Material Readiness: Perform weekly Clear-to-Build (CTB) analysis and Material Requirements Planning (MRP) to track components for engineering builds, proactively resolving supply gaps, working with contract manufacturer to confirm CTB status with each proto build. Prepare weekly CTB reports for NPI and Operations reviews.
Risk Management: Identify supply chain single points of failure (SPOFs), assess long-lead time risks, and develop mitigation plans for shortages impacting NPI builds and Production Ramp. Review open market and broker buy opportunities. Follow up with Component Engineering (CE) on alternative sources and submit/approve in ACE tool. Assist with 2nd Sourcing Qualification activities.
Cost Analysis & Budgeting: Evaluate material should cost, analyze quotes, and manage component PPV spend against standard cost. Drive approvals with BU and Finance and monitor component spending with Contract Manufacturer (CM). Compile preliminary pricing with sourcing manager/CM, suppliers directly to prepare Supply Chain team for projected NPI and MP Product Cost analysis.
Cross-Functional Collaboration: Serve as materials liaison between New Product Development (NPD) teams and global suppliers to ensure design-for-supply principles. Review support and component sampling with Suppliers on early development components required for upcoming proto builds.
NPI Proto Execution: Drive supply chain deliverables through multiple proto stages, ensuring operational readiness from prototype to Mass Production (MP) ramp
Qualifications & Requirements
Education: Bachelor’s degree in Supply Chain Management, Operations, Industrial Engineering, or a related field.
Experience: 3–5 years of experience in supply chain, procurement, or materials planning within a manufacturing environment (semiconductors, medical devices, or consumer electronics are highly preferred).
Technical Skills: Advanced proficiency in ERP/MRP systems (e.g., SAP, Oracle, NetSuite) and data analysis tools (Advanced Excel, Tableau, or SQL).
Analytics: Strong capability to utilize statistical models and AI tools for predicting material requirements.