Many RPA technology vendors and promoters suggest that the Bots can be used without much analysis upfront. However, this reduces the business impact and can create many of the up and downstream challenges, as discussed before. As in any improvement initiative it is important to understand the current business process context to identify improvement opportunities and create a baseline for the expected value and the business cased based thereon. To avoid key challenges of RPA it is important to understand the end-to-end process and the expected effects of RPA. Since RPA touches all dimensions of a process, they all need to be included in the analysis, using methods like BPMN 2.0 (Fisher, 2012): organisation (roles, departments, etc.), data, functions, process control flow, process deliverables and underlying technology (Scheer, 1998) (Kirchmer, 2017a) (Vivek, 2018). To realise the full potential of RPA, the analysis has to answer specifically questions like the following:

  • Which roles can be eliminated or re-directed? This allows the calculation of efficiency effects, especially cost reduction but also potential quality improvements by avoiding mistakes.
  • Which functions of remaining roles can be eliminated? This supports value identification in the same way discussed above.
  • How is the cycle time effected? This provides information about increased agility, leading, for example, to better customer or supplier experience.
  • Can the use of existing software systems be discontinued, or the development of complex system interfaces be avoided? This helps to evaluate technology cost reductions and increased systems performance.

The RPA related process analysis requires in a second step a deeper examination of the process components that will be automated. A move of the specification from pure business aspects to high level systems related activities is required: the documentation and analysis of every field that is read and entered by current users that may be replaced by a bot (Scheer, 2018). This creates the foundation for the evaluation of the detailed requirements for the use of RPA.

The analysis can be automated itself, at least partly, through process mining approaches (Scheer, 2018) (Kirchmer, 2017a). Since most of the processes, that are good candidates for RPA, are already supported through conventional application software, process mining systems can examine the systems log files to deliver process analytics or even models of specific process instances. Process mining tools are excellent complements to RPA tools in analysis but also during the later governance and continuous improvement of those processes.

A focused process simulation is often helpful to properly understand the current execution effort, cycle times and major bottlenecks. This simulation can then be used to demonstrate the impact of using RPA to automate certain steps in the process, highlighting the overall impact on the process performance. The characteristics of processes suited for RPA, especially the degree of automation, simplify the data collection to enable a process simulation.


The as-is process analysis is illustrated in figure 8. The analysis allows a simplification of the processes before transforming it using RPA as enabler. This simplifies RPA deployment and change management as well as the following value realisation.

Figure 8: Value-baseline through As-Is Process Analysis


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