## 4 Results  
### Quantitative Study via Process Mining  
Figure 3A shows the process flow for 95.6% of activities and 81.3% of connections. Figure 3B, in turn, shows a diagram for 100% of activities and 83.9% of connections. This \begin{table}
\begin{tabular}{c c c c c c} \hline
**ID** & **Dealership\_ID** & **Mileage** & **Activity** & **Actor** & **Timestamp** \\ \hline O12\_60 & O\_02 & 10,022 & Reception check-out & Service advisor & 27 March 2015 13:55 \\ O12\_60 & O\_02 & 10,022 & Repair end & Technician & 27 March 2015 13:00 \\ O12\_60 & O\_02 & 10,022 & Part transfer to & Parts advisor & 27 March 2015 12:04 \\ O12\_60 & O\_02 & 10,022 &
\begin{tabular}{c} workshop \\ Repair start \\ \end{tabular} & Technician & 27 March 2015 11:00 \\ O12\_60 & O\_02 & 10,022 & Service reception & Service advisor & 27 March 2015 9:41 \\ O12\_60 & O\_02 & 10,022 & Repair order printed & Service advisor & 27 March 2015 9:40 \\ \hline \end{tabular} Source: own compilation using Excel for Mac version 16.82 (Office 365) based on the author’s own study.  
\end{table} 
Table 3: Exemplary activities in the event logs database used in the study. Figure 2: Structure of the data in dealerships in the automotive sector. Source: Based on the work of [30]. group is discernable by activities involving repairs requiring the delivery of parts (part transfer to the workshop) and computer-diagnostics implementation (diagnosis test start and diagnosis test end). The selection of the number of activities and connections was adjusted, to increase the readability of the map and extend the analysis, in terms of the potential of ChatGPT implementation, by adopting the parameter of process-execution time. Moving forward, the focus was shifted to the overview process. Attention was first drawn to the happy path (Figure 4). In the case under investigation, the process-mining term 'happy path' refers to the most common flow of the after-sales process events. A happy path (happy flow) is the sequence of the activities expected to occur in a process, performed without any deviations or interruptions, and represents the optimal path through a given process under study [31]. Only 45.68% of cases exhibited a happy path, which accounts for 185 of the 405 cases under examination, with happy path throughput time = 11 days. Figure 4: Happy path of the studied after-sales process in the organizations studied, N = 10. Source: own compilation using CELONIS SNAP. Figure 3: The course of the investigated after-sales process. Source: own study using CELONIS SNAP. The happy path shown in Figure 4 does not include such activities as part transfer to the workshop (in 14% of cases, 55 events), repair order printed (in 14% of cases, 55 events), or diagnostic test start and end (12% of cases, 49 events). Based on the happy path, the optimal flow of events in an after-sales process was identified. On this basis, an attempt was made to identify the areas of improvement with the use of the ChatGPT tool. The business-process parameter factored in was throughput time. Subsequently, an attempt was made to analyze the throughput time (TPT) of the process under study. Since both the date and start time of activities were included in the event logs database, the values in the graphs are given in hours. Drawing on the sample of 405 repairs, the median throughput time, from the beginning to the end of the process (i.e., from the acceptance of the customer's request to the submission of the warranty claim to the manufacturer's system) was found to be 114 [h]. It is worth noting here that an active claim should be equated with the activity of issuing the customer an invoice. Within the set of the processes under examination, focus was given to those the execution of which exceeds 114 [h]. Figure 5 shows the characteristics of the process investigated, from the perspective of throughput time analyses, and so the average = 239 [h], maximum = 9928 [h], and minimum = 8 [h]. A middle value (median) was also adopted in the study.