Title: A multi-tiered spare parts inventory forecasting system

Authors: Zhuoqing Xie; Hongzhi Xie; Shuhui Liu; Yijing Huang; Xiaolin Deng; Biwei Liu

Addresses: National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China ' Spare Parts Center, China Nuclear Power Operations Co. Ltd., Shenzhen, 516545, China ' Spare Parts Center, China Nuclear Power Operations Co. Ltd., Shenzhen, 516545, China ' National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China ' Spare Parts Center, China Nuclear Power Operations Co.Ltd Shenzhen, 516545, China ' National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Abstract: To achieve intelligent nuclear power management, we propose a multilevel nuclear power spare parts prediction method. We combine qualitative prediction methods with various cutting-edge quantitative prediction methods to forecast the overall inventory of nuclear power spare parts and individual categories, enabling enterprises to minimise costs and prevent stockouts. Specifically, we classified the spare parts according to their usage characteristics and developed a hybrid model that integrates the CNN+BiLSTM and DLinear models (Zeng et al., 2022), taking into account expert opinions for each category. Experimental results show a significant improvement in accuracy compared to traditional methods.

Keywords: inventory forecasting; spare parts forecasting; deep learning; quantitative techniques; multi-layer.

DOI: 10.1504/IJSSC.2023.133248

International Journal of Space-Based and Situated Computing, 2023 Vol.9 No.3, pp.165 - 172

Received: 08 Jun 2023
Accepted: 27 Jun 2023
Published online: 03 Sep 2023 *

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