A model of calculating spare parts demand volume by considering preventive maintenance

被引:0
|
作者
Hu Q.-W. [1 ]
Jia X.-S. [2 ]
Zhao J.-M. [1 ]
机构
[1] Department of Equipment Command and Management, Ordnance Engineering College, Shijiazhuang, 050003, Hebei
[2] Department of Training, Ordnance Engineering College, Shijiazhuang, 050003, Hebei
来源
| 1600年 / China Ordnance Industry Corporation卷 / 37期
关键词
Age replacement; Demand forecasting; Ordnance science and technology; Preventive maintenance; Spare part;
D O I
10.3969/j.issn.1000-1093.2016.05.020
中图分类号
学科分类号
摘要
Spare parts are the material basis of equipment operation and maintenance, and spare parts procurement usually takes a large share of equipment lifecycle cost. The traditional methods of calculating the spare parts demand volume only consider the requirements of corrective maintenance, and cannot be used to calculate the spare parts demand volume by considering preventive maintenance. A model of calculating the spare parts demand volume based on age replacement policy is proposed. A discrete algorithm for the model is presented. An example analysis is carried out to verify the applicability and effectiveness of the proposed model. The proposed model is compared with traditional calculation model. The research results show that the proposed model can be used to improve the accuracy of calculating the spare parts demand volume. It can be also used to calculate the spare parts demand volume for any long planning horizon and analyze the spare parts cost in equipment lifecycle process. © 2016, Editorial Board of Acta Armamentarii. All right reserved.
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页码:916 / 922
页数:6
相关论文
共 16 条
  • [1] Hu Q., Bai Y., Zhao J., Et al., Modeling spare parts demands forecast under two-dimensional preventive maintenance policy, Mathematical Problems in Engineering, 2015, pp. 1-9, (2015)
  • [2] Croston J.D., Forecasting and stock control for intermittent demands, Operational Research Quarterly, 23, 3, pp. 289-303, (1972)
  • [3] Rao A.V., A comment on: forecasting and stock control for intermittent demands, Journal of the Operational Research Society, 24, 4, pp. 639-640, (1973)
  • [4] Syntetos A.A., Boylan J.E., On the bias of intermittent demand estimates, International Journal of Production Economics, 71, 1, pp. 457-466, (2001)
  • [5] Syntetos A.A., Boylan J.E., The accuracy of intermittent demand estimates, International Journal of Forecasting, 21, 2, pp. 303-314, (2005)
  • [6] Zhao J.-Z., Xu T.-X., Liu Y., Et al., Consumption forecasting of missile spare parts based on rough set, entropy weight and improved SVM, Acta Armamentarii, 33, 10, pp. 1258-1265, (2012)
  • [7] Stevenson W.J., Operations Management: Theory and Practice, (2012)
  • [8] Zhao Y., Fu H.-Y., Zhang J., Et al., Demand analysis of the spare parts of avionic devices, Systems Engineering and Electronics, 24, 3, pp. 1-3, (2002)
  • [9] Mani V., Sarma V.V.S., Queuing network models for aircraft availability and spares management, IEEE Transactions on Reliability, 33, 3, pp. 257-262, (1984)
  • [10] Tao X.-C., Guo L.-H., Xiao B.-P., Et al., Demand prediction model for spare parts based on fill rate allocation, Acta Armamentarii, 33, 8, pp. 975-979, (2012)