The Supply Network Designer is responsible for supporting the design and optimization of the company’s supply chain network through data-driven analysis and scenario modeling. The role focuses on developing and maintaining network models to evaluate configuration options, particularly in the context of planned volume ramp-ups, and to assess implications on cost, service levels, and risk.
In close collaboration with Commodity Management, Strategic Supplier Management, Operations, and relevant stakeholders, the role ensures alignment of key assumptions, including demand, capacity, inventory positioning, and external sourcing strategies. Based on this aligned data foundation, the role provides structured analyses and recommendations to support senior leadership decision-making.
Furthermore, the role contributes to the evaluation of external production and logistics setups, capacity ramp scenarios, and the positioning of strategic buffers along the supply network. It also defines data requirements and supports the integration of network models into relevant supply chain systems and tools.
- Build and maintain SC network models (digital twin) in optimization tools
- Develop and simulate network design scenarios (e.g., volume scaling, footprint changes)
- Translate business strategy and constraints into model parameters and assumptions
- Align key assumptions (demand, cost, capacity, service levels) with Category, Supplier Mgmt., and Operations
- Quantify cost, service, and risk trade-offs across scenarios
- Model capacity ramp-up options (space, equipment, workforce)
- Recommend decoupling points and inventory positioning strategies
- Assess network risks and simulate mitigation scenarios (e.g., dual sourcing, buffers)
- Derive KPI impacts and provide transparency for decision-making
- Prepare executive-ready materials and decision proposals
- Facilitate cross-functional alignment to ensure a consistent fact base
- Define data requirements and structure for integration with SC systems (e.g., Kinaxis, ERP)
- Continuously recalibrate models based on new data, assumptions, and business changes
