Designing and evaluating a predictive maintenance approach for seats at Schiphol.

Schiphol is one of Europe's largest airports, and with millions of passengers that sit on their seats, a maintenance challenge arises. Every year all the seats at Schiphol get checked for damages so a prediction for next year's replacement can be made, as well as to see what seats need urgent replacement. An expert prediction is made to estimate how many seats need to be replaced next year, and this prediction can deviate from the real amount. To get a more accurate prediction Schiphol has been collecting data on the physical state of the seats, and the goal of this research is to see if with this data a predictive maintenance (PdM) approach is possible with the help of a survival analysis. Such a PdM approach would help next year's prediction accuracy, is less prone to errors, gives a higher customer satisfaction and gives more insight towards Schiphol. We are following the design cycle to create an iterative cycle, create a literature review regarding different PdM algorithms and management implications, and by applying a survival analysis to the data we get more insight into the degradation process. We will create a survival analysis that's able to predict an individual's seat degradation process, and with the help of the analysis give a recommendation to Schiphol about the seat management implications our results have.