies, companies are able to optimize transportation routes, manage warehouse operations more effectively, minimize costs and reduce environmental impact.
It was found that one of the key advantages of AI is its ability to process large amounts of data and provide accurate recommendations for making strategic decisions, forecasting demand, taking into account seasonality and market trends, as well as analyzing logistics routes to improve delivery efficiency.
It is substantiated that the integration of AI changes the approach to supply chain management. The use of SCM systems in combination with AI allows to coordinate the flows of materials and information, increasing the transparency and adaptability of the entire process. ERP systems with AI components contribute to improving the management of finances, human resources and production processes. CRM systems, supplemented by artificial intelligence, allow to provide a personalized approach to customers, increasing their satisfaction and loyalty.
The implementation of AI in warehouse management systems (WMS) and transport (TMS) is especially important. Thanks to intelligent algorithms, these systems allow to automate storage processes, reduce logistics costs and build the best routes for transportation. Such approaches contribute to the sustainability of operations and reduce environmental impact.
It has been studied that AI creates new opportunities for the use of autonomous vehicles, drones and robotics in logistics. They allow to perform complex tasks with minimal human intervention, increasing the speed and accuracy of operations.
It has been found that the integration of AI into logistics is accompanied by a number of challenges. These include high implementation costs, the need to collect large volumes of high-quality data, as well as the need for qualified specialists to develop and maintain systems. In addition, issues of ethics, data confidentiality and ensuring transparency of algorithms remain important.