Shipping manifests often contain errors and inconsistencies that require manual correction. Mislabeling can lead to incorrect duty payments and prevent customs officials from accurately identifying incoming goods. Reviewing this data manually is time-consuming and prone to error. To solve this, a model was developed to automatically detect and correct common issues in shipping data. It reduces manual workload, improves accuracy, and increases transparency across the supply chain. The solution also establishes a foundation for scalable improvements in maritime data quality.
