Optimization of inventory management through computer vision and machine learning technologies

Abstract

This study presents implementing and evaluating a computer vision platform to optimize warehouse inventory management. Integrating machine learning and computer vision technologies, this solution addresses critical challenges in inventory accuracy and operational efficiency, overcoming the limitations of traditional methods and pre-existing automated systems. The platform uses convolutional neural networks and open-source libraries such as TensorFlow and PyTorch to recognize and accurately classify products from images captured in real time. Practical implementation in a natural warehouse environment allowed the proposed platform to be compared with traditional systems, highlighting significant improvements, such as a 45% reduction in the time required for inventory counting and a 9% increase in inventory accuracy. Despite facing challenges such as staff resistance to change and technical limitations on image quality, these difficulties were overcome through effective change management strategies and algorithm improvements. The findings of this study identify the potential for computer vision technology to transform warehouse operations, offering a practical and adaptable solution for inventory management. • The platform reduced inventory time by 45% and improved stock efficiency by 50%. • Overcounting errors dropped by 67% and undercounting by 85%. • The platform cut stock update time from 30–35 min to 10–12 min. • Electronics overcounts fell by 78% and undercounts by 91%. • The platform demonstrated compatibility with existing systems and flexible APIs.

Topics & Concepts