Customer Challenge

  • Prevent extended modification periods brought on by low inventory
  • Reduce time and cost diverted to chasing parts from other stores and sources
  • Meet aircraft modification Cycle Times.
  • Leverage for customer negotiation
  • Project goal is to predict and source 40% of the parts at the time of induction

Solution

  • Created algorithm to analyze past modification data to make better predictions for future supply needs.
  • The larger data set allows improvement of ML algorithm accuracy and enables growth
    • Attained 70% accuracy in predication utilizing actual data from 3 aircrafts
    • For future mods, Logistics leadership& customer can use to balance investment in parts vs risk
  • Tool developed for ease of use and ownership cand can be used either on main server or as stand alone
  • Predictions and extrapolations improve as more data is fed in

Outcomes

  • Inventory management enhancement:
    • Developed a tool for managing inventory in line with performance-based commitments
  • Supplier evaluation:
    • Analyzed supplier non-conformance data for modifications
  • Field requirements adaptation:
    • Incorporated ‘Remove & Repair’ and ‘Remove & Replace’ actions for non-standard intervals in the field

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