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

Eren Esgin

Abstract

In the context of intelligent maintenance, spare part prediction business scenario seeks promising returnon-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.

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Paper Citation


in Harvard Style

Esgin E. (2020). Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System.In Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-396-4, pages 218-226. DOI: 10.5220/0009103202180226


in Bibtex Style

@conference{icores20,
author={Eren Esgin},
title={Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System},
booktitle={Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2020},
pages={218-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009103202180226},
isbn={978-989-758-396-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System
SN - 978-989-758-396-4
AU - Esgin E.
PY - 2020
SP - 218
EP - 226
DO - 10.5220/0009103202180226