Are you confident you’re paying the right price for every component?
Traditional cost models and supplier negotiations often rely on historical benchmarks and single-variable pricing, leaving large portions of cost unexplained. As supply chains grow more complex, procurement and engineering teams need a smarter, data-driven approach.
This whitepaper explores how machine learning enhances Linear Performance Pricing (LPP) to create accurate, explainable should-cost models. It introduces a practical two-stage framework that helps teams audit spend, predict fair costs, identify overpriced parts, and strengthen negotiation outcomes.
Featuring a real-world case study on industrial packaging components, the paper demonstrates how multi-variable models can explain over 90% of cost variance, uncover pricing inefficiencies, and guide standardization decisions.
Download the whitepaper to learn how ML-driven cost intelligence can deliver transparency, predictability, and measurable savings across procurement and sourcing operations.