Addressing Key Challenges Faced by SME Exporters in Sri Lanka: A Comprehensive Approach Using Data Driven Technologies

In Sri Lanka's export landscape, various entities are present, including large businesses, multinational corporations (MNCs), and small and medium-sized enterprises (SMEs). SMEs in Sri Lanka specifically encounter distinct export challenges, such as the absence of automated information retrieval, certificated seller identification, optimal packaging suggestions, and hurdles in identifying global market opportunities. The current solutions for these challenges are both scattered and outdated, making it difficult for SMEs to discover a unified platform that efficiently addresses these issues. This study aims to provide a comprehensive solution for SMEs by leveraging data-driven approaches and modern technologies via an online platform. By Utilizing web scraping, automated information retrieval was achieved for targeted products. A neural network verifies seller certificates, permitting platform entry solely for certified sellers. Ant Colony Optimization identified the most optimal packaging material for shipments, and a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model analyzes time-series data to identify global market trends. The platform enables SMEs to interact with certified suppliers, optimize packaging material selection, recognize global market prospects, and provide access to crucial growth data swiftly through automation.