Leveraging Robotic Process Automation (RPA) With BPM and Decision Management
By Nathaniel Palmer, Director, Solution Architecture, SERCO
This is work which cannot be automated any other way. By definition it requires logging in and out of different systems to complete the process (or even a single task) and often involves third party systems or otherwise environments which cannot be integrated through a programmatic interface. Instead people do it, with swivel chairs and sticky notes, and as a result the design of the related rules and workflows are based on how the applications were built rather than the actual objectives of the end-to-end process which span across them.
This is why the value proposition of RPA can be so compelling. It is the ability to integrate process, rather than systems and applications, to deliver closer to holistic or comprehensive automation of work rather requiring (far more expensive) humans to perform this work manually. What is the best starting point for leveraging RPA? Look first at repetitive human tasks, where users are bogged down performing tedious work, repetitive steps, or otherwise without requiring any meaningful analysis. Also look for where users are shifting back and forth between different application interfaces as part of the task or process step.
A successful digital transformation strategy should tie together discrete moments of automation within a more comprehensive, end-to-end process
A successful digital transformation strategy should tie together discrete moments of automation within a more comprehensive, end-to-end process. To support this, have a clear model for the separation of concern between BPM and RPA. BPM was never designed to fully replace the work done by human beings, but rather to facilitate that work by assigning tasks, sequencing steps, enforcing rules, and other means of work management. In contrast, RPA in fact is purpose-built specifically to replace work otherwise less efficient and effective when performed by humans.
For example, one of the common mistakes is to create rules within the RPA definition (the configuration of software robots) which are complex, and thus miss the opportunity for separately managing decision logic (business policies and rules) from the procedural logic necessary to the automated task. No RPA platform is designed for decision management, yet a well-architected approach can and should leverage best of breed capabilities. As part of a broader automation or digital transformation strategy, we use decision management to ensure both consistency business rules, as well as enable workers to make better informed, data-driven decisions.
Consider this in the context of where RPA is most frequently applied today, the replacement of (typically offshore) manual transaction processing. There workers perform relatively repetitive tasks related to matters such as application processing or adjudication. The greatest challenge in these scenarios is to ensure workers follow the rules and policy guidelines for how work should be performed, which enforced via training, work instructions and SOPs, combined with surveillance based Quality Assurance (QA).
Imagine an alternative scenario where users are relieved of subjective decision making (i.e., having to rely on their own interpretation of policies and rules) and instead their work flows through a library of business logic where 100s or 1000s of rules are applied to validate data accuracy, to ensure consistency with policy, and to present a data-driven recommendation for the best action to take next. This provides objective measure (actual reportable data and analytics) to demonstrate that work performed according to established policy. It also lowers the training burden, by removing the need to understand exactly what to do at each while ensuring greater accuracy and consistency—as each and every transaction, process step, and data element is checked automatically (rather than applying QA to only a small sample).
Expand the aperture on this scenario, and image BPM is what it does best by coordinating the end-to-end process, managing the sequencing of steps and state of process as it advances the span of control from one step to the next. Now with the much finer grain definition of how work must be performed, consider that many of the steps which had previously required human intervention can now be performed by software robots, coordinated by the master process, with the instructions provided not by an automation script, but a complete set of rules and policies able to scale to the complexity of your business. Over time the scope of this automation can grow to encompass an increasing number of erstwhile human tasks as performance data is captured and more is understood about how the work should be performed. This is the promise of RPA when combined with BPM and decision management– expanding the realm of workflow solutions to deliver greater quality and efficiency.