Parallel Deployment in RPA

What is a Parallel Deployment in RPA?

A parallel deployment involves running the new RPA solution alongside the existing processes or systems for a specific period. Both systems operate simultaneously, allowing for a gradual transition to the automated solution while ensuring the continuity of operations.

When to Use a Parallel Deployment in RPA

A parallel deployment is most suitable in the following scenarios:

1. Critical Process Automation: For processes where continuity is essential, running both systems ensures operations are not disrupted.

2. High-Risk Environments: In industries like finance or healthcare, where errors could have significant consequences, parallel deployments provide a safety net.

3. Complex Transitions: When replacing highly integrated or legacy systems, this approach allows for careful validation.

4. Low Tolerance for Downtime: For organizations where downtime is not an option, this strategy ensures uninterrupted operations.

5. Data Validation Needs: Parallel deployment is ideal for scenarios requiring real-time comparison of data between the old and new systems to ensure accuracy.

6. User Training: When employees need time to adapt to the new system, running both systems simultaneously provides a buffer.

Benefits of a Parallel Deployment in RPA

1. Risk Mitigation

- Ensures continuity of operations by keeping the existing system operational during the transition.

- Provides a fallback option in case the new system encounters issues.

2. Real-Time Validation

- Allows for direct comparisons between outputs from the old and new systems, ensuring data accuracy and consistency.

- Helps identify and resolve discrepancies before fully decommissioning the old system.

3. User Confidence

- Users can gradually adapt to the new system while retaining access to the familiar old system.

- Reduces resistance to change by allowing a smoother transition.

4. Controlled Transition

- Facilitates a gradual shift to automation, reducing the stress associated with abrupt changes.

Challenges of a Parallel Deployment in RPA

1. Resource Intensive

- Maintaining and supporting two systems simultaneously requires additional resources and effort.

- Increases operational costs during the transition period.

2. Complexity

- Managing parallel operations can be complex, especially for large-scale processes with numerous dependencies.

- Requires careful coordination to avoid conflicts between the old and new systems.

3. Extended Transition Period

- Running both systems for an extended period can delay the realization of full benefits from automation.

4. Scope Creep

- Prolonged reliance on the old system may lead to delays in fully adopting the new solution.

Steps for Implementing a Parallel Deployment in RPA

1. Plan the Transition

- Identify processes to be automated and ensure compatibility with the existing system.

- Define the duration for which the old and new systems will run in parallel.

2. Prepare Infrastructure

- Ensure that both systems can operate simultaneously without performance degradation.

- Set up monitoring tools to track outputs and performance metrics from both systems.

3. Test Extensively

- Conduct end-to-end testing to validate the new system’s performance.

- Compare test results with outputs from the existing system to ensure consistency.

4. Train Users

- Provide training sessions to help users familiarize themselves with the new system.

- Encourage feedback during the parallel run to address user concerns.

5. Monitor and Compare

- Continuously monitor outputs from both systems to identify and resolve discrepancies.

- Use the insights gained to optimize the automated processes.

6. Transition Fully

- Decommission the old system once the new system is stable, accurate, and trusted by users.

- Ensure all stakeholders are aligned before fully transitioning to the automated solution.

Best Practices for Parallel Deployments in RPA

1. Set Clear Objectives

- Define success criteria for the parallel deployment phase, such as error thresholds and performance benchmarks.

2. Stakeholder Engagement

- Involve stakeholders in planning and execution to ensure alignment and buy-in.

- Communicate the benefits and timeline of the parallel deployment clearly.

3. Robust Monitoring

- Implement real-time monitoring tools to track performance, detect issues, and validate outputs.

4. Limit the Duration

- Set a clear timeline for the parallel run to avoid prolonged reliance on the old system.

- Focus on resolving discrepancies quickly to facilitate a smooth transition.

5. Document Everything

- Maintain detailed documentation of issues encountered and resolutions implemented during the parallel phase.

6. Support Teams

- Deploy dedicated teams to address technical issues and support users during the transition.

Key Metrics to Track During a Parallel Deployment

- Output Accuracy: Consistency between outputs from the old and new systems.

- Error Rates: Frequency and nature of discrepancies identified during the parallel run.

- System Performance: Stability and speed of the new system under load.

- User Feedback: Satisfaction scores and feedback on the new system’s usability.

- Transition Progress: Percentage of processes or users fully transitioned to the new system.

- Operational Costs: Additional costs incurred due to maintaining dual systems.

The parallel deployment strategy in RPA is an effective approach for organizations prioritizing risk mitigation and operational continuity. By running old and new systems simultaneously, businesses can validate outputs, build user confidence, and ensure a smooth transition to automation. While resource-intensive and complex, this strategy’s benefits in terms of accuracy, reliability, and controlled change make it a preferred choice for critical or high-stakes processes. With careful planning, robust monitoring, and a clear transition plan, parallel deployment can pave the way for a successful RPA implementation.