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Predictive Analytics in Utility Vehicle Maintenance

  • In public transportation, the motor pool often consists of various different vehicles bought over a duration of many years. Sometimes, they even differ within one batch bought at the same time. This poses a considerable challenge in the storage and allocation of spare parts, especially in the event of damage to a vehicle. Correctly assigning these parts before the vehicle reaches the workshopIn public transportation, the motor pool often consists of various different vehicles bought over a duration of many years. Sometimes, they even differ within one batch bought at the same time. This poses a considerable challenge in the storage and allocation of spare parts, especially in the event of damage to a vehicle. Correctly assigning these parts before the vehicle reaches the workshop could significantly reduce both the downtime and, therefore, the actual costs for companies. In order to achieve this, the current software uses a simple probability calculation. To improve the performance, the data of specific companies was analysed, preprocessed and used with several modelling techniques to classify and, therefore, predict the spare parts to be used in the event of a faulty vehicle. We summarize our experience running through the steps of the Cross Industry Standard Process for Data Mining and compare the performance to the previously used probability. Gradient Boosting Trees turned out to be the best modeling technique for this special case.show moreshow less

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Metadaten
Author:Stephan Trahasch, Jürgen Prinzbach
Year of Publication:2018
Pagenumber:6
ISBN:978-1-61208-681-1
Language:English
Parent Title (English):DATA ANALYTICS 2018, The Seventh International Conference on Data Analytics
First Page:97
Last Page:102
Document Type:Conference Proceeding
Institutes:Hochschule Offenburg / Bibliografie
Acces Right:Zugriffsbeschränkt
Release Date:2019/01/08
Licence (German):License LogoEs gilt das UrhG