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The Rise of Process Mining in the Automation Era

The Rise of Process Mining in the Automation Era

Alp Uguray July 12, 2019Blog
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Over the course of the last decade, many companies have dedicated vast investments to make sense of the large amounts of data collected in their IT Systems. Leveraging the collected data, Artificial Intelligence has been applied to drive meanings to achieve better decision-making, and the results are all around us. Recommendation systems in Amazon advising products based on our purchase behavior, Siri and Alexa’s understanding of our words, Facebook’s Newsfeed selections based on what we read and like, and the online advertisements in Internet are each based on an algorithm that learns from the behaviors and trends in collected data and predicts what we may need, do or want next. The more information there is, the better the predictions are.

One related field that has been significantly taking attention over the past decade is Process Mining. Process Mining aims to understand how each person is using an application by analyzing the event logs in IT Systems. In a nutshell, it recognizes the patterns, identifies trends and details how a process is executed by each user. The tools visualize the process maps of user interactions and recognizes the different pathways taken to accomplish each process. Analyzing the process can help business users reinforce their understanding of the process by recognizing inefficiencies, and even capture unknown or unforeseen process pathways. Just like our purchase behavior indicates what we may want next on an e-commerce platform, our process behavior sheds a light on how we can do our work better and more efficiently.

Despite its relative age (Process Mining Manifesto was first released in late 2011), it seems like we are just getting started with making sense of process data and building methodologies around it. The practical applications of Process Mining are vast. Many companies have variations of the same processes across different departments such as; Purchase-to-Pay (P2P) and Order-to-Cash (O2C). The two process are only a few examples of where Process Mining can be applied. An example analysis of P2P process by Celonis, one of the many process mining vendors, is below to visualize how it looks:

Process Mining Screenshot
How can Process Mining Help?

Process Mining models provide full transparency into the work that’s done by visualizing a given process in real time. This allows one to identify all process variants and highlight deviations from the future process, thereby uncovering bottlenecks, rework, delays, cost drivers and other inefficiencies. It opens the doors to further process optimization (e.g. standardization, process re-design, robotics, simplification). For business stakeholders looking to optimize and redesign a process before applying automation, the insights given can help with targeting accurate KPIs and analyzing the root causes of process weaknesses.

How can Process Mining help RPA achieve Digital Transformation at scale?

Process Mining opens a new path to achieve Digital Transformation at scale. In my observation, one of the most significant bottlenecks of Robotic Process Automation (RPA) is choosing the right process to automate and developing an in-depth understanding of how it is executed. By leveraging Process Mining, organizations can augment their process targeting and break-down automation eligibility for each step of the process. This creates more definitive business cases and efficient process targeting in a shorter amount of time compared to process capturing manually – through video recordings and subsequent manual analysis.

Many RPA tools such as UiPath and Blue Prism help visualize the status and performance of robots, including success/failure rates and exceptions. With a similar approach taken to analyze the event logs of users, Process Mining can understand RPA logs and analyze a Robot’s performance. It can provide key insights as one compares a manual As-Is process with the results of the ‘To-Be’ automated version. For instance, an analysis may reveal tangible and effective KPIs such as efficiency by process-step, Average Handling Time, through put time between each process step, Cost Savings and ROI. As automation removes the repetitive and mundane tasks from the workplace, Process Mining has the capability to guide organizations in finding these tasks, understanding them comprehensively, and effectively transforming them.

This article was sourced from the LinkedIn page of Alp Uguray, Consultant at Symphony Ventures.

Tags: Robotic Process Automation, Artificial Intelligence, Blog, Digital Transformation, Process Mining

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