botminds.aiRequest a demo

Aircraft Spare Parts Demand Forecasting demo

Forecasts part demand and sets stock levels by station and fleet, for materials planners 1:35

Book a demoExplore the solution

What the demo shows

Forecasts part demand and sets stock levels by station and fleet, for materials planners

Agents read component removals, aircraft health alerts, the flying plan, the check plan, new deliveries and the supplier's real delivery record, and forecast each part's demand at every station for 30, 60 and 90 days. Where a station will run short, the Sourcing Planner compares a pool borrow, a transfer from another station, an exchange and a purchase, and writes the cheapest option that arrives in time on a route map of the airline's stations, with its reason in one sentence. A second agent re-derives the forecast and checks the release certificate and the approval limit. The materials planner asks the agents about any shelf and approves, edits or rejects every borrow, transfer and order with a reason. The agents also propose what to do with idle stock, provision new aircraft and merge an acquired airline's parts.

Transcript

  1. Aircraft Spare Parts Demand Forecasting tells planners which parts each station will need and when.
  2. Surplus spares cost airlines $1.4 billion last year.
  3. Missing parts still ground aircraft.
  4. Marisol plans parts at O'Hare.
  5. The agents worked overnight.
  6. Brakeware alerts. 11 brakes on 6787-9S in 9 days.
  7. O'Hare needs nine breaks in 30 days and has four.
  8. Every number opens its source.
  9. Removals, flying hours, new aircraft.
  10. The supplier quotes 60 days.
  11. Recent orders took 120.
  12. The agents propose borrow three from the pool by Saturday and order two.
  13. Marisol asks about Denver's spare.
  14. The checker shows that would leave Denver at risk.
  15. She keeps the plan and approves with a reason.
  16. O'Hare is covered.
  17. Every number cited, every order signed, every step logged.
  18. The aim, 40% fewer parts groundings and an estimated $24 million back to cash.
  19. Built on Botminds: agents, data, screens, and governance on one platform.

Transcribed from the film’s narration. Captions are on the player.