# Service Process Modeling in Practice: A Case Study in an Automotive Repair Service Provider
## 5 Results and Discussion
### Positive Impact on Financial Performance  
This subsection presents the results of the analysis of financial data extracted from the organization's electronic management system, focusing on the impact of process improvements on financial performance. The data analyzed included revenue from service and parts sales, operational costs, and average billed hours per shop order.  
Analysis of financial data shows that by optimizing processes and increasing efficiency, an increase in the number of hours billed and revenue generated from the sale of parts and services was achieved. Service advisors trained to offer and sell additional services can contribute to increased revenue, as shown in this paper.  
Eliminating inefficiencies and downtime has led to a reduction in operational costs, contributing to increased profitability. For example, the implementation of an automated working time monitoring system can help to identify and eliminate non-value-adding activities.  
As illustrated in Figures 6-9, the positive evolution of average billed hours per shop order demonstrates the direct impact of process modeling on performance. These figures show a significant increase in the average number of hours billed per shop order in 2023 compared to 2022, especially in the first semester. This increase indicates better utilization of resources and increased efficiency in service processes.  
Figures 6-9 illustrate the evolution of invoiced hours, a key indicator of service department performance. In these figures, the \(X\)-axis represents the months of the year, while the \(Y\)-axis indicates the average number of hours invoiced per service order or the total invoiced hours. The bars are color-coded to distinguish between the data from 2022 (blue) and 2023 (orange), allowing for a direct comparison of performance across the two years.  
Figures 6-8 specifically show the average number of hours invoiced per order for each vehicle type (passenger cars, commercial vehicles, and damaged vehicles, respectively). An increase in this metric from 2022 to 2023 suggests improved efficiency in service delivery, as technicians are, on average, billing more hours per service visit. This could be attributed to factors such as better diagnostic procedures, optimized repair workflows, or increased sales of additional services.  
Figure 8: Average invoiced hours per order for damaged motor vehicles, comparing 2022 (blue) and 2023 (orange), measured in hours.  
Figure 6: Average invoiced hours per order for motor service, comparing 2022 (blue) and 2023 (orange), measured in hours.  
Figure 7: Average invoiced hours per order for commercial motor vehicles, comparing 2022 (blue) and 2023 (orange), measured in hours.  
Figure 9 presents the total invoiced hours for all service types combined. While it shows a slight decrease in 2023 compared to 2022, it is crucial to consider this in conjunction with the average hours per order. As observed in Figures 6-8, the increased average invoice value may compensate for the reduced volume, indicating a shift towards higher-value services.  
The positive evolution of average billed hours per shop order, illustrated in Figures 6-8, demonstrates the direct impact of process modeling on performance. It is important to note that the data presented in Figures 6-9 represent the first six months of 2022 and 2023. These figures show a significant increase in the average number of hours billed per shop order in 2023 compared to 2022, especially in the first semester.  
It is important to note that these figures reflect the performance of a single service provider organization and may be influenced by specific local market conditions and operational factors. Further research involving a larger sample size could provide a broader perspective on these trends.