Air Mobility Command uses Coupa algorithm powered by LLamasoft to dramatically improve aircraft demand forecast accuracy within two weeks
Air Mobility Command relied on manual, inefficient forecasting methods for aircraft demand that were increasingly inaccurate and unable to adapt quickly enough, causing delays, shortages, loss of resources, and difficulty making timely decisions about cargo needs.
Within two weeks of implementing Coupa Demand Modeling, demand forecasts became much more accurate and dependable, scenario analysis now delivers answers in minutes, and the command can make agile decisions as far out as a year in the future.
Frequently asked questions
What did this team achieve with this AI workflow?
Within two weeks of implementing Coupa Demand Modeling, demand forecasts became much more accurate and dependable, scenario analysis now delivers answers in minutes, and the command can make agile decisions as far out…
What tools did this team use?
Coupa Demand Modeling, Coupa Supply Chain Demand & Planning, LLamasoft.
What results were reported?
Forecast accuracy improvement: Boosted accuracy dramatically; Time to accuracy improvement: within two weeks; Scenario analysis response time: answers in minutes; Decision horizon: making decisions as far out as a year in the future (source-reported, not independently verified).
How is this supply chain AI workflow structured?
Manual forecasting identified as failing → Decade of historical data analyzed → Algorithm identifies demand drivers → Scenario analysis delivers answers.