The Human Resources (HR) of any organization take a considerable amount of effort to put in a performance management system in place. Key Performance Indicators (KPIs) are defined; employees go through an appraisal process which can be reviewed if required. Why should it be any different for bot that takes over the human effort automated in a process? In fact, there is more reason that bot performance be measured. Unlike humans, in the case of bots, there is a clear trail of the work done. There is logging information available. Now, logs are not described in the most intuitive ways for managers to measure performance. That is one of the reasons this wealth of information has been ignored. But this could change with Analytics.
Types of Bot-enabled Insights
Bot Analytics traditionally has been used only to monitor processes; time taken for a process, whether a process has faulted and so on. The scope of analytics can be increased to measure the performance of bot over a defined time period, a group of bots or bots deployed for a certain business function. The managerial outcomes required can define the scope of analytics and how hierarchies of bots need to be created. The KPIs get defined along with the details of how these are going to be measured. These KPIs can be bucketed as
- Operational Insights
- Business Insights
According to this blog on UiPath, Operational insights contain information that relates to the execution of RPA itself, but they don’t include things like the time saved and the money saved. Business insights tie what happened in your RPA deployment (operational) to how you have benefitted your company (business). Both these descriptions are very apt.
Why detailed logging is required
Using logs, it can easily be found out how many emails have been sent, how many invoices have been processed, etc. Now, these are not usually factored when carrying out process analysis. Astute business analysis requires incorporating process metrics which provide business insights within the Automation workflows. It must be a part of your design. An example would be logging the Invoice Customer and the Invoice Amount. It would then provide details of how much invoices are being processed in value terms per day. What is the average transaction value of such invoices? How much time and effort does it take to onboard a new employee of a certain role or designation? How much effort is lost in fixing errors in Account reconciliation? These are very valuable insights for the management to improve upon their processes and resource management.
How Analytics will better Orchestration
Business analytics can drive bot operational efficiency. Predictive business analytics can be used to drive better outcomes by better bot orchestration. As a simple example, it can be known how much transaction value of invoices is getting processed on a Friday. If it’s lesser than usual, that activity can be moved to the coming Monday. In retail customer service, there may be a surge in the volume of mails received on weekends. Bots can prioritize emails basis the value of purchases and redirect them to human operators. This ensures that issues related to high-value purchases are addressed as a priority. A scheduling logic to orchestrate the bots can be derived by business analysis of mail contents with product value as a key attribute. Analytics of a Server disk health monitor can be used to decide when and how the bots need to be triggered to carry out the health check. By predicting the number of applications that can have errors in an Insurance Claim Process, the number of human operators required to resolve the issue can be optimally decided. The moot point here is RPA in analytics may become important to constitute a discipline by itself. Its scope will increase beyond processing logs. There will be more use cases where Business Analytics and Operational Analytics intertwine challenging the skills of RPA practitioners. Developers will find a way to use orchestrator assets and queues for operational and business insights. Business Analysts and solution architects will factor in business metrics and incorporate them into the design of RPA workflows. The result will be a thrust on operational discipline and process orchestration which is validated by data.
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