A text mining study of competencies in modern supply chain management with skillset mapping

Abstract

This study explores the skills and competencies required by modern supply chain management professionals, focusing on the shift toward advanced technological capabilities. We analyze job advertisements from a prominent Canadian employment platform using web scraping, natural language processing, and machine learning techniques, including Latent Dirichlet Allocation and Term Frequency-Inverse Document Frequency. The findings reveal that job postings primarily emphasize traditional operational skills such as logistics, inventory control, and customer relationship management. However, there is a noticeable underrepresentation of advanced technological competencies, such as machine learning, data analytics, and automation, which are increasingly critical in today's supply chain environment. This gap highlights the need for greater alignment between job market demands and supply chain management's evolving digital transformation landscape. The study identifies key themes, including technical, managerial, and soft skills integration, emphasizing adaptability, data literacy, and strategic decision-making. The results suggest a misalignment between the competencies highlighted in job advertisements and the skills necessary for managing the complexities of a digitalized supply chain. This research offers practical recommendations for industry leaders to refine hiring strategies, academic institutions to modernize curricula, and job platforms to better showcase emerging skill requirements. Addressing this gap is essential to equip supply chain professionals with the tools and expertise to meet the challenges of a technology-driven future.