GAF Uses AI for Supply Chain Network Optimization

GAF, one of North America's largest roofing material manufacturers, is deploying AI-powered analytics and digital supply chain modeling to transform network optimization and scenario planning across its 30+ production facilities. According to Marianna Vydrevich, GAF's supply chain network design expert, AI tools are automating data engineering workflows and reducing dependencies on IT support for routine analytics tasks, particularly in high-value functions like inventory optimization, procurement classification, and supply chain scenario analysis. The implementation highlights a critical shift in how enterprise supply chain teams are approaching complex optimization problems.

Rather than treating AI as a general-purpose text generation tool, GAF is applying it specifically to demand planning, network design, and operational bottleneck identification—use cases that require sophisticated digital twins mirroring real-world supply chain complexity. Vydrevich emphasized that successful AI adoption in supply chain requires moving beyond basic automation; the organization is using tools like Coupa's Navi AI assistant to help analysts interpret network changes and model alternative scenarios. This development signals an emerging skill gap in the supply chain profession.

Vydrevich predicts that foundational data engineering and coding knowledge will become as essential for supply chain professionals as general business acumen is today, mirroring how coding became widespread in enterprise roles over the past decade. Organizations that fail to upskill their teams in AI literacy and data fundamentals risk falling behind competitors who can rapidly iterate on network designs, optimize procurement strategies, and respond to disruptions through predictive modeling.