End-to-End Outbound Supply Chain Optimization
Leading cross-functional initiative across a massive enterprise outbound supply chain spanning Fulfillment Centers, cross docks, middle-mile trucking, and final-mile delivery to reduce cost-to-serve and improve capacity.
Executive Overview
Managing a global physical and digital logistics network requires continuous synchronization across hundreds of Fulfillment Centers (FCs), intermediate sortation cross-docks, linehaul middle-mile transportation, and hyper-dense last-mile delivery operations.
Achieving global efficiency across this chain requires holistic network optimization. I have led initiatives across the enterprise outbound supply chain to model, simulate, and dynamically optimize physical flow across all transportation and sortation nodes.
Core Focus Areas & Deliverables
- Holistic Flow Optimization: Replaced isolated node-level optimization with unified end-to-end network flow modeling, preventing bottlenecks from shifting downstream into middle-mile or delivery stations.
- Predictive Last-Mile Resource Planning: Directed the development of machine learning predictive models for global driver and station resource planning, resulting in >800 fewer schedule gaps per day and $90MM in annual savings.
- Dynamic Labor & Forecasting Models: Improved short-term forecasting algorithms used for dynamic labor and workforce allocation, unlocking $145MM in annual operational savings.
- Capacity & Cost-to-Serve Optimization: Balanced linehaul utilization with dynamic sortation schedules, expanding aggregate package throughput without requiring capital expenditure on physical footprint.
Strategic Methodology
- Cross-Functional Orchestration: Unified engineering, operations research science, regional GM leadership, and frontline transportation teams around shared efficiency KPIs.
- Dynamic Flow Balancing: Implemented intelligent feedback loops that automatically adjust package routing when upstream processing delays or severe weather events impact specific transportation lanes.