How to create the best RPA architecture
Interest in automating robotic processes has grown rapidly as global companies recognize the benefits of implementing automation software. In particular, RPA is able to execute rule-based, high-volume transaction tasks in a cost-effective and optimized manner. Unfortunately, many companies face challenges when deciding which tool suits their current and future business needs. To help organizations choose the tool to use to achieve their goals, this series examines the eight elements of a successful RPA deployment. Part one is about how to build the best RPA architecture.
There are countless RPA tools available today, all of which have different features and functions. There is no one-size-fits-all tool that works for every company. Therefore, companies need to think carefully about which software will best achieve their goals. In addition, they need to create a framework to optimize the use of RPA – and next-generation automation – based on existing systems and processes. Without a proper structure and design, the RPA operating model will fall apart and companies will have little to show for their investment.
Organizations should consider the following in order to build the ideal architecture for their RPA deployment and set up the technology to achieve the highest ROI.
Beauty of layered designs
Despite the differences in RPA tools, most companies that deploy automation use a layered design to perform complex processes. This approach breaks down the logic and functions of a project into different parts with the aim of making the project more understandable and developing the automation. Organizations use a layered design model to assign roles for both RPA software and team members. This creates a clear distinction between the automated and the processes that require human intervention. This model also makes it easier for team members to focus on the sections and parts that show their individual strengths.
Layered designs offer more than just work delegation. In some cases, organizations add sub-processes to their levels that break down individual processes into a hierarchy of smaller components. For example, a larger process in a tiered design could be employee onboarding automation, and the sub-process of that would be to create an account for the employee in the cost system, a task that RPA could very easily and effectively manage.
While this approach seems time-consuming for the RPA architecture – especially for complex projects – the division of processes into different levels and sub-components is of crucial importance for optimizing the provision and maintenance of automation. The more defined each level is, the more efficiently RPA can complete a full project – and the easier it is for team members to understand where they fit in.
Assisted automation vs. unsupported automation
RPA tools offer two modes of deployment: assisted automation and unsupported automation. Each model has its advantages and disadvantages. Before an organization formulates its RPA architecture, it is important to determine which model best fits the organization’s needs.
Assisted automation is when an RPA tool automates other applications that run on the employee’s desktop. It requires a user to trigger the automated steps of a process and is most commonly used to assist people in performing complex processes. The benefits of assisted automation include shorter project turnaround times, higher cost efficiency, and improved customer and employee experiences. The main disadvantage of assisted automation is that inconsistencies in desktop settings – such as: Changing graphics, resolutions of display settings, and so on – can cause the RPA to fail and sometimes the desktop to lock when the automated steps are followed.
However, unsupported automation does not require a human agent. In this case, the RPA software works independently and only alerts employees if something goes wrong. With unsupported automation, the RPA software works around the clock – an ideal scenario for optimizing an entire process. Unfortunately, this presents a significant challenge: in order to work, the unsupported automation requires structured information and well-defined rules that may not be available in every project use case.
Regardless of which model companies choose, it’s important not to rule out vendors just because their product doesn’t support both models. In some cases, having more than one tool in a company is the most beneficial approach.
RPA control center
Once the design and deployment has been determined, the next step in creating the optimal RPA architecture is to ensure that the tool has a fully functional control center with critical functionality related to error handling, process analysis, and resource allocation. The control center acts as a central interface through which all process commands are issued. From this center, administrators have the operational flexibility to properly start, maintain, and update their RPA systems. This makes managing tasks like resource allocation and project naming across the company more efficient and provides a holistic view of the performance of the RPA system.
To do this, organizations need to review the functionality of the RPA control center. Error handling, in particular, is key to ensuring that processes are reaching their full potential. While RPA tools may have processes to notify employees when a failure occurs, a more complete system will keep a detailed record of each instance so teams can more easily track down problems in less time.
The division of the processes into different levels and sub-components is crucial for optimizing the deployment and maintenance of automation.
Another important feature of an RPA control center is process analysis, which monitors the real-time status of processes to confirm that the RPA system is performing as expected. The analytical component of a control center is essential to properly auditing an automation tool so that improvements can be made.
RPA control centers should also have the ability to manage resource allocation so that administrators can easily control what tasks each computer will perform. The most effective RPA tools are the result of proper resource allocation based on data derived from the process analysis function. Combined, these features enable the most effective management of automation tools and enable companies to take full advantage of the power of RPA.
While there are a multitude of RPA tools out there today, deciding which is best isn’t the first question businesses should ask. First, you need to consider what the RPA architecture for deployment will look like:
- Creating multi-tier processes;
- Identify whether the project requires assisted or unsupported automation; and
- Verify that the RPA control center has all the necessary components.
This is the only way to ensure that an RPA deployment is successful from the start.
Editor’s note: This is the first in an eight-part series by David Brain, COO of Symphony Ventures, a consulting and managed services company, and Phil Fersht, CEO and chief analyst at analyst firm HfS Research.