Executive briefing
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Largest measured cost
Work is sitting in the “To Do” queue before anyone acts on it. This cost is pure wait, not effort. Cutting time in queue here, with WIP limits, faster pickup, or removing the hand-off, is usually the fastest win.
Stage “To Do”
1,536 item-hours spent waiting in this queue
Every figure here opens into its own formula, inputs and assumptions under Ranked frictions below. No pattern in this analysis cleared the evidence threshold, so this is the largest measured cost rather than a fitted recommendation. A workspace with more history behind each stage gives the diagnostics enough to name an intervention as well.
Findings and limits
Generated from demonstration data. These recommendations were computed by the real engine from a simulated company, not from any real organisation.
This sample is smaller than the evidence threshold CostFlow requires before it will recommend anything, so it recommends nothing. That refusal is the product working: a confident-sounding action drawn from a handful of items is exactly what costs an executive their trust. See the recommendations on a full-size organisation →
Supporting detail
Everything above is the decision. Everything below is the working behind it: each priced friction with its formula, what moved since last time, what could not be priced, and how much of your data the analysis could see.
What this analysis covers
2,229 USD of priced friction
Range 1,114 USD to 4,458 USD, across every finding below.
Ranked frictions
A friction is a place in your process that loses money without anyone deciding to spend it: work waiting in a queue, items aging past your threshold, commitments already overdue. Each one is a stage, not a person and not a ticket.
Ranked by expected cost, biggest first.
#1 Queue wait in stage “To Do”
1,062 USD 531 USD to 2,124 USD Confidence C
1,536 item-hours spent waiting in this queue
Work is sitting in the “To Do” queue before anyone acts on it. This cost is pure wait, not effort. Cutting time in queue here, with WIP limits, faster pickup, or removing the hand-off, is usually the fastest win.
How this number was computed
What is this? Estimated follow-up cost of 3 item(s) waiting in stage "To Do", observed from event history.
How was it computed? Σ over items: waitDays × queueWaitAttentionHoursPerDay × hourlyRate(role); waitDays = observed waitHours ÷ 24; low/high follow the attention-hours range
What data went in?
| Item | Wait (days) | Visits | Open now | Attention h/day | Rate | Subtotal |
|---|---|---|---|---|---|---|
| Quarterly access audit OPS-3 | 49 | 1 | yes | 0.1 to 0.4 | 90/h (rates.Ops) | 441 USD to 1,764 USD (expected ~882 USD) |
| Renew data processing agreement OPS-1 | 5 | 1 | no | 0.1 to 0.4 | 120/h (rates.Legal) | 60 USD to 240 USD (expected ~120 USD) |
| Vendor risk review OPS-2 | 10 | 1 | no | 0.1 to 0.4 | 30/h (defaultRate:unmapped-actor) | 30 USD to 120 USD (expected ~60 USD) |
What was assumed?
defaultRate:unmapped-actor= 30 USD/h (customized by customer)parameters.queueWaitAttentionHoursPerDay= 0.1 to 0.4 h/day (expected 0.2) (customized by customer)rates.Legal= 120 USD/h (customized by customer)rates.Ops= 90 USD/h (customized by customer)
Confidence C, limited by:
- C: Default hourly rate applied to unmapped (pseudonymized) actor(s).
- B: Includes open stage intervals measured to the analysis time.
#2 Overdue exposure in stage “To Do”
342 USD 171 USD to 684 USD Confidence A
19 item-days past their due date
Commitments in “To Do” are past their due date and still open. The cost is the chasing, re-planning, and stakeholder churn they create. Re-scoping or renegotiating these dates stops the bleed.
How this number was computed
What is this? Estimated chasing cost of 1 item(s) past their own due dates in stage "To Do".
How was it computed? Σ over items: overdueDays × overdueAttentionHoursPerDay × hourlyRate(role); low/high follow the attention-hours range
What data went in?
| Item | Days overdue | Due date | Attention h/day | Rate | Subtotal |
|---|---|---|---|---|---|
| Quarterly access audit OPS-3 | 19 | 1 Jul 2026 | 0.1 to 0.4 | 90/h (rates.Ops) | 171 USD to 684 USD (expected ~342 USD) |
What was assumed?
parameters.overdueAttentionHoursPerDay= 0.1 to 0.4 h/day (expected 0.2) (accepted by customer)rates.Ops= 90 USD/h (customized by customer)
Confidence A No binding constraints: fully observed data and customer-confirmed assumptions.
#3 Aging / stagnation in stage “To Do”
297 USD 148 USD to 594 USD Confidence B
11 item-days sitting beyond the aging threshold
Items in “To Do” have gone untouched past your 14-day mark, quietly accruing carrying cost. Clearing or closing the oldest items first recovers the most.
How this number was computed
What is this? Estimated attention cost of 1 item(s) aging beyond 14 days in stage "To Do".
How was it computed? Σ over items: excessDays × attentionHoursPerDay × hourlyRate(role); low/high follow the attention-hours range
What data went in?
| Item | Days beyond threshold | Attention h/day | Rate | Subtotal |
|---|---|---|---|---|
| Quarterly access audit OPS-3 | 11 | 0.15 to 0.6 | 90/h (rates.Ops) | 148 USD to 594 USD (expected ~297 USD) |
What was assumed?
parameters.agingThresholdDays= 14 days (customized by customer)parameters.attentionHoursPerDay= 0.15 to 0.6 h/day (expected 0.3) (customized by customer)rates.Ops= 90 USD/h (customized by customer)
Confidence B, limited by:
- B: Durations inferred from snapshot dates, not event history.
#4 Queue wait in stage “Review”
288 USD 144 USD to 576 USD Confidence B
288 item-hours spent waiting in this queue
Work is sitting in the “Review” queue before anyone acts on it. This cost is pure wait, not effort. Cutting time in queue here, with WIP limits, faster pickup, or removing the hand-off, is usually the fastest win.
How this number was computed
What is this? Estimated follow-up cost of 1 item(s) waiting in stage "Review", observed from event history.
How was it computed? Σ over items: waitDays × queueWaitAttentionHoursPerDay × hourlyRate(role); waitDays = observed waitHours ÷ 24; low/high follow the attention-hours range
What data went in?
| Item | Wait (days) | Visits | Open now | Attention h/day | Rate | Subtotal |
|---|---|---|---|---|---|---|
| Renew data processing agreement OPS-1 | 12 | 1 | yes | 0.1 to 0.4 | 120/h (rates.Legal) | 144 USD to 576 USD (expected ~288 USD) |
What was assumed?
parameters.queueWaitAttentionHoursPerDay= 0.1 to 0.4 h/day (expected 0.2) (customized by customer)rates.Legal= 120 USD/h (customized by customer)
Confidence B, limited by:
- B: Includes open stage intervals measured to the analysis time.
#5 Overdue exposure in stage “Review”
240 USD 120 USD to 480 USD Confidence A
10 item-days past their due date
Commitments in “Review” are past their due date and still open. The cost is the chasing, re-planning, and stakeholder churn they create. Re-scoping or renegotiating these dates stops the bleed.
How this number was computed
What is this? Estimated chasing cost of 1 item(s) past their own due dates in stage "Review".
How was it computed? Σ over items: overdueDays × overdueAttentionHoursPerDay × hourlyRate(role); low/high follow the attention-hours range
What data went in?
| Item | Days overdue | Due date | Attention h/day | Rate | Subtotal |
|---|---|---|---|---|---|
| Renew data processing agreement OPS-1 | 10 | 10 Jul 2026 | 0.1 to 0.4 | 120/h (rates.Legal) | 120 USD to 480 USD (expected ~240 USD) |
What was assumed?
parameters.overdueAttentionHoursPerDay= 0.1 to 0.4 h/day (expected 0.2) (accepted by customer)rates.Legal= 120 USD/h (customized by customer)
Confidence A No binding constraints: fully observed data and customer-confirmed assumptions.
Context
Context signals explain conditions behind frictions. They are never priced or ranked.
- 2 of 3 in-flight items (67%) sit in queue- or review-kind stages; the largest single pool is stage "In Progress" (1 items).
inFlight: 3, queueKind: 1, reviewKind: 1, activeKind: 1, blockedKind: 0, waitingSharePercent: 67
Coverage & confidence
- Aging / stagnation: ran, 1 finding(s)
- Queue wait: ran, 2 finding(s)
- Overdue exposure: ran, 2 finding(s)
Every figure is an estimate shown as a range, computed from your own work items and the rates you confirmed, and traceable to its formula (open “How this number was computed”). Confidence A/B/C reflects how much we observed versus inferred. Where a required input isn't confirmed, we leave the item unpriced rather than guess. Ref golden-demo-jira
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