## 2 Results of Literature Analysis
### Content Analysis
#### 2.2.2 Process
Many approaches were applied to adopt AM in spare parts SC. Most existing studies rely on qualitative, analytical, and optimization analyses [19]. The research focuses on empirical studies, case studies, and scenario analyses to study the implementation of AM in spare parts logistics. However, the review shows that the most utilized approach is the quantitative modeling method. As quantitative modeling and computer-based tools are becoming important for decision making in companies, their utility is also widely spreading for AM-based SC decisions [73]. Based on the content of these methods, the two main categories considered for quantitative models are optimization and simulation modeling. Optimization models can be used for many purposes, including supporting decisions for resource allocation, making span minimization in scheduling problems, and studying the effect of resupply time on inventory performance. Mixed-integer linear programming(MILP) and the Markov decision process (MDP) are other approaches utilized among optimization models. MILP is used because of its robustness [38]. Table 3 provide an overview of the optimization model structure in terms of the approaches and tools utilized in the optimization process, the objective of the model, parameters, number of stages, and nature of the planning horizon. This shows that all models deal with time and cost minimization. Table 4 also illustrate the simulation modeling in terms of the approaches and performance metrics. The models were developed for either a single time or multiple time periods. In general, only one period is considered as the planning horizon for optimization models. [PERSON] [55] studied an infinite time horizon to investigate on-site additive manufacturing for the Royal Netherlands Army. The analysis showed that AM spare parts were the best solution to avoid stock-out between the two replenishment periods. However, at the end of an order cycle, whenever the conventionally manufactured parts arrived, the AM part was immediately replaced and disposed of as the reliability of AM parts was assumed to be inferior, as they are less resistant to cyclic loading. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Approach** & **Performance Metrics** \\ \hline \multirow{2}{*}{[PERSON] [27]} & \multirow{2}{*}{SD} & Inventory level and inventory cost \\  & & Time horizon for the simulation \\ \hline \multirow{2}{*}{Li [29]} & \multirow{2}{*}{SD} & Costs (transportation, manufacturing, administrative, and inventory) \\  & & Carbon Emission Sources (raw material production, material powder \\  & & production, manufacturing, material transportation, \\  & & product transportation) \\ \hline \multirow{2}{*}{Zhang [53]} & \multirow{2}{*}{Discrete Event Simulation} & Costs (material, energy, operator, penalty, maintenance, AM, parts) \\  & & Interarrival time \\ \hline \multirow{4}{*}{Caldas [5]} & \multirow{2}{*}{Discrete Event Simulation} & Average Service Level \\  & & Average Lead Time. \\ \cline{1-1}  & & Costs (Fixed \\ \cline{1-1}  & & Production and supplying Inventory \\ \cline{1-1}  & & Stockouts) \\ \hline \multirow{2}{*}{[PERSON] [19]} & \multirow{2}{*}{Discrete Event Simulation} & Sojourn time (queue, manufacturing time, and logistics time) \\  & & Total costs (penalty, machine, and logistics) \\ \hline \multirow{2}{*}{Xu [37]} & \multirow{2}{*}{Discrete Event Simulation + Agent-based Simulation} & Order fulfilling lead time \\  & & Order fulfilling average cost \\  & & Proportion of cannibalization \\ \hline \multirow{2}{*}{[PERSON] [54]} & \multirow{2}{*}{Monte Carlo Simulation} & System average \\  & & lead time \\ \hline \multirow{2}{*}{[PERSON] [31]} & \multirow{2}{*}{Monte Carlo Simulation} & Turnaround time \\ \hline \multirow{2}{*}{[PERSON] [22,34]} & \multirow{2}{*}{Monte Carlo Simulation} & Occurrence of expected spare parts demand \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Approaches and Performance metrics for simulation modeling.