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RPA Pitfall #4: Choosing a Process That Changes Frequently

We have seen organizations deal with one particular pain point – dealing with a process that is always down or in need of support. This is often when an organization chooses to automate a process that changes frequently, causing the automation to crash regularly as well as require constant modifications.  This in turn puts a lot of strain on the overall program and worse most organizations do not end up seeing the value balance out the effort in having automated the process in the first place.  This same struggle also occurs when a process fails to clearly define why frequent changes are happening or how to predict them, in turn, this deters businesses from automating these desirable processes.  One way to both avoid the headache of constant breakdowns and corrections on larger complex workflows is to utilize technologies like Process Mining in order to standardize, optimize, and predict changes, allowing organizations to turn this downfall into an automation win.

Process mining uses underlying data created by an automations running systems to provide a proper diagnosis as to why it’s constantly changing. This diagnosis returns insights and provides an understanding that can help the organization see exactly what is causing the changes by examining all the exceptions and reasons that they occur. When a process changes frequently it is usually attributed to major exceptions thrown by the process or an important changing variable within the bot actions. An example of this would be the loan application process. In a bank’s loan application process the majority of applications are processed based on income, credit score, outstanding loans, etc. however, exceptions can be incurred from applications for veterans, teachers, customers with disabilities, or any other group that receives federal or lender discounts and unique interest rates. In this case, the process can become bottlenecked due to availability of processers working against the number of applications the bot requires human actions. Other errors that may require human correction would be changing variables such as interest rate fluctuations, preferred lending company policy changes, or even internal operating systems with frequent updates. Any lending company would want to prioritize the automation of their loan processing task, however, to avoid the constant breakdowns and large amounts of exceptions, the organization would want to use process mining to eliminate and predict the changing variables, increasing automation ROI and throughput.

Process mining has been gaining momentum within the automation industry as it is able to break down processes to a micro level and provide the business with a list of action items to resolve the issues and optimize the process overall. Back to our loan application example, the frequent process exceptions can be extracted, mapped out, and transformed into fully automated subprocesses based on the information returned by the accurately mined process maps. This eliminates the need for human involvement with the exceptions without having to reconstruct the original application processing automation. By dissecting the process and automating the error handling for a specific step, the overall automation can run end-to-end without stopping and requiring human input to solve the error. Process mining transforms faulty automations based on a deep understanding of the process. The finalized solutions are then capable of anticipating encountered changes, allowing for organizations to automate the desired process regardless of frequent changes.