USAGE OF MACHINE LEARNING IN INVENTORY AND WAREHOUSE MANAGEMENT SYSTEMS

Abstract

The management of inventory and warehouse is a critical component of supply chain operations. This article explores the impact of machine learning on inventory and warehouse management systems. This research is important because machine learning is gaining increasing recognition for its potential to increase operational efficiency and accuracy across industries.

The purpose of this research is to examine the hypothesized potential of implementing machine learning to solve challenging problems in inventory management systems in an attempt to improve their efficiency. In addition, the study aims to evaluate current applications of these capabilities to provide a basis for future research.

The research is grounded in a comprehensive review of existing literature on the use of machine learning in inventory and warehouse management. To explore practical implementations, the study also analyses how leading companies have integrated machine learning into their products.

The result of this study is a review of existing research and practical applications of machine learning in inventory and warehouse management. This paper provides a detailed analysis of how machine learning can be used to improve demand forecasting, inventory levels, warehouse planning, and order fulfillment processes. This material serves as a basis for further study and research in this area.

The scientific novelty of this research is the study of machine learning methods applied to inventory and warehouse management.  The study expands the understanding of how machine learning can be used to solve problems in these areas.

The practical implications of the research provide a basis for the implementation of mentioned technologies in enterprises. Firstly, the study highlights the potential for significant cost savings, efficiency gains, and increased customer satisfaction. Secondly, it offers valuable material for further research in supply chain management.