There are more than 27 million people in situations of forced labor worldwide on any given day. The international charity Hope for Justice and social enterprise Slave-Free Alliance strive for data-driven approaches to help organizations move toward slave-free operations and supply chains. By leveraging a suite of business intelligence, open source and advanced analytics, disparate data sources, such as PDF documents, can be mined for patterns to detect possible exploitation in difficult-to-track supply chains. Our team integrated Power Query and Python to extract and transform unstructured data and SAS® Visual Text Analytics on the SAS® Viya® platform to develop a deeper understanding of the data through natural language processing techniques. We discovered emerging textual themes and patterns through our text analytics life cycle approach. Our team used the prevalence of specific concepts and existing text-based knowledge to suggest commodities and industries with an increased risk of forced labor due to supply chain complexity and lack of oversight when products frequently changed hands. To convey our findings and maximize the range of capabilities the SAS ecosystem offers, we deployed an interactive dashboard to further explore forced labor based on country and commodity. We applied these insights to a risk assessment tool for organizations to mitigate or build resilience against modern-day slavery.
