Supply chain visibility and monitoring
| dc.contributor.author | Rizoug Zeghlache, Manar | |
| dc.contributor.author | Youcefi, Nour El-houda | |
| dc.contributor.author | Lounnas, Bilal: supervision | |
| dc.date.accessioned | 2024-07-15T09:00:01Z | |
| dc.date.available | 2024-07-15T09:00:01Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | Supply chain visibility and monitoring is essential in modern business management, as it helps to accurately track the flow of materials and products, enhancing efficiency and reducing costs. Fleet management is an important part of logistics, and technologies such as the Internet of Things (IoT) and artificial intelligence are improving it through the use of cameras, sensors, and deep learning algorithms. In the research, a license plate recognition system was developed using the YOLO algorithm and optical character recognition (OCR) technologies, which improves operational efficiency, reduces human errors, and proves the possibility of using deep learning to improve fleet and supply chain management. | |
| dc.identifier.uri | https://dspace.univ-msila.dz/handle/123456789/43746 | |
| dc.language.iso | en | |
| dc.publisher | Mohamed Boudiaf University of M’sila | |
| dc.subject | Supply chain management | |
| dc.subject | visibility | |
| dc.subject | monitoring | |
| dc.subject | Artificial intelligence | |
| dc.subject | Internet Of things | |
| dc.subject | plate number detection | |
| dc.subject | OCR | |
| dc.subject | YOLO | |
| dc.title | Supply chain visibility and monitoring | |
| dc.type | Thesis |