Classifying MRP Strategy of Aircraft Spare Parts Using Supervised Machine Learning

Abstract

Spare-parts provisioning strategy is crucial for airlines or Maintenance Repair Overhauls (MRO). The strategy is divided into multiple classes that refer to several criteria, then uses Material Requirements Planning (MRP) to run those strategies. However, classifying spare parts into a strategy is onerous and requires innovation. Therefore, we employ three algorithms: Decision Tree, Random Forest, and Support Vector Machine (SVM), and use spare parts and aircraft utilization data to classify the strategy and optimize spare parts provisioning of MRO over time. The results showed Random Forest performs best by accuracy, sensitivity, and specificity with a score of more than 97%.

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