Modeling and Forecasting the Demand for Aircraft Recoverable Spare PartsThis report explores issues in forecasting and modeling the demand for aircraft recoverable spare parts to improve the Air Force's estimation of spares and repair requirements over quarterly, annual, and longer planning horizons. Specifically, it demonstrates the utility of approaches that account explicitly for nonstationarity and their superiority over current methods used by the Air Force Materiel Command for these purposes. The authors recommend using a weighted regression, a special case of the Kalman filter, for forecasting demand for high-demand items. This approach is a logical extension of Bayesian statistics, which explicitly accounts for nonstationarity in stochastic processes, assigning greater weight to more recent than to less recent demands. Coupled with an improved approach to variance estimation that assigns greater uncertainty to longer planning horizons than to shorter ones, this holds the promise of reducing the cost of spares investments while achieving adequate levels of system performance. |
Contents
Demand Modeling in the Current System | 26 |
Nonstationarity in BaseLevel Demand Processes | 35 |
Parameter or Coefficient Variance | 42 |
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Common terms and phrases
action quantities greater Air Force Air Force's Aircraft Recoverable Spares aircraft spare base base-level demand Bayesian binomial probability distribution Bitburg data cannibalization compound Poisson compound Poisson process current system demand modeling demand process demands for aircraft depot repair described discussed Dyna-METRIC effects eight quarters eight-quarter moving average evaluations expected value explanatory variables explored exponential smoothing flying hours forecast variance forecasting methods function Geisler important improved techniques Kalman filter model line items logistics support management adaptations mands mean absolute deviation Mean Squared Error measurement equation nonstationarity NRTS Forecaster number of items observed VTMRs parameter peacetime period planning horizons Poisson distribution predictions probability distribution problem QPAs RAND research Recoverable Item repair requirements estimation resupply RMSE root mean squared spares and repair spares requirements specification ẞt stochastic process stochastic variability stock levels stockage policy tion transformation true demand rate underlying variance estimation variance-to-mean ratio VTMR estimator wartime weighted regression forecaster WRSK
References to this book
Lean Logistics: High-velocity Logistics Infrastructure and the C-5 Galaxy Timothy L. Ramey No preview available - 1999 |