## 2 Materials and Methods
### Methodological Procedure  
The methodological procedure was split into three steps, as presented in Figure 2.  
The study's initial phase was conducted through on-site visits to the cooperative, focusing on data collection, process comprehension, and time measurement through discussions with employees. Then, historical data was gathered on truck arrivals, waiting times, and lead times. To mitigate potential biases, special care was given to consulting with the staff and ensuring cooperative representation. Staff consultations were conducted separately from data collection to prevent external influences on individual employee responses. Additionally, data was collected from various sites within the cooperative to provide a more comprehensive and unbiased assessment of its performance, reducing the risk of concluding based on a single site's characteristics. Following the data collection phase, the study embarked on the development of multiple scenarios to support the simulation process. Given the innovative nature of the machine intended for injury classification, the absence of prior research or studies  
Figure 1: The soybean seed classification process. Figure 2: Methodological process. elucidating its logistical impact on system performance necessitated the generation of artificial data through simulation. Within this context, the cooperative outlined three options for staffing levels and four for potential machine quantities, encompassing 1, 3, or 5 dedicated workers and 0, 1, 3, or 5 machines available for acquisition. Subsequently, the central objective revolved around determining the workforce composition (1, 3, or 5 employees to be hired) and machine deployment (0, 1, 3, or 5 machines to be purchased). This consideration involved exploring various permutations of the number of machines (\(m\)) and workers (\(w\)) while ensuring that \(m\leq w\). In situations where there were more workers than machines (\(m<w\)), it was assumed that workers could simultaneously participate in the analysis process alongside the machines, eliminating the necessity for idle waiting periods. Moreover, it was firmly established that each worker could manage a single truck at a time, with the stipulation that once a worker initiated service for a truck, the process would proceed without interruption. In the second step, a computer simulation model was designed to evaluate all the described scenarios. A Discrete Event Simulation using AnyLogic University v.8.5.2 was developed. As presented in Figure 3. The truck arrivals were input according to the historical data since 2017 provided by the cooperative. The simulation can explore different scenarios mixing real and virtual data, generating useful results for decision-making [53]. The simulation inputs are presented in Table 1. \"Workers\" and \"Machines\" determine the quantity w of workers and m machines available for the process. The values for both can be 1, 3, or 5 available workers/machines for future scenarios (as suggested by the decision-makers). \"Worker Wait\" is a Boolean parameter. When \(m<w\), \"Worker Wait\" takes \"True\" if the worker waits for a machine to finish the task to use it or \"False\" if the worker executes the task in parallel. \"Human Time\" and \"Machine Time\" are the lead times for a human or a machine, respectively, to execute the task. \"Truck Arrivals\" are all the truck arrivals in the system. The case of simulating different scenarios by varying the number of workers without including the machine was also considered. In this case, 0 machines and 1, 3, or 5 workers were considered. It is important to note that this scenario does not directly reflect the real-world situation. In the simulated scenario, it is established that the workers must perform the classification manually with a lead time of 195 or 615 s, which is not currently the case in the real world where the \"Injury Classification\" is not even performed.