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In the context of intelligent maintenance, spare part prediction business scenario seeks promising return-oninvestment (ROI) by radically diminishing the hidden costs at after-sales customer services. However, the classification of class-imbalanced data with mixed type features at this business scenario is not straightforward. This paper proposes a hybrid classification model that combines C4.5, Apriori algorithms and weighted kNearest Neighbor (kNN) adaptations to overcome potential shortcomings observed at the corresponding business scenario. While proposed approach is implemented within CRISP-DM reference model, the experimental results demonstrate that proposed approach doubles the human-level performance at spare part prediction. This highlights a 50% decrease at the average number of customer visits per fault incident and a significant cutting at the relevant sales and distribution costs. According to best runtime configuration analysis, a real-time spare part prediction model has been deployed at the client’s SAP system.

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System