Robotic Process Automation (RPA) is a business process automation technology based on software robots (bots) or artificial intelligence (AI)/digital workers. RPA is software module robots, in contrast to hardware robots used in manufacturing production lines or warehouse pick-and-place tasks. RPA is often used to automate repetitive manual processes.

In traditional workflow automation, a software developer codes a list of actions to automate and interface to back end system using application programming interfaces (APIs) or dedicated scripting language. RPA systems, in contrast, create the action list by watching the user perform tasks in the application’s graphical user interface (GUI), and automate by repeating those tasks directly in the GUI.

An enterprise business process can be operated by numerous bots. For example, a simple order fulfillment process may use multiple bots to automate various order sub-processes. However, as the number of deployed bots increase, the management of the bots, their synchronization with the central controller, and maintenance, is a major challenge. In essence, “how do we manage the bots?”

Why RPA Management (RPAM)?

It is evident that organizations deploy RPA bots to automate repetitive, manual processes. RPA bots also require significant human support. But more critically, how are these bots managed to ensure that they are operating as they should, and any dependency changes, such as, RPA software, infrastructure, cloud/virtual environment updates, or end-user device changes, do not result in operational failures? After all, the more the number of operational bots, the larger will be the number of failures, leading to larger service issues, increased operational costs, and poor customer satisfaction. RPAM avoids just that and ensures that bots function as expected while continuously monitoring the environment.

Since bots operate with multiple dependencies (applications, devices, networks, infrastructure), repairing a bot will require skills in these domains. The more the number of operational bots the larger the staff size, skill breadth, and capacity required. Companies also often use multiple bot vendors which further increase support costs due to bot diversity. It is estimated that on the average, maintaining and servicing 8 to 16 bots requires 1 FTE annually. This equates to between 130 to 260 hours of annual manual support requirement per bot.

Intelligent and automated bot support is the only solution to reducing the life-cycle total cost of RPA ownership. An RPAM environment effectively and efficiently manages the complexity of large-scale bot deployments by monitoring, measuring, and maintaining bots while proactively remediating failures. RPAM environments should also be designed with a controller to act as an intermediary between the RPA software orchestrator in the cloud and the operational bots. In this capacity, RPAM should observe the inter-dependencies between the orchestrator and the bots, and oversee any impending failures due to software upgrades, container re-distributing, scheduling changes, and other site specific (hybrid, on-premise, or cloud) variations. With proactive reporting and appropriate alerts, it engages high-value human resources only when needed. Organizations, therefore, need to consider this maintenance cost when considering total cost of ownership for RPA vendor offers and evaluate automated RPAM solutions as well.

It is evident that RPA yields significant operational and customer experience benefits. It is also clear that as organizations automate more functions with bots from multiple vendors, bot maintenance costs will also proportionally increase, with skill requirements being in premium. Intelligent and automated RPAM is the only solution to this problem. It is a win-win-win for all – the customer, the employees, and the organization.

 

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