Industry
Logistics and last mile
Control service, capacity, and economics across a network that never stands still.
Operating context
The conditions leadership must reconcile.
Operating pressures
What makes the operating decision consequential.
Demand and capacity mismatch
Queues, backlogs, and service misses can reflect multiple constraints. The first task is to identify the binding one.
Network variability
Local conditions, partner capacity, and operating exceptions require a management system that distinguishes signal from noise.
Unit economics
Recovery is incomplete when service improves by adding uncontrolled cost or shifting loss elsewhere in the network.
Frontline adoption
A network design holds only when its decision rights, mechanisms, and escalation paths work at the operating edge.
Relevant work
How Luna Sol approaches the operating system.
Backlog and capacity recovery
Network operating model
Delivery-partner architecture
Service and cost control
Related proof, products, and insights
PSA backlog stabilization
Case study ? completed advisory work
Constraint, countermeasure, capacity, and governance work during a public recovery arc.
/work/psa
Amazon last-mile retrospective
Prior experience ? prior operating experience
Selected prior operating experience, not a Luna Sol engagement.
/experience/amazon
Backlog Recovery Model
Decision product ? firm method
Test demand, capacity, quality loss, timing, and recovery margin.
/tools/backlog-recovery-calculator
Evidence boundary
What the public record can support.
This focus area is supported by completed PSA advisory work and Ryan Miller's prior Amazon operating experience. Those evidence bases are labeled separately throughout the site.