CA-09
WIP Age Inflation — Stale Items in Visible WIP
CFD Advanced
Default severity: medium
PrioritizationProcessMetricsOutputRisk
What it detects
A configurable proportion of items in active stages have been in their current status longer than the configured staleness threshold — visible WIP is inflated by stale items that are not genuinely in progress.
Detection formula
staleness_threshold = config.ca.wip_staleness_days // default: avg_cycle_time FOR each active_stage S: stale_items = COUNT(issues WHERE current_status = S AND time_in_status > staleness_threshold) stale_share = stale_items / COUNT(all_items_in_S) IF stale_share > config.ca.max_stale_share // default: 30% THEN FLAG — report effective_wip vs inflated_wip per stageExamples in practice
- A team shows a configurable proportion of items in active stages have been in their current status longer than the configured staleness threshold while wip staleness days is set to avg_cycle_time.
- Example signal: A configurable proportion of items in active stages have been in their current status longer than the configured staleness threshold — visible WIP is inflated by stale items that are not genuinely in progress.
Suggested response
Use planning-event context when interpreting advanced CFD bottlenecks, spikes, and ageing WIP.
Coaching playbook
Symptom
A configurable proportion of items in active stages have been in their current status longer than the configured staleness threshold — visible WIP is inflated by stale items that are not genuinely in progress.
Why it matters
When "WIP Age Inflation — Stale Items in Visible WIP" 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
Use planning-event context when interpreting advanced CFD bottlenecks, spikes, and ageing WIP.
Facilitation questions
- What system change would stop "WIP Age Inflation — Stale Items in Visible WIP" 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.