## 2 Related Research  
The use of virtual models in a computer simulation to simulate production floor is not novel in the literature. Factory simulation allows experiments to be conducted efficiently to test different scenarios and process capabilities, reducing the need for costly and time-consuming physical experiments. Such physical experiments are also difficult to conduct directly online [11]. To run a simulation of the production parameters, one of the most popular tools is Tecnomatix Plant Simulation by Siemens Digital Industries Software, which is a discrete simulation program, event-controlled, and object-oriented [11; 12; 13].  
Many researchers have used the tool to optimize manufacturing systems, for example, [PERSON] et al. [13], who analyzed resource availability, user allocation, and throughput in a conventional manufacturing factory floor [14; 15]. [PERSON] et al. presented a simulation for planning an assembly system, with the goal to improve workflow and remove interruptions [16]. Tecnomatix has also been successfully used to evaluate machine utilization and production efficiency [17] and energy-related considerations towards the creation of a digital factory [18]. Other applications include a detailed analysis of production process and the detection of bottlenecks [19]. Moreover, [PERSON] et al. used a simulation tool to early compare different production plans, verifying the accuracy of the different solutions before realization [20].  
Regarding cost models, especially for AM, different examples can be found in the literature, as discussed in [21]. Most of them focused on a single aspect of the AM process (typically the part generation by the machine), without necessarily covering a holistic view of the full process chain from design to deployment. In the field of injection molding tool inserts' manufacturing, a cost model and comparison between the costs of AM- and CM-based process chains was presented in [22]. [PERSON] et al. analysed previous cost models for AM and presented a new model named MiProCAMAM (mixed production cost allocation model for additive manufacturing) [23]. The model consists of five phases: preparation, build job, setup, building, and removal. The model calculates the production costs starting from the process and the geometry of the part, considering the design freedom given by AM. Process times and performance are evaluated by estimating the build time. A similar structure is presented in this paper.  
Other techniques can be applied to conduct cost estimations and to collect the data required for the technology and cost model. For example, [PERSON] et al. [24] identified four classes of data required for such a cost analysis. These are qualitative--intuitive (based on experience), qualitative--analogical (based on historical data), quantitative--parametric (defined from a variable), and quantitative--analytical (using enterprise resource planning). Qualitative data are easier to use and are helpful in early decision-making; however, they can sometimes lack accuracy. Intuitive data rely on the experience of skilled specialists, but they make it possible to run an early evaluation when little or no stored data exist. Intuitive and analogical data are the types mainly used in this research. Extra input data were obtained from the literature data, where necessary and available. Obviously, a proper and reliable data collection plan is vital for the success of such a project, as the quality of the input data will affect the quality of the simulations and the results of the cost model. The great variety of applications of AM shows the importance of having a reliable method for an accurate manufacturing methods' comparison, including a more detailed business model and cost-benefit calculation tools, as reported in the review in [25].