Executive Summary
Organizations are under increasing pressure to extract more value from supplier relationships while managing cost volatility and limited transparency into underlying cost structures. A persistent challenge for procurement teams is the inability to accurately assess what a product or service should cost, which weakens negotiation leverage and limits opportunities for sustainable savings. Traditional cost analysis methods are often manual, time-intensive, and insufficiently detailed to support dynamic market conditions.
This webcast from GEP examines how digital should-cost analysis, powered by AI-driven modeling tools, enables procurement teams to build accurate, data-driven cost models. It explains how these tools deconstruct supplier pricing into components such as materials, labor, overhead, and margins, providing greater transparency into cost drivers. This allows procurement leaders to move beyond price benchmarking toward fact-based negotiations grounded in cost intelligence.
A key focus is on how digital modeling improves speed, scalability, and accuracy compared to traditional approaches. The webcast highlights how procurement teams can simulate scenarios, respond to market changes, and identify savings opportunities across categories. It also emphasizes the role of these insights in strengthening supplier collaboration by enabling more informed, objective discussions around pricing and value.
By adopting AI-powered should-cost analysis, organizations can enhance negotiation outcomes, reduce dependency on supplier-provided data, and drive more consistent savings realization. The webcast provides a practical perspective on how digital tools can transform cost analysis into a strategic capability within procurement.
Watch the webcast now.
FAQs
It provides cost transparency, enabling fact-based negotiations, stronger supplier discussions, improved pricing accuracy, and identification of savings opportunities based on detailed cost breakdowns rather than assumptions.
It enables faster, data-driven cost modeling, scenario analysis, and real-time insights into cost drivers, improving accuracy and responsiveness in sourcing and negotiation decisions.
They are manual, slow, and lack scalability, limiting accuracy and the ability to respond to market changes or analyze complex cost structures effectively.