Introduction
The increased error rates in Blue Prism's Robotic Operating Model deployments this month present a critical challenge that requires immediate attention and a systematic approach to resolution. As we delve into this issue, we'll employ a structured framework to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, there was a minor update to the deployment process. Impact on approach: If confirmed, we'd focus on the changes made and their potential impact.
Why it matters: Helps identify if the issue is universal or specific to certain use cases. Expected answer: The issue is more pronounced in larger, more complex deployments. Impact on approach: We'd investigate factors specific to complex deployments if this is the case.
Why it matters: Different error types point to different root causes. Expected answer: There's an increase in runtime errors specifically. Impact on approach: We'd focus on factors that could affect runtime performance.
Why it matters: Changes in underlying processes can impact RPA performance. Expected answer: No significant changes reported by clients. Impact on approach: We'd shift focus to internal factors if no external changes are identified.
Practice similar questions
Subscribe to access the full answer