Aircraft Engine Overhaul Demand Forecasting Using ANN

• Autorzy: Piotr Kozik , Jarosław Sęp
• Języki publikacji: EN

Abstrakty

EN

Due to the unpredictable nature for aircraft maintenance repair parts demand, MRO (Maintenance, Repair, Overhaul) business perceive difficulties in forecasting and are currently looking for a superior forecasting solution. This paper deals with techniques applicable to predicting spare part demand replacement during helicopter PZL 10W engine overhaul - operating according to hard - time. The experimental results show new forecasting method based on hard - time as the predicted time of required demand and ANN technique as forecasting models predicted numbers of spare parts. The evolution for a new forecasting method, which will be a predictive error-forecasting model which compares and evaluates forecasting methods, based on their factor levels when faced with intermittent demand show as possibility of big changes in MRO lean manufacturing. The results confirm the continued superiority of the new method, whereas, most commonly leveraged methods such as moving average used by MRO business are found to be questionable, and consistently producing poor forecasting performance.(original abstract)

Słowa kluczowe

EN

Artificial intelligence; Planes; Exploitation

PL

Sztuczna inteligencja; Samoloty; Eksploatacja

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2012
• Tom: 3
• Numer: nr 2
• Strony: 21--26

Twórcy

autor

Piotr Kozik

• WSK "PZL Rzeszów" S.A.

autor

Jarosław Sęp

• Rzeszów University of Technolog, Poland

Bibliografia

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Identyfikatory

DOI

10.2478/v10270-012-0012-2