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19 July 2017

RPA Success with Process Mining in 4 Steps

For the past couple of years, Robotic Process Automation (RPA) has been a hot topic amongst business process experts. Many companies have begun to look into RPA as a tool to improve their operations, but what exactly does it bring to the table? In this blog post, we find out what is Robotic Process Automation and what limitations it has, while also presenting process mining as the solution for the successful implementation of RPA.

 

What is RPA?

The hot topic everybody is talking about…

Robotic Process Automation offers an approach for automating manual tasks in business. The focus of RPA is to carry out these tasks automatically on the existing software front-end.

RPA vs Traditional Workflow Automation

Traditional workflow automation operates with structured data, uses data integration methods and heavy scripting to achieve its goals. With RPA, the focus is on automating tasks that handle unstructured data, and it aims to operate on the same level as an end-user would: The repetitive high-volume processes are streamlined, manual work is automated, and your experts’ time is saved for more profitable work.

RPA makes operations accurate as software robots repeat the task with pinpoint accuracy and efficiency and they are scalable in accordance to the demand. All these actions produce data that can be used for analyzing performance. Gartner has pointed out, however, that while RPA has a clear definition, the offerings on the market are varied and do not necessarily represent the proper definition.

How to Get Started with RPA?

Gartner recommends that organizations scope out possible points of application before starting to automate processes. In order to deploy RPA most efficiently, it is first key to pinpoint the areas and processes where automation would have a huge impact.

This is where process mining comes into the picture.

 

What is Process Mining?

Process mining refers to the method of using data gathered from information systems for analysis.

As a lot of data is produced by information systems and stored as logs, a lot of unused data lies dormant. This data contains valuable information on how each step of the process is carried out and what does it relate to. This creates a venue for process mining tools to show their worth.

How does Process Mining Support RPA Implementation?

By using modern data mining methods, this unused process data is made useful as they are loaded into a system that enables process BI, such as QPR ProcessAnalyzer. The data contains exact information on how a process is carried out in the real world. It also tells us what kind of lead times are common and how does it compare to the KPIs set for our process. 

Above all, process mining allows you to get a clear overview on how the process is actually occurring in your systems and thus enables you to take control of the process by clarifying points that need improvement.

 

A Successful RPA Project Enabled by Process Mining in 4 steps.

The core target of a RPA project is to improve efficiency and reduce costs by automating manual process steps.

Automating processes cannot be initiated without understanding what are the most profitable areas for automation, and if processes are currently in a suitable state for an RPA project. Organizations need to understand that RPA is not a silver bullet of success, if the foundation for such a project is not fertile. Moreover, the return on the investment is qualified based on whether the correct processes that are high volume and repetitive were selected for robotic automation.

 

1. Understand Processes, Deviations, and Variations.

“Improvement cannot be done without understanding the current state”. This applies for process improvement – how to improve process efficiency with robotics without knowledge about what the current process state is or what kinds of deviations or variations there exist? Process mining brings vital insight by revealing the as-is process state from a data-driven perspective, and reduces the ambiguity caused by decision-making based on a hunch. Same time the understanding of, which are the wanted process steps and what are the deviations that cause most of the unnecessary steps and work, is built.

RPA success with QPR ProcessAnalyzer deviations

2. Harmonize Processes.

Processes naturally have very wide variations and it is not effective nor profitable to use RPA on all process variations, but only on the most frequent ones. Programming the software robots is quite expensive, and a misplaced investment would not pay itself back ever. Therefore, processes should be harmonized first, to create high volumes per variation. Only then the automation by robotics would be as beneficial as possible. The more different variations robots need to cope with, the higher the RPA costs are.

RPA success with QPR ProcessAnalyzer paths variations

 

3. Implement RPA Solution.

This is a pure RPA project implementation step of constructing the rules for robots and programming the workflow execution.

 

4. Process Follow-up / Process Compliance.

The results of the automation projects can be difficult to measure. Therefore, process mining is important - to know that the software robotics is achieving the desired outcome, and that processes are executed as designed. This kind of process benchmarking and process compliance measuring is also an effective way to focus the organization’s investments more accurately and with a higher return on investment.

RPA success with QPR ProcessAnalyzer benchmarking

All in all,

RPA is an effective automation method when used correctly. When the pre-implementation process state is unknown or incorrect processes are being automated, the results are not effective or profitable. Therefore, process mining should be used side by side with RPA project to guarantee high ROI.

 

Interested to learn more? Book a session with us or leave us a message!

 

Feel free to check out QPR's process mining solution for RPA deployment support.

 

olliandriku's picture
Riku Mikkonen & Olli Komulainen

 

 

 

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