Intelligent Process Mining in Robotic Process Automation

Why and How Intelligent Process Mining Improves RPA Results

Understanding Your “As-Is” Process State

Many organizations are embracing Robotic Process Automation (RPA) as a key aspect of their digital transformation efforts. RPA provides companies with a “digital worker” strategy that can have significant quantifiable impact for teams that plan and execute automation projects using best practices that help clearly identify automation-friendly processes, while avoiding automation of poorly designed or broken processes. The ability to evaluate “as-is” state processes helps clearly set ROI expectations, assure agile service delivery, or realize the benefits of an improved customer journey.

RPA offers immense promise for better service, increased process accuracy, and tremendous cost efficiencies — no doubt the reason that service-focused industries are adopting it in droves.

If your organization is using a manual approach to understand end-to-end process execution, you’re making a substantial upfront investment of time and money, and only getting a piece of the picture. The software tasked with executing operations and back-room transactions generates immense amounts of process-related data. Being able to extract that data and aggregating then analyzing it provides you with actionable insights into your “as-is” processes. This concept may sound simple and easy to implement but it is proving to be a substantial challenge for most organizations to undergo. Why is this hard? One big reason is that when multiple systems execute different aspects of any process, it’s virtually impossible to recreate a single, usable view of how processes using old approaches. Another is that the analytic tools used to generate key metrics deliver snapshots in time, rather than viewing process execution in the context of time.

Intelligent Process Mining easily and cost effectively aggregates process data across many disparate systems and re-constitutes it as an interactive model that reflects 100% of process execution actually operates.

Process Intelligence Adds Value to RPA

While the majority of current RPA initiatives focus on automating high-volume, relatively simple processes involving structured data without human intervention. Bots are increasingly being considered in processes with unstructured data, as well as more complex environments in which humans are part of the process, or where some cognitive reasoning may need to be employed. Increasing complexity and sophistication drives up deployment costs, in turn challenging RPA ROI justifications. The ability to access actionable process intelligence is just as valuable in the digital worker environment, as it is when human workers are involved. It allows you to:

  • Spot redundant processes that you may be unaware of.
  • Identify robotic processes optimizations that can free up digital worker cycles — making even the most productive digital workforce even more productive.
  • Discover inefficient human-digital worker hand-off or vice versa
  • Provide quantifiable data on the financial impact of digital worker by process.
  • Compare human vs digital labor in terms of cost, accuracy, efficiency, and duration.

A Process Intelligence platform instantiates a corresponding risk and compliance framework for your Robotic Operating Model that both monitors and assesses automated process performance regularly:

  • Establishes a data-driven foundation for process governance and clearly documents and automates steps for risk mitigation.
  • Creates an RPA center of excellence that captures processes, exports processes into stubbed out RPA processes, or ranks processes for their perceived value score based on multiple data criteria.
  • Expand RPA scope by identifying process exceptions and launching remediation automatically.
  • Perform broader lifecycle management of both digital and human processes, and their interactions.

Target the Right RPA Opportunities

Process mining is used by many different industries to automatically model and present process flows. Similar to the digital twins first used in manufacturing settings, it exposes the inner workings of any business process as it actually happened, based on real data. A detailed process digital twin is the heart of intelligent process mining. Intuitive BI-style analytics makes it easier and faster to discover, analyze, and automatically monitor near real-time process flows — exposing the true depth and breadth of automation opportunities and challenges.

Here are four key common RPA challenges that an Intelligent Process Mining solution addresses — improving both implementation and at-scale sustainability:

Target processes with the greatest automation potential while reducing time to value

  • Delivers a single comprehensive end-to-end view of actual process execution that spans multiple business applications to uncover prime automation opportunities as well as potential side effects.
  • Easily identify high-value automation candidates based on actual process execution data that displays all process variations, as well as time and cost implications.
  • Enables quantifiable, data-driven return on investment calculations based on: #of transactions; # of process steps; Process AHT/TAT (duration); Cost per transaction.
  • Eliminates arduous, costly and often subjective manual process evaluation.

Intelligent Process Mining delivers 100% process visibility. Find high-risk or costly patterns you don’t expect.

Target High-Value RPA Opportunities

Avoid automation of broken or poorly executed processes

  • 100% “as-executed” process visibility allows teams to identify, analyze and correct process execution issues such as bottlenecks, compliance risks, or mis-sequenced execution pre-RPA
  • Avoid or fix broken processes that amplify RPA development costs and extend time to value
  • Identify potential changes in process execution that can expand the scope and value of RPA investments

Maintain post-RPA visibility for impacted processes

  • Ensure your automation investment is operating as expected post-deployment
  • Monitor automation’s up and down stream impact to ensure ongoing protocol compliance
  • Automated process execution monitoring in mixed mode scenarios (where bots incorporate human assistance) safeguards ROI commitments
  • Easily specify detailed scenarios or conditions that trigger real time alerts to the right people at the right time.
  • Clear, quantifiable post implementation cost impact that’s automatically monitored daily — providing data-backed justification for future automation initiatives

Execute RPA at enterprise scale

  • Use Process Intelligence as the air traffic control tower to establish a compliance and risk governance framework by monitoring enterprise-wide business processes in near-real time
  • Scaling from tens to hundreds, or even hundreds to thousands, of bots requires significant command and control to ensure automation remains synchronized across every process and business system it touches.
  • Monitor bot-enabled process execution in real time using alerting functionality to spawn automated remediation processes.

Build Your Own Process Digital Twin

An Interactive Process Model, also known as a Process Digital Twin, not only delivers 100% visibility for any process flow executed by your RPA bots or IT systems, it makes it possible to understand the complex dynamics that drive unexpected or undesired behaviors. Chances are you already have a significant IT platform investment as well as a variety of departmental systems, to ensure every meaningful piece of information is acquired about every support incidence or assigned task.

Intelligent Process Mining unlocks more of that value by automatically Discovering process flows, providing sophisticated tools to Analyze process behaviors, helping you Optimize productivity, customer satisfaction, and quality of service. Then, Monitor process execution in near real-time to sustain peak performance.

A Process Intelligence platform can help you improve every aspect of the RPA process — from end-to-end, regardless of the number of systems being used to store your data. Converting process data from any number of IT systems into actionable insight results in more accurate automation decisions, made faster and at lower cost. Intelligent Process Mining provides the confidence of making data-driven decisions that have sustainable impact on any aspect of service delivery. In short utilizing Intelligent Process Mining technology, you can ensure that your processes are flowing at peak efficiency in less time at a lower cost.

Increase Visibility — Accelerate Change

About the author

Ryan Raiker is an accomplished business consultant with experience working with small and medium enterprises. Ryan has worked in project management in State, and Local government. He studied Business Analytics and later earned his MBA from Widener University in Chester, Pennsylvania. Currently Ryan is focusing on Brand Management and Product Development for TimelinePI.

August 4, 2018