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- W2997112982 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." @default.
- W2997112982 created "2020-01-10" @default.
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- W2997112982 date "2020-01-01" @default.
- W2997112982 modified "2023-09-27" @default.
- W2997112982 title "Short term predictive demand model based on transport times for the reverse supply chain: A case study at KLM Engineering a Maintenance Component Services 2.0" @default.
- W2997112982 hasPublicationYear "2020" @default.
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