RPA performance metrics enable Managers and RPA deployment teams to understand the difference that Intelligent Automation makes to their organizations.
Tracking ROI on Automation efforts and performance metrics provides organizations with valuable foresight. It can enable them to create realistic plans for the future of their automation journeys and even push towards enterprise-wide RPA use. Some performance metrics are used to assess qualitative outputs, which can be very helpful when moving in the direction of enterprise-wide RPA use.
Costs & ROI
One of the reasons RPA Monitoring is essential to any business’s RPA strategy is that it compares the effects and costs pre-and-post – automation. The difference in the cost of a process before and after RPA implementation is easy enough to measure. The cost before automation is the cost of the manual effort (usually the salary of the Full-Time Employees (FTEs) instructed to perform the task). Whereas, the costs after automation is the cost of the Digital Worker(s) and the cost of FTEs who handle any RPA maintenance and process anomalies that the Digital Workers cannot process.
Once you have successfully deployed Digital Workers in your department, you will begin to see the value of RPA. By applying performance analytics to the data collected from your processes, you will be able to make use of this data and see your processes from a different perspective. The beautiful thing with automation and performance analytics is that you can immediately act upon the information and data you are provided with. Digital Workers and performance analytics can give you real-time data, which paints a picture of your processes. That is essential if your organisation is trying to cut or gain visibility on costs.
The following are some of the ways that RPA can make an organization improve its processes and, when combined with Performance Analytics, make better use of its Digital Workers:
1. Improved accuracy:
Performance Analytics, reporting, and metrics allow you to eliminate identified errors. This comparison extends to manual versus automation metrics since manual error rates that occurred prior to automation can be benchmarked against post automation performance.
By monitoring the performance of Digital Workers, controllers can assess when they are idle or underused and then schedule non-time-bound task execution at those times. In this way, Performance Analytics informs Digital Worker scheduling.
3. Compliant Digital Workers:
Monitoring Digital Workers will mean compliance standards are being evaluated and met in real-time. This includes (i) watching how and when Digital Workers access specific systems and (ii) making sure that this is in line with policies, protocols, guidelines, and laws.
4. Enabling engagement in new projects:
When Digital Workers take over repetitive tasks, FTEs can be reallocated to more strategic projects that require critical thinking, creativity, and problem-solving, which can add more value to the business. Meanwhile, the Performance Analytics allows the FTEs to see what Digital Workers are doing with (now) automated tasks.
5. Tool utilization:
Since Digital Workers can work in web browsers, in a Windows environment, and in Mainframes, RPA Performance Analytics allows Controllers to see how often specific systems are being used and for how long. This enables them to measure ROI on System investments, not just RPA investments, which can lead to savings downstream, as underused Systems can be phased out.
Improving the performance of Digital Workers can seem like a tough task if you’re not familiar with RPA. However, performance data for RPA deployments need to be utilized correctly. This ensures a successful deployment of RPA, which increases the likelihood of investment in RPA leading to an effective balancing of workloads across the enterprise and even successful enterprise-wide integration. That said, Digital Workers are not immune to interdepartmental backlogs and office politics, so if one group of Digital Workers falls behind, another group (responsible for “follow-up tasks”) can stand idle for hours and harbor inefficiencies in any organization. This is what RPA Performance Analytics addresses.
Performance Analytics is a vital component of any effective RPA strategy.