Proceedings of the 2017 7th International Conference on Applied Science, Engineering and Technology (ICASET 2017)

Spare Parts Demand Prediction Research of Navigation Marks Based on the Method of Quadratic Exponential Smoothing

Authors
Quan Wen
Corresponding Author
Quan Wen
Available Online May 2017.
DOI
10.2991/icaset-17.2017.30How to use a DOI?
Keywords
Quadratic exponential smoothing, Navigation mark, Spare parts, Prediction
Abstract

This paper aims to research and provide a reasonable prediction method that can be applied on routine work of navigation marks. On one hand, the reasonable prediction method can help maintenance and management unit to reduce cost and relative risk. On the other hand, according to Yangtze river waterway practical condition and historical consumption data of navigation marks' spare parts, the study has shown the quadratic exponential smoothing method be adopted in actual consumption research can satisfy the requirement of demand prediction.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 7th International Conference on Applied Science, Engineering and Technology (ICASET 2017)
Series
Advances in Engineering Research
Publication Date
May 2017
ISBN
10.2991/icaset-17.2017.30
ISSN
2352-5401
DOI
10.2991/icaset-17.2017.30How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Quan Wen
PY  - 2017/05
DA  - 2017/05
TI  - Spare Parts Demand Prediction Research of Navigation Marks Based on the Method of Quadratic Exponential Smoothing
BT  - Proceedings of the 2017 7th International Conference on Applied Science, Engineering and Technology (ICASET 2017)
PB  - Atlantis Press
SP  - 158
EP  - 162
SN  - 2352-5401
UR  - https://doi.org/10.2991/icaset-17.2017.30
DO  - 10.2991/icaset-17.2017.30
ID  - Wen2017/05
ER  -