# Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method
## 4 Case Study
### Hierarchical Structure of Ship Spare Ship Parts Classification  
There are three main categories of classification criteria most commonly used in cases such as: (1) the inherent properties of the spare parts (criticality and cost); (2) consumption characteristics including failure rate and inventory turnover; (3) supply chain characteristics (lead time and supply reliability). Fourteen criteria are selected for this case study under advisement of the spare parts manager to determine the final class of spare parts: X\({}_{1}\) (average lead time), X\({}_{2}\) (number of suppliers), X\({}_{3}\) (system monitoring level), X\({}_{4}\) (criticality), X\({}_{5}\) (annual consumption), X\({}_{6}\) (average unit cost), X\({}_{7}\) (safety inventory), X\({}_{8}\) (supply reliability), X\({}_{9}\) (demand predictability), X\({}_{10}\) (out-of-stock risk), X\({}_{11}\) (out-of-stock cost), X\({}_{12}\) (annual consumption cost), X\({}_{13}\) (inventory holding cost), X\({}_{14}\) (in-stock cost). Table 1 provides further description of the 14 criteria. All the 14 criteria are collected based on expert knowledge and the literature in view of inventory, supply, and technology information. There are seven input variables in this case, as shown in Table 1, wherein X\({}_{1}\) (average lead time), X\({}_{2}\) (number of suppliers), X\({}_{5}\) (annual consumption), X\({}_{6}\) (average unit cost), and X\({}_{7}\) (safety inventory) are quantitative values gained from the actual dataset while X\({}_{3}\) (system monitoring level) and X\({}_{4}\) (criticality) are qualitative values assessed by the spare parts manager based on their personal experience. The hierarchical structure of spare parts classification in the ship system is constructed here according to both causal relationship and expert knowledge. As shown in Figure 7, the relationship of multiple criteria can be explained clearly. For criteria, they can be scaled into different levels according to the data feature space. The original structure and distribution of criteria data for the spare parts are shown in Table 2.  
In the hierarchical structure, the variable relationship is constructed into layers and a final level emerges that is determined by the inventory cost and out-of-stock cost. The target output of hierarchical structure falls into four classes: A, B, C and D. The spare parts in class A are always highly expensive to keep in stock but are seldom needed. These out-of-stock spare parts will bring about the system heavy losses. Thus, the corresponding component is focused on closely monitoring and prognosis. The spare parts in class B are also highly expensive to keep in stock but not as much as class A, and the replacement of spare parts in class B happens more frequent. It is often strategic to decide the inventory amount of B spare parts and guarantee supplies. The spare parts in class C are always those that are used as part of normal consumption of scheduled maintenance that are ordered in regularly. They are neither expensive nor difficult to obtain when necessary. Spare parts in class D can be the daily consumption items that have little influence on the operation.