• June 3, 2020
Process Mining: An Indispensable Companion to RPA?

In the few short years business publications have been chronicling the dazzling ascent of robotic process automation (RPA) as a cornerstone of digital transformation, a mountain of positive coverage has often been punctuated by various “Why RPA Implementations Fail” pieces. While articles of that nature often cite design, governance and insufficient cross-departmental collaboration as reasons companies might not be getting the most out of RPA, a consideration that seems to get less attention than it should is identifying the processes that will benefit most from being automated or avoiding automating bad processes. Enter process mining.

Process mining was developed and popularized in European academia and the first process mining companies started there. It began with an insight by Dutch computer scientist Wil van der Aalst, who realized data from IT systems could yield valuable insights that would enable organizations to understand and optimize their own business processes.

Process mining captures information from enterprise transaction systems and provides detailed—and data-driven—information about how key processes are performing. It analyzes event logs that show how work is actually happening—who did it, how long it takes, and if it deviates from normal. It then creates key performance indicators for the process, enabling a company to focus on concrete steps that will improve it.

For companies that are considering RPA to make their operations more efficient, or for cost savings, or to enable growth in tight labor markets, process mining is becoming an indispensable companion technology. The first or second manual business process a company automates might be readily apparent. But for organizations that want to scale RPA and truly leverage the technology to achieve those goals, identifying likely candidates for automation is challenging. Process mining analyzes IT data across the breadth of an organization to achieve this.

Companies implementing RPA can use process mining throughout the technology’s lifecycle to optimize implementation

Using Process Mining to Lay the Groundwork

You cannot improve a process without understanding its current state. Process mining software sits on top of existing IT infrastructure, analyzing event logs of all systems and yielding a complete picture of your business processes as they currently exist. With that roadmap in place, it becomes easier to identify processes that are not optimized.

A company can take a process map developed by its process mining tool to determine which processes are ready for automation. They can also use it to identify steps in a process that have multiple variations, discrepancies, or other inconsistencies, that will require further investigation (i.e., a bad process). 

Laying this groundwork before any process has been automated can save a significant amount of organizational pain. Automating a bad process will result in the same bad output—you’ll just get it faster and more consistently.

Assessing RPA Performance

The results of automation projects can be challenging to measure. When companies begin RPA pilots, process mining is one way they can assess the efficacy of newly automated processes. As bots start their work within a pilot project, their activities are tracked by the underlying IT systems. After an appropriate number of iterations, the process can be evaluated by using process mining.

Since it provides automation and execution rate data directly from your systems, you know if bots need to be re-trained or reassigned for maximum process improvement and efficiency. Companies can use process mining to benchmark the most effective RPA implementation, guiding their efforts as they scale the technology.

Conclusion

When used correctly, RPA can increase efficiency and productivity. If the state of a company’s processes is not comprehensively understood before implementation or the wrong processes are being automated, the results will reflect that. Process mining is a powerful tool that can help companies engaged in RPA avoid these challenges and achieve the potential they are expecting.