The problem
Why this decision becomes expensive without structure
Air cargo teams struggle to balance hub capacity, routing choices, service levels, and disruption risk across disconnected planning tools and manual network decisions.
Spreadsheets and manual planning break down when constraints interact. Generic AI tools lack the structural matching needed to produce usable, reviewable outputs. This use case needs a decision workflow that fits the problem shape, not a one-size-fits-all answer.
Typical use cases
Where this solution fits
Design or refine hub-and-spoke cargo flows
Compare routing and capacity tradeoffs across airports and nodes
Allocate cargo volumes under network and service constraints
Evaluate disruption scenarios and alternate network designs
Improve air cargo throughput planning across hubs and routes
Outputs you receive
Decision-ready outputs for this use case
Mongeflow packages this work into stakeholder-ready output layers and premium export formats.
Benchmark context
16% landed cost reduction
Feng et al. (2015) Transportation Research
Where this solution is used
Related industries
See this workflow inside Mongeflow
Explore how Mongeflow turns this operational problem into a structured decision path with clearer outputs, assumptions, and handoff.