Title: Multi-dimensional classification of wind turbine spare parts in a multi-echelon inventory system
Authors: Bin Yan; Yifan Zhou; Zhaojun Li; Chaoqun Huang; Jingjing Liu
Addresses: School of Mechanical Engineering, Southeast University, 2 Southeast University Road, Nanjing, China ' School of Mechanical Engineering, Southeast University, 2 Southeast University Road, Nanjing, China ' Department of Industrial Engineering and Engineering Management, Western New England University, 1215 Wilbraham Road, Springfield, USA ' Beijing Goldwind Science and Creation Windpower Equipment Co. Ltd., 19 Kangding Street, Beijing, China ' Beijing Goldwind Science and Creation Windpower Equipment Co. Ltd., 19 Kangding Street, Beijing, China
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.
Keywords: multi-echelon inventory; wind turbine spare parts; normalisation; multi-dimensional classification.
International Journal of Applied Decision Sciences, 2023 Vol.16 No.2, pp.189 - 209
Received: 07 Nov 2021
Accepted: 17 Dec 2021
Published online: 10 Mar 2023 *