System and method for scenario-driven optimization of sourcing cost

Abstract

Sourcing is a practice of locating and selecting entities based on set criteria. Determining most appropriate supplier entities at the lowest cost can develop a competitive advantage. However, existing sourcing techniques lack effective ways to target best suppliers for right part bundling and to efficiently optimize sourcing cost for sustained savings. Present disclosure leverages machine learning and optimization methods with technology data to provide a computationally efficient solution. Relationships between parts and corresponding attributes are obtained for parts bundling and best supplier locations are mapped for selected part bundles. A sourcing cost minimizer is used in conjunction with an iterative process to grow or limit supplier capacity by constraining award amount, bundle mix, or number of supplier entities based on multiple alternative scenarios being defined to systemically minimize cost and manage risks driven by changes in customer demand or manufacturing location.