Abstract
The quantitative evaluation of the importance degree of spare parts is essential as spare parts’ maintenance is critical for inventory management. Most of the methods used in previous research are subjective. For this reason, an accurate method for the evaluation of the importance degree combining an improved clustering algorithm with a back-propagation neural network (BPNN) is proposed in the present paper. First, we classified the spare parts by analyzing their historical maintenance and inventory data. Second, we evaluated the effectiveness of classification using the Davies–Bouldin index and the Calinski–Harabasz indicator and verified it using the training data. Finally, we used BPNN to determine the training data necessary for an accurate assessment of the importance degree of spare parts. The previous importance evaluation methods were susceptible to subjective factors during the evaluation process. The model established in this paper used the actual data of the company for machine learning and used the improved clustering algorithm to implement training and classification of spare parts data. The importance value of each spare part was output, which additionally reduced the impact of subjective factors on the importance evaluation. At the same time, the use of less data to evaluate the importance of spare parts was achieved, which improved the evaluation efficiency.
Highlights
Spare parts management [1,2,3] is an important part of inventory management
With the development of science and technology, components have became more and more complex, the types and quantities of parts are increasing, and the cost of ordering and storing has increased. erefore, how to carry out targeted inventory management of spare parts has important research significance. is article proposes a concept to make corresponding inventory strategies based on the importance degree of spare parts. erefore, how to achieve accurate, rapid, and objective importance degree assessment is of great significance to the enterprise because it helps to arrange spare parts inventory
From the general procedures of the evaluation model, in the first stage, the PCA method is used to deal with data dimension reduction, and the sample dataset is autonomously divided into different classifications by the improved clustering algorithm, and the rationality of the classifications is evaluated using the DB and CH indices, and the data processed by the improved clustering algorithm is used for the neural network learning and training data. e second stage constructed a back-propagation neural network (BPNN) evaluation model; the model was used to predict the importance degree of spare parts by learning the historical data after processing. e third stage verified the effectiveness of the proposed evaluation method by comparing examples with the sample importance degree
Summary
Spare parts management [1,2,3] is an important part of inventory management. It has a significant impact on overall business and cost control. E Davies–Bouldin (DB) and Calinski–Harabasz (CH) indices are being used to evaluate the classification effect This method can reduce the influence of subjective human factors and can be used to evaluate the importance degree in a timely, rapid, and accurate manner when new repair parts enter the inventory system. Erefore, the present paper proposes an evaluation method based on the clustering algorithm and BPNN without considering subjective factors to assess the importance degree of maintenance spare parts. E present proposed evaluation method has the following advantages: (1) it quantifies the importance degree of spare parts directly from raw data, without considering expert experience, and (2) when new spare parts enter the inventory system, it evaluates their importance values quickly and efficiently according to the corresponding historical data. Extracted data for the learning of the neural network. e purpose of the cluster is to make the data sample types at the input end of the neural network hierarchical, and the spare parts type coverage is complete, reducing prediction errors
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