# Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method
## 4 Case Study
### Hierarchical Structure of Ship Spare Ship Parts Classification
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Criteria** & **Name** & **Description** \\ \hline X\({}_{1}\) & Average lead time & The time between order replacement and arrival of the demanded spare parts. \\ X\({}_{2}\) & Number of the suppliers & The number of suppliers that can supply some kind of spare parts. \\ X\({}_{3}\) & System monitoring level & Assessed by the maintenance manager who has long experience on the ships. \\ X\({}_{4}\) & Criticality & Assessed by the maintenance manager who has long experience on the ships. \\ X\({}_{5}\) & Annual consumption & Calculated from the historical data of past spare parts usage. \\ X\({}_{6}\) & Average unit cost & The spare price. \\ X\({}_{7}\) & Safety inventory & A buffer stock prepared to prevent the uncertain factors of future demand. \\ X\({}_{8}\) & Supply reliability & The suppliers’ fulfillment to orders. \\ X\({}_{9}\) & Demand predictability & The predictability of the spare parts demand. \\ X\({}_{10}\) & Out-of-stock risk & The possibility of unavailable spare parts. \\ X\({}_{11}\) & Out-of-stock cost & The penalty cost caused by out-of- stock of the spare parts. \\ X\({}_{12}\) & Annual consumption cost & Calculated by average unit cost multiply annual consumption. \\ X\({}_{13}\) & Inventory holding cost & The cost to hold the safety inventory. \\ X\({}_{14}\) & In-stock cost & Calculated by annual consumption cost plus inventory holding cost. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multiple spare parts classification criteria. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Citeria** & **Scale** & **Level** & **Color Assignment** \\ \hline \multirow{6}{*}{(Day)} & \(0\leq X_{1}\leq 3\) & Short & \\  & \(3\leq X_{1}\leq 7\) & Medium & \\  & \(7\leq X_{1}\leq 10\) & Long & \\  & \(X_{1}\geq 10\) & Extreme long & \\ \hline \multirow{6}{*}{\(X_{2}\)} & \(0\leq X_{2}\leq 2\) & Low & \\  & \(2\leq X_{2}\leq 6\) & Medium & \\  & \(X_{2}\geq 6\) & High & \\ \hline \multirow{6}{*}{\(X_{3}\)} & 1 & Low & \\  & 2 & Medium & \\  & 3 & High & \\ \hline \multirow{6}{*}{\(X_{4}\)} & 1 & Low & \\  & 2 & Medium & \\  & 3 & High & \\ \hline \multirow{6}{*}{\(X_{5}\)} & \(0\leq X_{5}\leq 0.25\) & Extreme low & \\  & \(0.25\leq X_{5}\leq 0.75\) & Low & \\ \cline{1-1} \cline{3-3} (Piece) & \(0.75\leq X_{5}\leq 10\) & Medium & \\  & \(10\leq X_{5}\leq 50\) & High & \\  & \(X_{5}\geq 50\) & Extreme high & \\ \hline \multirow{6}{*}{\(X_{6}\)} & \(0\leq X_{6}\leq 25\) & Extreme low & \\  & \(25\leq X_{6}\leq 200\) & Low & \\ \cline{1-1} \cline{3-3} (Yuan) & \(200\leq X_{6}\leq 1000\) & Medium & \\ \cline{1-1} \cline{3-3}  & \(1000\leq X_{6}\leq 50,000\) & High & \\ \cline{1-1} \cline{3-3}  & \(X_{6}\geq 50,000\) & Extreme high & \\ \hline \multirow{6}{*}{\(X_{7}\)} & \(0\leq X_{7}\leq 10\) & Extreme low & \\  & \(10\leq X_{7}\leq 20\) & Low & \\ \cline{1-1} \cline{3-3} (Piece) & \(20\leq X_{7}\leq 60\) & Medium & \\ \cline{1-1} \cline{3-3}  & \(X_{7}\geq 60\) & High & \\ \hline \multirow{6}{*}{\(X_{8}\)} & 1 & Low & \\ \cline{1-1} \cline{3-3}  & 2 & Medium & \\ \cline{1-1} \cline{3-3}  & 3 & High & \\ \hline \end{tabular}
\end{table} 
Table 3: Level and color assignment of variables. Figure 8: The hierarchical structure of spare part No. 144.