Supply Planning Manager

Global Component Planner For Memory Commodities

As a Global Component Planner for Memory commodities, you will be responsible for end-to-end supply planning to ensure Cisco meets its global revenue targets. You will serve as a strategic lead, working cross-functionally with Supply Chain Operations, Finance, and Business Units to drive collaborative planning, mitigate supply risks, and navigate complex technology lifecycles.

Your Impact

• Strategic Supply Planning: Design and implement end-to-end global supply plans for Memory and related sub-commodities, including integrated co-planning initiatives with key suppliers.
• Inventory & Fulfillment Excellence: Partner with global hub operations to optimize inbound fulfillment modes. Own the inventory decision-making process, ensuring all actions align with Cisco's "Weeks of Supply" (WOS) strategy and financial goals.
• Shortage Mitigation & Agility: Proactively identify and resolve component shortages through agile communication and rapid problem-solving with regional partners and global suppliers.
• E&O Management: Apply advanced analytical and collaborative techniques to manage and mitigate Excess & Obsolescence (E&O) risks while maximizing total revenue potential.
• Supplier & Commodity Strategy: Cultivate strong partnerships with Global Supplier Management (SuCM) and external vendors to ensure commodity-specific strategies are aligned with Cisco's business requirements.
• NPI & Technology Transitions: Lead the commodity supply response to volatile industry transitions. Coordinate closely with Engineering, Hubs, and Suppliers to manage qualifications and ensure successful New Product Introduction (NPI) ramp-ups.
• Cross-Functional Leadership: Drive alignment across extended partner teamsincluding Finance and Business Unitsto ensure a synchronized approach to component planning and supply chain resilience.

Requirements

• 7+ years' experience with Materials Management, Supplier Relationship Management and/or Supply/Demand Planning
• Experience and knowledge of manufacturing processes and procedures
• Experience with data analysis, data analytics and data modeling