Multi-dimensional classification of wind turbine spare parts in a multi-echelon inventory system
by Bin Yan; Yifan Zhou; Zhaojun Li; Chaoqun Huang; Jingjing Liu
International Journal of Applied Decision Sciences (IJADS), Vol. 16, No. 2, 2023

Abstract: Optimising inventory management of spare parts is important for wind power companies to reduce operation and maintenance (O&M) costs. We summarise the indicators for wind turbine spare parts classification by analysing O&M management characteristics of the wind power industry. Spare parts of wind turbines are classified based on three dimensions: value, demand, and importance. The existing multi-dimensional spare parts classification methods discretise the indicator on each dimension. However, we use the K-means algorithm to classify spare parts based on normalised indicators. The proposed classification method significantly decreases the reliance on expertise and information loss caused by indicator discretisation. The proposed multi-dimensional classification method is validated using a practical case study of wind turbine spare parts classification, demonstrating that the proposed method can obtain reasonable classification that simultaneously stabilises the service level and reduces inventory costs.

Online publication date: Fri, 10-Mar-2023

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