In our other articles, we’ve talked extensively about the benefits of Robotic Process Automation in different industries. We’ve also covered the most popular automation tools to help businesses select the best fit for their needs.
However, we’ve also noted that 41% of companies are still reluctant to adopt RPA due to the lack of clarity in their business processes. Their fears are understandable, as RPA implementation is a significant leap you can’t take without preparation. The same can be said about other major business decisions—you can’t just throw money at the problem and hope it will work. The difficulty lies in identifying the issue and then coming up with a solution.
The good news is, the answer to this challenge is already here: it’s process mining. The solution is relatively new, but it’s expected to grow substantially in the years to come with an almost 50% CAGR until 2029. The potential for process mining to streamline discovery and decision-making is very much there.
And the even better news is that the capabilities of process mining can be further enhanced with Robotic Process Automation. In this article, we’ll show you how the two can be used together for the best results. But first, let’s get you started with process mining, how it works, and what its benefits are.
What’s Process Mining?
At the basic level, process mining is exactly what it seems: mining for processes. Process mining goes through the event log data generated by the software systems used by your business (more on that later) to discover workflows that could use some improvement. That data is then visualized to facilitate decision-making. Advanced process mining systems can also suggest upgrades on their own.
There are three main types (or applications) of process mining:
- Discovery — The algorithm scans the available data in search of patterns that can be used to create new, efficient workflows.
- Conformance — The aim of this process mining type is to check if the current processes are working as intended and find any deviations.
- Enhancement — In this check, additional data (e.g., yielded from conformance analysis) helps identify potential improvements in the existing processes.
Typically, all three types are used together to find, audit, and optimize workflows.
How does Process Mining work?
As we’ve mentioned earlier, process mining relies on event log data from other systems. Event logs are the digital footprints left after a piece of software processes data. These traces store information about past activities and their use is normally limited to error tracking. This means that each business that runs data-processing systems such as SAP, Oracle, Salesforce, Microsoft Dynamics, etc, has tons of data that is mostly just lying there, unused…
… unless you do process mining. The three record attributes that are crucial for that are:
A unique number is assigned to each event for easier identification. This can be an order number, client name, or any other identifier.
The time when the event was finished. With the event timestamps for all activities within a workflow in place, we can see how long each of them realistically took to complete.
All activities are connected with the case ID. For an order number that could be the customer placing the order, you responding to it, or an invoice being sent.
Based on these three data points (the so-called “data triple”) gathered from a statistically significant data sample, the process mining software creates a process graph. This visualization allows you to examine the workflow step-by-step and find inconsistencies, tasks that take too long or are outright redundant, bottlenecks, and other inefficiencies.
Advanced process mining solutions can do even more. Some identify the root cause of the problem or monitor process performance 24/7. Others predict results for the set KPIs or give you instant, automated reports, and insights.
What are the benefits of Process Mining?
Before we go straight into listing the pros of process mining, let’s talk shortly about how businesses traditionally discover inefficiencies and areas for improvement.
Gathering information about workflows is a part of Business Process Management (BPM). This is done mostly manually, through interviews, workshops, and process mapping that spans several days and requires the combined effort of members across different teams. It’s time- and work-intensive, and since so many people are involved, the findings can be a little skewed and not entirely objective. That said, this traditional method has its benefits: it engages various stakeholders and takes a qualitative approach.
Process mining, on the other hand, relies on data rather than human effort. Process mining algorithms can crunch numbers and find patterns much faster than any team of experts. The number and size of data sets don’t matter—unlike humans, process mining systems don’t have to focus on a single point. Bias isn’t a factor, either, since the software draws its conclusions based solely on objective data.
This comparison should give you a good idea of how useful process mining can be but its benefits go beyond that. Here are the key ones:
Process mining helps you find bottlenecks, errors, redundancies, and other inefficiencies and eliminate them to make your workflows simpler and drive better results.
Similar to RPA, process mining helps businesses earn more not by increasing sales, but rather by lowering operational costs.
Process mining gives you a better understanding of the inner workings of your business so you can act based on hard facts, not guesses and opinions.
By gaining insight into your workflows, you can validate their compliance with the regulations specific to your industry.
With process mining, you can constantly track the performance of your workflows and upgrade them when necessary.
Automation is often the best answer to workflow inefficiencies. Process discovery allows you not only to find them but also to assess what and how to automate.
RPA and process mining—what sets them apart?
If you’re familiar with Robotic Process Automation, that last benefit of process mining should really speak to your imagination. Implementing RPA involves lots of planning, consulting, and brainstorming, and a tool like a process mining is an invaluable tool to facilitate the process. In fact, process mining often precedes automation initiatives—so often that it can lead some to believe that it is an integral element of RPA. But that’s only partially true.
Sure, process mining and RPA are commonly used together but they are two independent solutions. Process mining is a discovery tool that can enable automation tools such as RPA. Due to that, it is often one of the first steps in RPA implementation that allows stakeholders to assess the scope of the project as well as determine which processes are worth automating.
That said, process mining can also reveal other answers to operational challenges, like updating physical infrastructure, eliminating excessive tasks, or reorganizing supply chains. All in all, process mining is beneficial during RPA implementation but is also an independent tool on its own, and a few factors distinguish the two:
If you face Process Mining as an IT consultant, let us show you more about how you can benefit from automation in consultancy.
Discover SAP automation with RPA and UiPath! And learn the differences between RPA and Machine Learning.
How can automation support Process Mining?
If you’re familiar with Robotic Process Automation, that last benefit of process mining should really speak to your imagination. Implementing RPA involves lots of planning, consulting, and brainstorming, and a tool like a process mining is an invaluable tool. However, the relationship between RPA and process mining isn’t one-sided and the former can benefit from automation just as much, and in more than one way.
- First, to bring the expected results, process mining needs data. Not just any data, though. Event records are often duplicated, incorrect, or incomplete. If the process mining algorithm is fed that low-quality data, there’s a good chance that its findings will be distorted and unreliable. However, scanning databases for errors and then correcting them manually would take too much time and effort. Here, RPA can be used to go through event logs in search of missing or mistaken data, no matter how large the sample is. Then, bots will use their data extraction and entry capabilities to fill the gaps and fix the errors to provide process mining systems with clean and complete records.
- With machine vision, RPA can also enhance the three key attributes extracted from event logs. By recording the on-screen activity of workers responsible for particular steps of the workflow, Robotic Process Automation will provide you with additional context, e.g. what systems are used at each stage of that workflow. For instance, solutions such as UiPath Task Capture can take screenshots for every mouse-click in the workflow and map it directly onto the process diagram.
- Another compelling use of process mining automation is creating a DTO — digital twin of an organisation. It’s an automated virtual model of an organisation updated with new data that allows decision-makers to see how their business will react to changes and test potential upgrades before they’re implemented. For some companies, this method has already proven effective. Siemens, for example, used DTOs to evaluate and improve the digitalization potential for its order-to-cash processes. The implementation was validated and led to an 11% reduction in manual rework and a 25% increase in the automation rate of said processes.
- Next is the analysis of the workflow models generated by process mining tools. Typically, it would still be performed by human subject matter experts due to the business and technical knowledge required. However, thanks to natural language processing, machine learning, and sequence modelling, the most tedious parts of the analysis can be assigned to algorithms. Surely, as said technologies will continue to develop, so will their role in the analysis process.
Some machine learning-based process mining solutions can already recommend the best course of action to optimise a specific task. One particularly intriguing use case for automation is identifying deviations from a standard process.
Currently, more complex, multi-path workflow models still need to be analysed by human experts who know the off-screen business context of each step. In the near future, however, this process can be expedited with the help of intelligent algorithms to correctly identify actual deviations at a much higher rate.
In today’s fast-paced world, it’s crucial to understand how the technologies businesses have at their disposal can mutually amplify their effect and work together for an even better outcome. Equally essential is the inside-out knowledge of the organization’s functioning, from single processes to sophisticated workflows. The connection between process mining and automation is a testimony to both these principles.
If you want to find out if your business is ready to get started with Robotic Process Automation, you’re in the right place. Reach out and tell us about your plans, challenges, and processes. We’ll evaluate your workflows and create a free proof-of-concept so you can test our work before making any commitment. Then, we’ll guide you on your way toward RPA adoption.
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