Key Indicators for the Period Analyzed
This section presents six key indicators that summarize the project’s performance for the selected period.
1. Period Analyzed
Displays the start and end dates of the selected period.
Example:
2024-07-01 - 2024-09-30
2. Real Weeks
Represents the total number of weeks included in the selected period.
Example:
If the user selects Last 3 months, the period contains approximately 12 weeks.
3. Active Weeks
Indicates how many of the weeks in the selected period the project was actually active.
Example:
If the user selects Last 3 months (12 weeks), but the project began operations only 8 weeks ago:
Active weeks = 8
All performance calculations use Active Weeks, not Real Weeks.
When the selected period fully matches the project's active timeline:
Real Weeks = Active Weeks
4. Throughput bruto
What is Throughput?
Throughput represents the number of items (issues) completed during a given period.
It is a measure of delivery capacity.
Definition
Throughput bruto includes all resolved issues, even those that contain inconsistencies.
Formula
Throughput bruto = Total Resolved Issues / Active Weeks
5. Throughput real
Concept
Throughput real reflects the team's clean performance.
It considers only the issues resolved correctly, without inconsistencies.
Formula
Throughput real = Correctly Resolved Issues / Active Weeks
6. Inconsistency (%)
What is an inconsistent issue?
An inconsistent issue is one that contains errors such as incorrect status, bad resolution, missing information, wrong categorization, or any data-quality irregularity.
Definition
Indicates what percentage of all resolved issues contain inconsistencies.
Formula
Inconsistency (%) = (Inconsistent Issues / Total Resolved Issues) * 100
Interpretation of Throughput and Inconsistency
💡 Purpose: Help users clearly understand what these metrics say about performance and data quality.
Throughput and inconsistency together provide a complete view of how the team is performing.
A high throughput indicates stronger delivery capacity — the team is completing more work per active week.
However, throughput alone does not measure correctness or quality.
A high inconsistency percentage highlights that many of those completed issues contain errors or irregularities, such as incorrect statuses or wrong resolutions. In this situation:
- The team may appear productive, but the work may require rework.
- Real throughput decreases because only correctly resolved issues count as clean output.
- Data becomes unreliable for reporting, forecasting, or continuous improvement.
✔️ Ideal scenario:
High real throughput + Low inconsistency = strong productivity and clean, reliable delivery.
How to Address High Inconsistency Levels
🚨 When inconsistency is high, you likely have process or configuration issues. Below are recommended actions to improve data quality and reduce inconsistent resolutions.
1. Review and tighten workflow configuration
- Ensure closing statuses require a valid resolution.
- Remove invalid or unused resolutions from transitions.
- Prevent transitions from skipping required final steps.
2. Strengthen governance around the Resolution field
- Restrict permissions so only authorized roles can set or modify the Resolution.
- Prevent the field from being manually editable at any time.
- Use post-functions to automatically set consistent resolutions when appropriate.
3. Add validation rules and automation
- Add workflow validators to ensure required fields are filled before closing.
- Use automation rules to enforce consistent categorizations or fix common mistakes.
- Automatically flag or reopen issues with incorrect resolutions.
4. Establish process discipline
- Train the team on proper closing practices and the correct use of resolutions.
- Maintain and communicate a clear “Definition of Done.”
- Conduct periodic reviews of inconsistent issues to identify patterns.
⭐ Goal: Reduce rework, improve reporting accuracy, and ensure the system reflects the true state of work.