LAS VEGAS — AI-powered analytics and digital supply chain modeling are reshaping network optimization and scenario planning across manufacturing operations, according to Marianna Vydrevich, supply chain network design & optimization expert at GAF.
Vydrevich said AI tools are helping supply chain teams automate data engineering workflows and reduce dependence on IT departments for routine analytics tasks.
“It’s really an enhancement of capabilities,” Vydrevich told FreightWaves in an interview at the Coupa Inspire 2026 conference on May 13. “The main enhancement so far has been on the data engineering front.”
GAF is one of North America’s largest manufacturers of residential and commercial roofing and waterproofing materials, operating more than 30 locations across the continent. The company is headquartered in Parsippany, New Jersey.
Coupa Inspire 2026, held May 11 through May 13 at ARIA Resort & Casino in Las Vegas, brought together hundreds of procurement, finance and supply chain executives focused on spend management, sourcing and supply chain technology.
Coupa is a cloud-based, AI-native platform designed for total spend management and supply chain optimization.
Related: ‘AI is the new UI’: Coupa customers race to automate supply chains
Vydrevich said AI has already become ‘an absolute game changer’ for business analytics and data engineering workflows.
Vydrevich said AI delivers the greatest value in analytics-heavy functions such as inventory optimization, procurement classification and supply chain scenario analysis.
Vydrevich said AI’s role in supply chain network optimization is more complicated than simple text generation or basic automation because it requires digital models that closely mirror real-world supply chains.
“Doing network design, applying AI to network design is a higher bar than for other tasks,” she said. “You’re creating a digital twin which might not have all the exact details as your actual supply chain because it’s a model.”
She also highlighted Coupa’s Navi AI assistant as a tool capable of acting like a “junior modeler” to help analysts interpret network changes and operational bottlenecks.
“A lot of people are misusing it,” Vydrevich said of some enterprise AI adoption. “They’re trying to apply it to the wrong use cases.”
Vydrevich also predicted that coding and data engineering knowledge will become foundational skills for future supply chain professionals.
“Every office job will require it; every person will need to understand the basics of data engineering,” she said. “Like it was with coding 10 years ago.”
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