BC-10
Burn Chart Dimensional Inconsistency
Burn Charts
Default severity: medium
PrioritizationMetricsOutputEffort
What it detects
The four burn dimensions (issue count, story points, time spent, business value) show contradictory signals — e.g. issue count burn-down looks healthy but story point burn-down is stalled. Indicates that completed issues are not the right size or type to deliver the committed value.
Detection formula
FOR each_pair (D1, D2) IN dimension_pairs: progress_D1 = done(D1) / total(D1) progress_D2 = done(D2) / total(D2) IF ABS(progress_D1 - progress_D2) > config.bc.dimensional_inconsistency_threshold // default: 0.25 THEN FLAG inconsistency between D1 and D2 Common pattern: issues_done_pct >> sp_done_pct = small stories completed, large stories untouchedExamples in practice
- A team shows the four burn dimensions (issue count, story points, time spent, business value) show contradictory signals while dimensional inconsistency threshold is set to 0.25.
- Example signal: The four burn dimensions (issue count, story points, time spent, business value) show contradictory signals — e.
Suggested response
Re-plan remaining scope when burn charts show late work, scope creep, or stalls.
Coaching playbook
Symptom
The four burn dimensions (issue count, story points, time spent, business value) show contradictory signals — e.g. issue count burn-down looks healthy but story point burn-down is stalled. Indicates that completed issues are not the right size or type to deliver the committed value.
Why it matters
When "Burn Chart Dimensional Inconsistency" keeps appearing, the team is signalling a repeatable process gap. Left unexamined, the pattern hides where work really stalls and makes improvement metrics harder to trust.
What you can achieve
Re-plan remaining scope when burn charts show late work, scope creep, or stalls.
Facilitation questions
- What system change would stop "Burn Chart Dimensional Inconsistency" from firing again?
- What do the cited issues have in common — same root cause or same workaround?
- Who owns the two-week experiment and how will we verify on the next import?
Run this rule against your own tracker data with Flow Analyzer.