Abstract
During the initial aviation material recommended period, the fault rate of parts is constant, random probability of aviation material demand satisfied Poisson theory, the Poisson distribution can be carried out to predict air materiel demand; for material which has certain historical fault information, we can introduce Bayesian method, then calculate the demand for spare parts. The validity of the method is proved by examples.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, Y., Liang, J.: Overview and enlightenment of demand forecasting method research about aircraft spare parts. J. Civ. Aviat. Univ. China 32(1), 92–96 (2014)
Sha, H., Cai, J., Lin, H.: Research on safety stock of air-material under the laws of the different needs. J. Logist. Sci. Tech. 11, 47–49 (2011)
Kang, Z., Xu, K., Yang, C.: Application of ANFIS in aviation material spare parts requirement prognostication. J. Sichuan Ordnance 36(1), 84–86 (2015)
Aronis, K.-P., Dekker, R.: Inventory control of spare parts using a Bayesian approach: a case study. Eur. J. Oper. Res. 154, 730–739 (2003)
Yang, Y., Wang, D., Chang, S.: Research on forecasting the demand of aviation spares by simulating based on SPN and arena. Value Eng. 8, 129–130 (2012)
Ruan, M., Li, Q., Huang, A.: Optimization of cooperative ordering project for consumable spare parts under (R, Q) inventory policy. Trans. Beijing Inst. Technol. 7(33), 680–684 (2013)
Zhu, S., Li, S., Li, W., et al.: A spare parts providing model with the maximal availability. J. Air Force Eng. Univ. Nat. Sci. Ed. 2(6), 22–24 (2005)
Liu, Z., Liu, Y., Wang, D.: Aeronautic spare parts supervise model based on operating availability and demand analysis. Aeronaut. Comput. Tech. 5(37), 38–41 (2007)
Zhang, D., Ming, X., Zhao, C., et al.: Spare parts demand forecasting method based on BP neural networks and equipment characters. Mach. Des. Res. 1(26), 72–76 (2010)
Luan, Y., Han, Q., Zhang, Y.: Study on storage control model of repairable aerial material based on Poisson distribution. J. Value Eng. 45–46 (2016)
Zhao, J., Xu, T., Ge, X., et al.: Consumption forecasting of missile spare parts based on wavelet transform and revised GM-ARMA model. J. Beijing Univ. Aeronaut. Astronaut. 4(39), 553–558 (2013)
Gao, K., Xing, G., Sun, D., et al.: Application of grey correlation SVM in reserve consuming prediction of spare parts. Electron. Opt. Control 3(19), 100–105 (2012)
Zhao, H., Wang, J., Zhu, G.: A predictive model of the following spare parts based on Bayes. J. Microelectron. Comput. 21(9), 104–106 (2005)
Marcot, B.G.: Metrics for evaluating performance and uncertainty of Bayesian network models. Ecol. Model. 230, 50–62 (2012)
Douail, N., Csaba, H.: Diagnosis support system based on clinical guidelines: comparison between case-based fuzzy cognitive maps and Bayesian networks. Comput. Methods Programs Biomed. 113, 133–143 (2014)
Wolbrecht, E., Dambrosio, B., Paasch, R., et al.: Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks. Artif. Intell. Eng. Des. Anal. Manuf. 1(14), 53–67 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Niu, P., Hu, W., Wang, Z. (2019). Prediction of Aviation Material Demand Based on Poisson Distribution and Bayesian Method. In: Deng, K., Yu, Z., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2018. Advances in Intelligent Systems and Computing, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-00214-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-00214-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00213-8
Online ISBN: 978-3-030-00214-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)