## 2 Results of Literature Analysis
### Content Analysis
#### 2.2.2 Process  
Based on the content in the literature, research on the SC process for AM can be divided into two main groups: (i) industry-based processes and (ii) decision-making processes, as shown in Figure 2. The discussion of each sub-process is provided below. Development and Distribution Processes  
* Manufacturing  
An analysis of AM application in COVID-19-related healthcare is provided by [PERSON] et al. [52]. The authors analyzed the cost components for 3D printers, printer maintenance, raw material, labor, overhead, and sterilization, and mentioned that the majority of healthcare products could be produced via additive manufacturing with equipment that is available on the market. [PERSON] et al. [53] identified the operational details of an AM process to analyze the total costs of material, energy, operator, machine maintenance, annualized machine depreciation, and on-time delivery. The authors observed that the AM operation is dependent on the size of the spare part, and in some cases, they may not be able to financially compete with the conventional method of parts supply. [PERSON] et al. [6] also mentioned that the performance of AM is better because of the longer set-up times required for CM. Therefore, if there are continuous requirements for the spare parts, the CM-based setup does not need to change, so in terms of setup time alone, CM may be more advantageous. However, there are other factors such as spare parts inventory requirements, the ease in obtaining and storing raw materials, and easier production of available digital files for the parts. [PERSON] et al. [71] analyzed different dimensions of automotive SC integration, performance, and firm performance from the perspective of additive manufacturing adoption. The authors found that the adoption of AM improves SC and firm performance. However, the authors mentioned that adopting AM technology alone would not create this improvement; it will also have to focus on the integration of AM with the existing SC activities. [PERSON] [23] mentioned the opportunities for digital spare parts production by considering three aspects: demand production, speed of production compared to CM, and digital storage. The author mentioned that the limited types of 3D printers, the volume of production, integration of the IT system, and post-processing problems are some barriers to the adoption of AM in the industry. A review of the paper shows opportunities for AM, but there could be problems with the wider acceptance of AM as it requires a continuous flow of demand so that the spares can be developed on a continuous basis. If the spare parts are more durable, investments in AM may not increase cost efficiency, although it will reduce lead time requirements and increase production efficiency. * Inventory Control  
[PERSON] et al. [53] also focused on a discrete event simulation model that involves spare parts backordering, inventory replenishment, and order evaluation to assess the inventory for an AM-based manufacturing system. The model considers costs in terms of operator cost, inventory-carrying cost, fixed ordering cost, replenishment shipping cost, fixed warehouse cost, and penalty costs for late delivery. [PERSON] et al. [6] developed a minimization problem and mentioned that comparing the inventory performance of AM is better than that of CM because of the lower stock level with AM. As there is already a facility to produce spare parts with AM, the stock levels in AM are lower than those in the CM. The on-site inventory level was studied by [PERSON] [55] by considering the cost and inventory for introducing AM for spare parts production in remote locations. The author suggested an optimal inventory policy that helps determine when to print a part and when to wait for its scheduled replenishment. The impact of additive manufacturing on inventory performance was also investigated by [PERSON] [28]. The author mentioned that the adoption of AM has the potential to reduce supply risk and result in better inventory performance and management.