Strategic insights from the classroom: teaching apparel sourcing using US trade data and HS code analytics

As global sourcing becomes increasingly data-driven, fashion merchandising students must develop competencies in trade-data analysis and evidence-based decision making. This study examines a project-based instructional approach integrating Harmonized System (HS) code analysis, trade-data interpretation, and data visualization within a senior-level Global Sourcing course. Grounded in constructivist pedagogy and Kolb's Experiential Learning Theory, 73 undergraduate Apparel Merchandising and Product Development students analyzed decade-long U.S. apparel import trends using USA Trade Online and RStudio. Students identified sourcing patterns, evaluated trade relationships, and developed data-driven sourcing recommendations using authentic industry datasets. Learning outcomes were assessed through pre- and post-assessments measuring knowledge, skills, and confidence in trade-data analysis. Results showed statistically significant improvements across all measures, with large effect sizes for understanding HS codes, identifying sourcing partners, interpreting trade trends, and applying trade data to sourcing decisions. Integrating authentic trade datasets into fashion curricula can strengthen trade-data literacy, enhance workforce readiness, and better prepare graduates for data-driven sourcing and global supply chain careers.