# Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method
## 1 Introduction
[PERSON] [18] also utilized BP and GA learning methods in ANNs for reliable, accurate inventory classification. [PERSON] [19] adopted an SVM based on a Kraljic three-dimensional (3D) model for enhanced quality-oriented material classification accuracy. [PERSON] [20] integrated a fuzzy neural network and decision tree for spare parts classification in terms of actual demand in a steel enterprise with multiple attributes. [PERSON] [21] constructed a novel kNN model wherein the k value can be optimized automatically to enhance the efficiency and accuracy of classification. Models based on intelligent or heuristic algorithms can process high-latitude and nonlinear sample data, but lack the rules explanatory in a degree. Thus, for the existing spare parts classification methods, there may be some persistent problems summarized from the following three aspects:  
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2. Visualization: Neither multi-criteria nor traditional ANN models can provide decision-makers with intuitive, visually interpretable data. 3. Rules explanatory: Data-driven artificial intelligent algorithms proceed in a \"black box\", where rules are not explained and results are relatively uninterpretable, despite their certain learning ability. 4. Learning ability: While there are understandable variable-selection and structure-construction processes in rule-based methods, their learning ability and generalization are relatively poor. Therefore, a novel approach based on hierarchical classification and the Convolutional Neural Network (CNN) will be developed in this study to resolve the above problems. The proposed model combines the advantages of both hierarchical structure and CNN. Hierarchical classification serves to construct a criteria structure based on the causal relationship of multi-criteria, which makes for an explanatory rule-setting process. Moreover, the multiple criteria are distributed on the subspace by reducing the hierarchy in order to reduce the restriction of the model on the number of criteria. As described by [PERSON] and [PERSON] [22], the hierarchical structure shows the causal relationship between criteria and makes it possible to consider as many variables as demanded. [PERSON] [23] createda hierarchical multi-criteria spare parts classification method and verified it in an Italian household appliances manufacturing company. With the constructed structure, the criteria can be colored for specific data features, which transfers the classification into an image-identification problem. The CNN is a deep learning method [24; 25; 26] for image identification and classification, and several networks based on CNN have been explored in the past years, such as LeNet, AlexNet, VGG (Visual Geometry Group)-Net, ResNet and DenseNet. [PERSON] [27] proposed a CNN-based symbolic hierarchical cluster method for operation trend prediction problems, where the CNN structure is used to classify trends. [PERSON] [28] used AlexNet to classify different disturbances in a 3D-printing heating map process. All developed networks from CNN have their own excellent specifics. The core of ResNet model is to establish skip connection between the front layer and the back layer, which is helpful in relieving the gradient problem by residual fitting in the back propagation of training process [29]. DenseNet is mainly distinctive in the dense connection and feature reuse that improve the calculation capacity [30]. However, AlexNet is a deep CNN structure which can solve gradient problem better with the Relu function. Additionally, both dropout and GPUs (Graphics Processing Units) technology are adopted to improve the calculation capacity when considering the overfitting problem [31]. This paper is organized as follows: Section 2 introduces the deep CNN principle, which is the basis of the proposed algorithm. The proposed G-DCNN method and its stepwise operation process are presented in Section 3. Section 4 reports the case study conducted to validate the proposed method, as well as a comparison between the proposed method and other similar methods.