# Use of Data-Driven Simulation Modeling and Visual Computing Methods for Workplace Evaluation
## 3 Methods
### Simulation Modeling  
Captured data from visual computing methods and real-world data provided by the company form the basis for building a simulation model. Real-world data provided by the company were primarily used in a reference simulation scenario; these data were numerically determined when designing the workplace within the company. The data describe the process time of manual assembly, the process time of the automated test phase and the desired quantity of manufactured products. The simulation model is designed in the software environment Simio ([[https://www.simio.com](https://www.simio.com)]([https://www.simio.com](https://www.simio.com))), which enables the development, experimentation, and evaluation of data-driven simulation models. Figure 5 shows a simulation model of three simulation scenarios, which are used to estimate the possibility of increasing the number of finished products on the manufacturing assembly line. Simulation scenarios are defined as  
* The reference simulation scenario (S\({}_{\text{R}}\)), which represents the current state of the manufacturing system. The manufacturing process produces on average 1250 pieces/shift according to the numerical values determined by the visual computing recorded values. The average process time of the manual assembly process is 30 s. Using the normal distribution, the deviations of realistic processing time in the simulation experiment are between 27.5 s and 33 s. The normal distribution function is generated according to the results of the numerical evaluation of the visual computing workplace evaluation. The processing time of the testing stage is a constant 25.5 s. S\({}_{\text{R}}\) is marked in blue in Figure 5.
* The first simulation scenario (S\({}_{1}\)) includes an additional manual assembly workplace and one automated testing stage. The processing times of the operations are the same as for S\({}_{\text{R}}\). In Figure 5, S\({}_{1}\) is marked in brown. * The second simulation scenario (S\({}_{2}\)) follows the guidelines of Industry 4.0, as it introduces a collaborative robot at the manual assembly workstation that can handle a maximum load of two test operations with a process time of 12.75 s, and process scrap rate reduction from 8% to 2.5%. The process time of the robot was determined based on a simulation study carried out in the software environment of the intended robot workstation manufacturer. The service of the  
Figure 5: Data-driven simulation model in Simio presenting three simulation scenarios. robot, the workplace preparation and the random control of the manufactured parts by the robot assembly, is carried out by an employee who can supervise three collaborative workplaces in one shift. In Figure 5, S\({}_{2}\) is highlighted in gray. The simulation model runs with three-shift working from Monday to Friday, with a shift rated time (maximal usable time) of 415 min for a worker and 470 min for a robot. The usable shift time includes the breaks and workplace preparation times of the workers, the shift handover, and the cleaning times at the workplace. A one-year manufacturing process runs in 264 working days. The assumptions in the simulation model assume that the input material is always available in sufficient quantities and that the finished products are brought to the warehouse or promptly sent to the customer. The simulation model manufacturing process operation assumptions are defined as follows:  
* Product assembly workplaces follow a pull operation system. The input sequence of assembly products is released based on customer demands from the enterprise resource planning system. * Manually operated forklifts, when requested automatically, take the input material handling process of the required assembly parts to the input buffers. The simulation model assumes that the input buffer is never empty. Final assembled products are transferred to the enterprise workhouse and shipped to the customer according to the just in time methodology.