Process Mining & RPA
By: Nicolas Andres
Tags: Rpa, Optimization, Process Optimization, Process Mining

Process Mining as an RPA strategy enabler.

Is RPA good enough?

There is no secret that today’s business operations are largely process-driven and supported by a variety of intertwined IT systems.

During the latest years, we’ve seen the Digital Transformation Wave boost every industry to incorporate technology in their operations, pursuing efficiency, and attempting to improve the way to do business. This trend speeded up even more because of COVID-19 effects.

However, most transformations don’t achieve what they expected. The complexity behind the scenes makes it very difficult to streamline the processes associated with a business operation.

Robotic Process Automation (RPA) tools come up as a palliative solution to remove the operational friction on those processes, with a focus on automating as many tasks as possible, and thereby affecting cost associated and improving efficiency, not only of the process but also of the employee’s routine.

The reality is that just automating is actually not sufficient help for the business. The major challenges faced today to scale-up RPA implementations come from identifying the right processes/tasks to automate or to apply any other optimizations. And to be capable of monitoring and measuring the impact generated.

It is here where Process Mining shows-up as an enabler of Automation Strategies.

What is Process Mining and why is it a promising technology?

The traditional path to discover how a process is performing or how a process looks end-to-end depends mostly on user interviews, poor documentation, and traditional consultancy methodologies like Six Sigma or any other manual intensive technique.

This results in potential disagreement between the team members and discord about the truth — Let’s be honest, we’ve all been facing the wall full of sticky notes and some occasional argument -. But don’t get me wrong, this is not a malicious position per se, but a product of subjectivity and natural human biases.

Needless to say, it is a time-consuming process (in my experience, could take several weeks to understand and map less than 50% of the process) and it is hardly accurate.

All these constraints make it really difficult for the stakeholders to understand just the ‘as-is’ of the processes and even less feasible to detect potential automations. Wherever possible, it is necessary to eliminate the contradictions and procure an objective perspective about the process. That is exactly what Process Mining does.

Process Mining provides a fact-based view of how processes execute in production, giving an objective input instead of a blur optic.

How does it work? — Well, mining tools lean on the digital footprint (event-logs) recorded by any enterprise systems (SAP, ServiceNow, Salesforce, etc), to extract process-related information and automatically discover a process blueprint.

So, until now, our first constraint around the process definition is saved. — As you may already notice, event-log maturity is a key component for mining a process -.

Of course, this is not the only capability that process mining has. Once we know how the process actually looks like we must use the tool to understand what is happening and how we can enhance those behaviors.

This technique places emphasis to cope with “conventional” business processes (Procure to Pay, Order to Cash, Source to Pay, among others). But, taking notice of the current trend of the Internet of Things, and considering that multiple events are being generated and stored almost immediately, leads us to think that the potential application of process mining techniques has several new domains.

So far, we can state that process mining is both a data-driven and process-centric approach to reproduce the reality within a graphical interface.

How can business operations benefit from Process Mining as technology?

Let us take a step back for a moment to take a look at the current opportunities that process mining provides.

First of all, let’s focus on the “as-is” of any process, what in miner’s slang is called ‘Process Discovery’.

As I already mentioned previously, process understanding or even redesigning based on SMEs (Subject Matter Expert) knowledge is not enough. Business decisions must be supported on a data-driven technique that can take event-logs and automatically shows the shape of business processes, getting full transparency about the steps involved in the process in a blink of an eye.

So, the time and resource effort saved at this point are already worthly. But ‘Augmented Process Discovery’ it’s just the first step out of many which must be taken towards a frictionless operation.

UiPath Process Discovery — Conformance Checking

The second important aspect is data-interpretation or data-science. It’s all about interpreting the data shown by the tool and identifying potential deflections on the expected process behavior.

In other words, is to exploit event data in a meaningful way.

Once we get a full picture of the process we can provide insights, identify bottlenecks, anticipate problems, record policy violations, recommend countermeasures, and streamline processes. The richest the event log is, the meaningful analytics we will be able to apply.

It is worth mentioning that it also enables us to see how data is flowing in real-time across the process.

Last but not least, the enhancement.

It is not the same to apply changes to any process with information than without it. Having meaningful information about the process and its constraints or obstacles, allows the owner to estimate the impact of those changes. And also helps to quantify savings to prioritize which will be the order to execute on a roadmap.

Keep in mind that to succeed in this journey, we must think in Process Mining as an iterative approach, where each iteration unlocks and improves business potential.

How Process Mining boost the RPA strategy

Over the last few years, RPA generated success stories in some organizations. But at the same time breed skepticism and fear. Understandable considering it is a new technology.

Now, which are the main challenges that RPA strategies faced nowadays?

Basically, it’s all about to scale and to be able to manage this new virtual workforce. To succeed in any automation strategy we have to consider at least two things.

On the one hand, we need clear initiatives in the backlog. This will justify the investment, the more automation we generate, the more ROI we will get (OPEX side in any TCO schema).

On the other hand, we need strong governance to track ongoing initiatives, monitor their performance, analyze KPIs, among others. But at the same time, moving forward identifying optimization opportunities in other business units.

The latter is not trivial, to identify optimization opportunities can be a really labor-intensive and subjective task. Many variables must be considered to evaluate its feasibility (technical and business-oriented) that makes an initiative suitable for automation and worthful for the business at the same time.

Quite often it’s hard to reveal the viability, here I see two major scenarios that frequently appear around RPA strategies.

There are organizations that complete a first pilot project successfully, but they struggle when they try to roll out the program to the entire company. But there are also companies facing troubles, to just begin with the RPA program due to the obstacles to select suitable projects

All these obstacles demand an extra effort that makes us work for RPA instead of putting RPA to work for us to achieve the desired fluid operation.

Even those companies that identified the suitable process to automate, deal with the issue of being automating not optimized processes. Bering in mind the usual — and understandable — rush to get started as soon as possible with RPA, the whole program ends up automating ‘bad processes’ with predictable poor results.

In Bill Gates words: “Automation applied to an inefficient operation will magnify the inefficiency”

Of course, this is far from the only challenges that RPA strategies face. Nevertheless, it helps to illustrate how Process Mining works as a compass to show exactly where automation makes sense, and where other kinds of solutions must be considered (e.g. re-engineering the process first).

This technology changes the perspective, making RPA much more accurate and setting a data-driven decision tool which gives a visual of where the pains are.

Conclusion

So, Process Mining software brings insight by drawing data directly from your IT systems (such as CRMs, ERPs, BPMs, etc) with built-in connectors.

Thanks to its scientific approach to analyze business processes, this tool exposes the root causes of inefficiencies and enables us to take real control of the process by throwing light on points that need to be improved, providing a strong basis for any business case.

RPA technology shouldn't be seen as a silver bullet that will solve every problem alone. But together with Process Mining techniques provides a great opportunity to investigate, analyze, and redesign a process before robots are implemented. The key to success in these programs is to optimize first, then automate.

To summarize Process Mining helps us in 4 aspects:

1. To recognize the actual state of your processes

2. Evaluate and optimize the dynamic of the processes.

3. To identify where RPA makes sense and shows potential returns.

4. Monitor automation rates, process compliance, and other KPIs continuously

Thank you for taking out the time to go through my article!

Cheers!