# Inspect an arrival burst inside a review window

Illustrative planning brief; no automatic product import.

Synthetic planning case: Eight requests arrive at 09:00. The reviewer processes one per hour from 09:00 to 17:00, with no later arrivals. A receiving requirement asks whether all can finish by 11:00.

## Decision

Daily capacity of eight fits the daily total, but only two reviews finish by 11:00 and six remain. The two-hour receiving requirement is infeasible in this scenario.

## Owned work

- Retain the arrival timing
  - Owner role: Intake owner
  - Acceptance evidence: All eight requests enter at 09:00 instead of being averaged across the day.
- Check the receiving window
  - Owner role: Queue planner
  - Acceptance evidence: Only two service slots exist before 11:00 under the declared rate.
- Resolve the early-window requirement
  - Owner role: Decision owner
  - Acceptance evidence: Change scope, qualified capacity or receiving time explicitly; a daily total is not the answer.

## Workflow

1. Retain the arrival timing. Check: All eight requests enter at 09:00 instead of being averaged across the day.
2. Check the receiving window. Check: Only two service slots exist before 11:00 under the declared rate.
3. Resolve the early-window requirement. Check: Change scope, qualified capacity or receiving time explicitly; a daily total is not the answer.

## Judgment

The example assumes deterministic one-hour service, a ready queue and no breaks. It is not a latency benchmark.


## Filled manual planning note

Daily capacity of eight fits the daily total, but only two reviews finish by 11:00 and six remain. The two-hour receiving requirement is infeasible in this scenario. By 11:00, two one-hour reviews have finished. Eight minus two leaves six; spreading the eight arrivals into an average of one per hour would hide the actual burst. The example assumes deterministic one-hour service, a ready queue and no breaks. It is not a latency benchmark.


## Workflow questions

### Does a balanced daily total prove acceptable waiting?

No. Arrival timing and the receiving window determine which deadlines can be met.

### Can I claim one review per hour in real work?

Not from this invented scenario; actual service conditions need evidence.

## Product connection

Use the owned checks and downloaded brief to discuss this planning decision alongside your TeamBoostAI tasks. Confirm available fields, roles and account features separately. The example is manual; it does not calculate live analytics, create work or run an experiment in the product.

Confirm account availability before adopting this manual outline.

## Original worked case

Synthetic records, manual planning only. No account import or live analytics.

### Inspect the invented case records

Checkpoint | Cumulative arrivals | Cumulative reviews | Pending
--- | --- | --- | ---
09:00 | 8 | 0 | 8
11:00 | 8 | 2 | 6
17:00 | 8 | 8 | 0

### Reasoning

By 11:00, two one-hour reviews have finished. Eight minus two leaves six; spreading the eight arrivals into an average of one per hour would hide the actual burst.

### Bounded result

Daily capacity of eight fits the daily total, but only two reviews finish by 11:00 and six remain. The two-hour receiving requirement is infeasible in this scenario.

### Distinct decision

The guide tests a short-window burst against a specific receiving deadline.

### Limits

The example assumes deterministic one-hour service, a ready queue and no breaks. It is not a latency benchmark.

### Definitions and method context

- The Kanban Guide — https://kanbanguides.org/the-kanban-guide/ — Workflow and flow-measure context. Queue policies and staffing scenarios are explicitly local examples, not delivery guarantees.