Dynamic spare parts transportation model for Arctic production facility

Accepted manuscript version. Published version at http://doi.org/10.1007/s13198-015-0379-x . Timely delivery of the required spare parts plays an important role in meeting the availability target and reducing the downtime of production facilities. Spare parts logistics is affected in complex ways wh...

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Bibliographic Details
Published in:International Journal of System Assurance Engineering and Management
Main Authors: Ayele, Yonas Zewdu, Barabadi, Abbas, Barabady, Javad
Format: Article in Journal/Newspaper
Language:English
Published: Springer Verlag 2015
Subjects:
Online Access:https://hdl.handle.net/10037/8630
https://doi.org/10.1007/s13198-015-0379-x
Description
Summary:Accepted manuscript version. Published version at http://doi.org/10.1007/s13198-015-0379-x . Timely delivery of the required spare parts plays an important role in meeting the availability target and reducing the downtime of production facilities. Spare parts logistics is affected in complex ways while operating in the Arctic, since the area is sparsely populated and has insufficient infrastructure. It is also greatly affected by the distinctive operational environment of the region, such as cold temperature, varying forms of sea ice, blizzards, heavy fog, etc. Therefore, in order to have an effective logistic plan, the effect of all influencing factors, called covariates, on the transportation of the spare parts need to be identified, modelled and quantified by the use of an appropriate dynamic model. The traditional models, however, lack the comprehensive integration of the effect of covariates on the spare parts transportation. The purpose of this paper is to introduce the concept of a dynamic model for spare parts transportation in Arctic conditions by considering the time-independent and time-dependent covariates. The model continuously updates the prior probabilities according to the most recent time-dependent covariates to provide posterior probabilities. The application of the model is illustrated using a case study.