5 Ways Process Mining and RPA Complement Each Other

Process Mining
There are some misconceptions in cyberspace about process mining and another new technology, robotic process automation (RPA). Let’s clarify things and learn more about how these two technologies work together to improve ROI.

First, here are the basic definitions of process mining and RPA:
Process mining means gaining insights into business processes through data in IT systems. output? An overview that accurately represents the “as is” state of a business process.
RPA refers to the use of so-called “bots” to automate process steps in a company that previously required human action. These technologies and techniques differ in five respects and are highlighted by how they work together to achieve business goals.

1. Process Mining Gives You the As-Is State of Your Business Processes

.. then RPA creates an overview of these processes and translates them into actionable automation.

Process mining software is deployed on top of existing IT infrastructure and looks for event logs left there by other systems. These logs contain what, who, and when to create an interactive representation of the process.
The RPA team can then get the process map and deploy the bot to perform these exact steps. Over and over again, infinitely or on a set schedule.

2. Process Mining Highlights the Best Candidates for Robotic Process Automation

Organization-wide analysts can use the transparent process maps of process mining tools to determine which processes are ready for automation. You can also use it to identify steps with multiple variations, or other inconsistencies that need further investigation.

By starting with such an analysis, you can avoid one of the most common points of failure in RPA, automation of broken processes. This step is important for successful deployment, as RPA requires easy-to-reproduce tasks with a standardized data center to be most effective.

3. Process Mining Locates Deviations Before RPA Automates Them

You`ll get the chance to harmonize these steps to ready them for automation, in cases where you find that a process has too many variations or deviations. Employees won`t be able to tell you anything about any process that has been sidestepped for too long – most probably because nobody has realized it yet.

However, Process Mining doesn`t rely on anyone`s memory to collect its data.

Say you`re working with a process that has 45 variations. Ten of these represent single-digit percentages of total cases. Understanding this through process mining gives you the opportunity to streamline your process. Perhaps you can combine these 10 process variants into one.

Now you can continue to automate using RPA, recognizing that you are not automating the mess.

4. RPA Uses the Process Map as a Guide for Bots

Having a process map that outlines the steps that a human worker takes, makes the process of deploying a bot much easier.

As a simple example, suppose you discover a process consisting of 10 steps, all performed by a single employee working on a regular workstation. Process steps consist primarily of mouse clicks on specific menus and keystrokes for copying and pasting data into specific tables. Analysts can perform the same tasks in sequence and use them as bot templates.

Process mining provides a template, and RPA takes that template to automate the process. As a result, future compliance checks and monitoring can be performed much faster, further improving the cycle.

5. RPA Gives Results, Process Mining Measures Them

Now, you are automating some processes and generating statistics and new numbers to compare with KPIs.

Now it’s time for process mining to intervene and provide a way to match those numbers for comparison. Process mining can be done at any time, so you can generate a new process map of an automated process and compare it to the previous automated results.

This also applies to the generation of new baselines for future use.
Therefore, RPA bot improvements can be quantified by process mining. This allows you to measure KPIs and get the basis for a proof of concept.