Demand Forecasting of Equipment and Materials by Weibull Distribution Based on Bayesian Estimation and Monte Carlo Simulation

doi:10.3969/j.issn.1000-1093.2017.12.019

Abstract

Abstract: The demand of new equipment and materials cannot be mastered well because of less historical demand data and undefined demand. To address this problem, a demand forecasting method based on Weibull distribution is proposed for equipment and materials in the case of small failure samples. The parameters of equipment and material life distribution are estimated by Bayes estimation and MCMC simulation for K-S goodness-of-fit test, including scale and shape parameters. A Monte Carlo simulation-based forecasting method for the annual demand of equipment and materials is presented, in which repairing maintenance, preventive maintenance and service time of equipment and materials are considered. The analysis of examples shows that the life distribution model derived from Bayes estimation has higher degree of fitting in the case of few samples, and the Monte Carlo simulation-based forecasting method is simple and effective. Key

Key words: ordnancescienceandtechnology, equipmentandmaterialdemandforecasting, Weibulldistribution, Bayesestimation, MonteCarlosimulation

CLC Number:

WU Long-tao, WANG Tie-ning, YANG Fan. Demand Forecasting of Equipment and Materials by Weibull Distribution Based on Bayesian Estimation and Monte Carlo Simulation[J]. Acta Armamentarii, 2017, 38(12): 2447-2454.

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第38卷第12期
2017 年12月兵工学报ACTA
ARMAMENTARIIVol.38No.12Dec.2017

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