Short term predictive demand model based on transport times for the reverse supply chain

A case study at KLM Engineering & Maintenance Component Services 2.0

Master thesis (2020)

Authors

P.D. Sulyman Mechanical Engineering

Contributors

W.W.A. Beelaerts van Blokland Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
R.R. Negenborn Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
J.W. Frouws Ship Design, Production and Operations - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2020 Peywand Sulyman
Aviation Short term forecasting Reverse MRO SC Transport time prediction Arrival time prediction
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Published Date

16-01-2020

Language

English

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Faculty

Mechanical Engineering

Abstract

The aviation Maintenance, Repair and Overhaul (MRO) industry faces an enormous growth due to the increasing air traffic. To accommodate this growth and increase efficiencies of processes it is required to increase the transparency of demand in the Supply Chain (SC). Research into predicting demand in the MRO industry have mainly been focused on the spare part request (forward logistics). This results in a research gap regarding increasing transparency in the reverse SC. This paper investigates an approach to increase transparency in demand by prediction the arrival times of shipments in the reverse SC based on transport times. The transport times from two customers have been collected and analysed. The analysis showed the presence of different transport behaviour depending on the process which included inefficiencies and waste. Based on the analyses different methods to predict the transport time have been formulated and evaluated. The results show two methods superior to the others which are the median and decision tree. Furthermore, the research indicates the accuracy of predictions increases with more available data and stable processes. The results show to be insufficient to accurately predict the majority of the shipments and therefore require further research.