1. Linking perspectives
Now that we have looked at resources, control-flow and time, let's see how we can also combine them in our analysis. But first, let's have a quick recap.
2. Recap
In this chapter we talked about the organizational perspective: who executes the process?
We talked about control-flow and structuredness: how is the process executed?
And we talked about performance and time: when is the work carried out and how long does it take?
For each of these aspects, we used process metrics, which return various numeric results, as well as visuals, which return maps of the process as well as other process-specific graphs, like dotted charts.
While each of these aspects is important in its own right, it is also interesting to look at the links between them and between any additional information we have stored in our data.
For example, is there a relation between the actors working on a case, and the order in which the work is done.
How is the order in which the work is done related to the efficiency in terms of throughput time?
Or are there differences in how different resources plan and execute their work in terms of time schedules?
3. Leveraging granularity levels
A first way to linking different perspectives is by making use of the granularity levels of the metrics. For example, by calculating the processing time at the level of resources, we can link the organizational and performance perspective. By analyzing rework by resources, we can link the control-flow and organizational view.
Alternatively, we might also want to include additional data attributes in the analysis. This can be done by grouping the event log.
4. Grouping data
Incorporating categorical data attributes into the calculation of a process metric can be done using the group_by function, similarly as when working with regular data in the tidyverse.
5. Grouping data
Grouping on a variable will implicitly split up the event log according to different values of that variable.
6. Grouping data
Any process metric which gets calculated for a grouped event log will be calculated for each group individually.
7. Grouping data
The results for each of the group will then be combined in one single output, which can also be visualized using the plot function.
8. Grouping data: Example
This workflow allows us to easily compare different groups of cases. For instance, consider our online learning context. We can compare the behaviour of students from different backgrounds or connect good and bad grades with different process characteristics.
9. Combining elements
Combining all these ingredients allows for a very flexible toolset to perform process analysis.
10. Combining elements
Using the piping symbol, each of the different tools can be easily combined. Because of this flexibility, we can now answer almost every process-related research question you can think of.
11. Let's practice!
In het next couple of exercises, we'll look at some of these multi-perspective analyses.