Ordering decision-making methods on spare parts for a new aircraft fleet based on a two-sample prediction

• A modeling framework of ordering spare parts for a new fleet is proposed.
• Models for ordering time and number are established based on two-sample prediction.
• The computation of future failure time is simplified using Newtonian binomial law.
• Comparison of the first failure time PDFs is used to identify process parameters.
• Identification methods for spare parts are validated by Engine Driven Pump case study.

Ordering decision-making on spare parts is crucial in maximizing aircraft utilization and minimizing total operating cost. Extensive researches on spare parts inventory management and optimal allocation could be found based on the amount of historical operation data or condition-monitoring data. However, it is challengeable to make an ordering decision on spare parts under the case of establishment of a fleet by introducing new aircraft with little historical data. In this paper, spare parts supporting policy and ordering decision-making policy for new aircraft fleet are analyzed firstly. Then two-sample predictions for a Weibull distribution and a Weibull process are incorporated into forecast of the first failure time and failure number during certain time period using Bayesian and classical method respectively, according to which the ordering time and ordering quantity for spare parts are identified. Finally, a case study is presented to illustrate the methods of identifying the ordering time and ordering number of engine-driven pumps through forecasting the failure time and failure number, followed by a discussion on the impact of various fleet sizes on prediction results. This method has the potential to decide the ordering time and quantity of spare parts when a new aircraft fleet is established.