Robotic Process Automation or RPA has gained tremendous momentum in the last five years or so and business experts worldwide are speculating about its future consequences. Many are already considering using RPA as a means to improve the operations but it cannot be denied that since it is still in its initial stages of development and implementation, there are few glitches that one has to contend with. Process Mining is a viable solution for the successful implementation of RPA.
RPA offers an automated system for manual tasks, especially those that are repetitive and follows a set pattern. These tasks can now be carried on by the “robots” in much less time and much more flawlessly. The aim of RPA is to handle unstructured data as well and put it into a pattern, something that leaves scope for human error when done manually. Work can now be accomplished through pinpoint precision. RPA works brilliantly when implemented but the concern is to identify the areas in the organization that needs automation so that RPA can be deployed most efficiently. As easy as it might sound, the process of identification is not so simple. It also analyzes the extent to RPA that can be implemented in an organization or whether it is needed at all!
Process mining is the method by which data is used for analysis which is gathered from the various information systems. Much of the data that is produced is stored as logs and a lot of unused data also lies dormant, getting buried under piles of data generated later. This data contains a lot of valuable information about how the steps of the process are carried out and what exactly it relates to. Avenue for process mining tools are created and by using contemporary data mining methods, the unused process data is again brought to the forefront and made useful. They are loaded into a system that enables process BI and the data would contain the exact information on how a process is actually carried out in the real world. Process Mining can help a user get a clear overview of how the process is actually occurring in the system and also enables him to take control by gaining greater clarity on the points that need improvement.
Since the main objective of RPA is improved efficiency through automation, complete success in this field cannot be obtained unless the areas that need automation have been properly identified. Process mining helps in that identification and this is one of the reasons why some organizations, in spite of automating their systems through RPA often see that there has been no significant change in the end results. Perhaps the automation was not done in the right areas and process mining would have helped with this. It could provide a general overview of the processes within the company based on specific data and identify processes suitable for RPA implementation right from the preoperational phase. It also determines the optimal process flow and process path and also provides monitoring and continuous improvement of RPA itself after it has been implemented, making it an ongoing process of development which helps the company to grow continuously.