Automating warehouse inventory management
| dc.contributor.author | Hajibabaei, Neda | |
| dc.contributor.supervisor | Baniasadi, Amirali | |
| dc.date.accessioned | 2024-09-05T21:58:14Z | |
| dc.date.available | 2024-09-05T21:58:14Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering MEng | |
| dc.description.abstract | Efficient inventory management is crucial for the smooth operation of warehouses in large retail and chain stores, where traditional methods of manual barcode scanning are often labor-intensive and prone to errors. This project addresses these challenges by developing an automated system that utilizes QR codes and computer vision techniques for inventory tracking. By implementing OpenCV to detect QR codes from images captured within Walmart’s warehouse environment, the system processes and categorizes the data to identify warehouse sections and product details. Comprehensive reports are generated to facilitate accurate inventory management, and visualizations are created to provide clear insights into inventory distribution. This approach significantly enhances the accuracy and efficiency of inventory processes, reducing labor costs and improving decision-making and resource allocation. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/20381 | |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | automating | |
| dc.subject | warehouse | |
| dc.subject | inventory | |
| dc.subject | management | |
| dc.subject | QR code | |
| dc.title | Automating warehouse inventory management | |
| dc.type | project |