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What You Need to Know About Process Discovery for Automation

Robotic Process Automation (RPA) has become one of the vital transformative technologies driving innovation in today's business world. The crisis brought by the COVID19 pandemic has caused organizations around the globe and across industries to seek technological solutions that can help them automate their highly-manual, repetitive business processes and drive better business resiliency. 

With so many organizations taking their first steps in their RPA journey, a key question continues to be central for these organizations. How to best capture and understand how and what business processes should be automated?

For most organizations getting started with RPA, the most challenging part is understanding what to automate and prioritizing those automation candidates. Process discovery is crucial to a successful RPA initiative because it records and analyzes an employee's actions at a very detailed level to decide which is the best approach for implementing automation. Without the proper understanding of the business processes that are candidates for automation, over 40% of RPA projects fail.

Tackling Process Discovery

For organizations that are inexperienced in highlighting automation candidates, it is essential to have process discovery at the center of their RPA initiative. This way, these adopting organizations can understand the best processes that could benefit from automation to achieve a fast ROI. For organizations with previous experience with robotic process automation (RPA) and process discovery, it is always best to maintain process discovery best practices and perform an end-to-end process discovery for the business process they seek to automate.

What are Process Discovery Best Practices?

Organizations need to remember just because they can automate a process, it doesn't mean it should be. Organizations new to RPA should start by identifying rule-based, standardized processes that cross multiple systems, requiring a non-intrusive approach to automation. Organizations must evaluate RPA opportunities to find accumulated "swivel chair integration" (rekeying data between systems). Areas where they can identify where humans perform work involving structured, digitized data processed by pre-defined rules. Of course, as automation technologies advance with more AI capabilities being added, more complex long running workflows with unstructured data can also be considered for automation. But without the addition of complex AI skills, the best processes suitable for automation should meet the following characteristics:

Highly-manual and repetitive. The business processes that staff members perform daily and regularly are prime candidates for automation and yield the fastest ROI. In contrast, automating business processes that employees perform sporadically may generate a lower ROI and impact on the organization.

Rule-based. The business processes that are best suited for automation should consist of a defined set of pre-defined rules that all staff members follow to complete the task. 

Mature and standard processes. Business processes selected to be automated candidates must be well-established processes and should not change frequently. The team involved in analyzing an automation candidate's suitability should keep in mind that a process must have defined process steps, a defined execution order, and defined systems where the process runs. If an organization encounters business processes performed in multiple ways and yields the same results, said processes must undergo a process standardization procedure to optimize and standardize them before being subjected to automation.

Structured data inputs. Organizations need to ensure that all data used by a business process is structured and digitized. Some of the data inputs that RPA can interact with are Excel spreadsheets, databases, JSON files, HTML files, XML files, PDF files, TXT files, and many more. But, if the data is in a unstructured format or not digitized, for that case, RPA can leverage AI-powered technologies like intelligent OCR (Optical Character Recognition) and NLP (natural language processing) to scan data, digitize it, and structure it. 

Low exception handling required. If a business process generates exceptions regularly, that process is not a prime candidate for automation. But, if an organization wants to automate a business process that is prone to exceptions, for that case, the organization must analyze that process and determine if the organization should make standardization efforts, so the process is more viable for automation.

Multiple systems interaction. Business processes that require staff members to interact with various systems are prime candidates for automation since robots can interact with any software performing any task as a human would.

UiPath Process Mining: An Essential Tool for Process Discovery

As organizations understand the characteristics that business processes must have to be prime candidates for automation, it is also essential that organizations are leveraging the right tools to identify the business processes that can be top automation candidates.

For process discovery, UiPath has developed Process Mining, a powerful solution that will help organizations identify prime automation candidates across the enterprise. 

UiPath Process Mining leverages an organization's business applications' existing data and provides a deep understanding of complex business processes. UiPath Process Mining will provide a high-level view of an organization's processes and produce detailed graphs that layout each process in chronological order from end to end.

Furthermore, UiPath Process Mining helps organizations identify inefficient processes that would be a good fit for automation. The solution does this by highlighting where the bottlenecks are, what weighs down a process, and the impact this inefficient process has on the bottom line. 

With UiPath Process Mining, RPA stakeholders can make data-driven decisions about automating critical business processes. Additionally, the solution allows organizations to use smart tags and KPIs to monitor their business processes, understand what is working and what is not, and how the organization can optimize those processes.

Watch our "Process Mining: Design Your Robots With Scientific Precision So They Are Right The First Time" webinar to learn more about UiPath Process Mining.

Standardizing and Process Optimization

At the beginning of this article, we talked about how the COVID19 crisis has accelerated organizations' automation adoption across industries. Today, the crisis has deepened the need for organizations to do more with less. Therefore lean methodology has become vital to becoming successful in adopting automation and building business resiliency fast. 

Lean Sig Sixma (LSS) is a crucial complement to RPA. With LSS, organizations manage to reduce the manual load by eliminating waste and simplifying and standardizing processes. Still, organizations reach a point where they realize they cannot eliminate all the grunt work. This point is where RPA comes into its own, expanding the LSS toolkit with an approach that empowers the business to continue to improve and move up the sigma scale without the need for disruptive IT initiatives.

How does Lean Six Sigma help an organization with RPA?

Identify the right candidates. Business processes that are hindered by bottlenecks are top candidates for RPA. LSS tools such as bottleneck analysis can help organizations identify the right candidate business processes within the enterprise by highlighting constraints.

Improve process stability. LSS tools can help organizations improve a business process's stability by reducing variation in the process and making the process more suitable for automation.

Enhance process robustness. Leveraging LSS tools such as failure mode and effects analysis (FMEA) will help organizations uncover the potential failure modes of a business process and develop suitable mitigation actions for them, making the process a better candidate for automation.

Best Practices to Keep in Mind with Lean Six Sigma for RPA

  • Filter the process candidates with Six Sigma.
  • Standardize process redesign in your CoE (Center of Excellence) with LSS practices.
  • Systemize the delivery process.
  • Never automate broken processes with temporary fixes and high exceptions.

As we have discussed, process discovery is an essential part of every RPA initiative. Organizations must keep in mind that process discovery includes task capturing, process analysis, and granular documentation as some of its core parts.

When an organization does not properly perform process discovery, it might face increased costs and ineffective automations and ultimately fail its RPA journey. By leveraging the right process discovery tools and filtering processes with the right optimization approach, organizations can ensure streamlined planning and implementation of RPA across the enterprise. 

Download our eBook, learn about what business processes have the highest automation potential within your organization's industry, and gain visibility of the critical business areas best suited for automation.

Contact us or request a meeting if you're ready to chat with one of our experts and start your digital transformation journey today.

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