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Auto-balancing robot workload across work groups

Suggest edit Updated on October 7, 2021

Pega Robot Manager uses Auto-balancing to dynamically move robots between work groups to maximize robot efficiency and ensure robotic work assignments are processed within SLA.

By continuously monitoring work groups and work queues, Robot Manager determines which work groups require more robots to complete all of their assignments on time, and which work groups have more robots than required based on the service-level agreement (SLA) of open assignments and the work group priority. As a result, Robot Manager dispatches robots across work groups to ensure that the number of robots in a work group is enough to efficiently complete all open assignments.

Automatic workload balancing provides the following advantages in terms of efficiency and costs:

  • Automatic repurposing keeps available robots busy, with minimal idle time.
  • Robot administrators can focus on other tasks because they don't need to manually move robots between work groups or create schedules.
  • As the robots are constantly redirected to complete open assignments across multiple work groups, you need fewer robots to complete the incoming work.
What to do next: You can control how Robot Manager distributes the workload among your robots by performing the following actions:
  • Designating work groups for auto-balancing of robot workload

    Determine whether Pega Robot Manager can move robots between work groups in your application, based on the service-level agreement (SLA) of assignments and robot availability. To ensure that robots are allocated to complete essential assignments first, you can include work groups in automatic workload balancing and determine the work group priority.

  • Managing service-level agreement of robotic assignments

    A service-level agreement (SLA) determines the specific time by which robots must complete all instances of a specific assignment type. By defining an SLA, you can measure the performance and service of your robots, based on your business objectives.

  • Estimating the workload across work groups

    Control how Pega Robot Manager anticipates workload across work groups by adjusting the Auto-balancing evaluation interval.

  • Analyzing auto-balancing data

    Understand how Pega Robot Manager balances workload across work groups by reviewing the Analysis dashboard that provide a graphical insight into robot status, current assignments, and work group capacity.

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