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

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2020-2-24

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Esgin, Eren

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In the context of intelligent maintenance, spare part prediction business scenario seeks promising return-on-investment (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 k-Nearest 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

Subject Keywords

Intelligent Maintenance, SAP, Spare Part Prediction, Weighted k-Nearest Neighbor, CRISP-DM, Classification

URI

https://hdl.handle.net/11511/58143

DOI

https://doi.org/10.5220/0009103202180226

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Graduate School of Informatics, Conference / Seminar

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Citation Formats

E. Esgin, “Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System,” 2020, p. 218, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/58143.