Transforming Unstructured Customs Data into Strategic Insights with Robotic Process Automation

Abstract

 Strategic decision-making in logistics and foreign trade relies on fast, accurate, and analytically actionable data. However, many commercial data platforms do not provide data through an Application Programming Interface (API) and must be manually obtained. Manual processes are error-prone, labor-intensive, and costly for companies. In this study, a sample data analytics framework based on Robotic Process Automation (RPA) has been developed to automatically extract, clean, and transform unstructured customs declaration data into analytical insights. Data was obtained from Datamyne.com using Python and Playwright, cleaned and standardized with Pandas and NumPy, stored in SQL Server, and visualized with Microsoft Power BI dashboards. Empirical findings show that the proposed system reduces daily data collection time by 93%, increases data accuracy from 95% to 98%, and reduces annual operational costs by approximately 94%. Furthermore, the integration of automated data pipelines with dynamic BI dashboards significantly increases analytical agility by enabling real-time monitoring, detailed analysis, and threshold-based KPI alerts. These results demonstrate that RPA-supported automation not only eliminates repetitive manual processes but also provides a measurable and sustainable competitive advantage for logistics and foreign trade operations by strengthening data-driven decision-making.