> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# ShipBob audit profile, Vortex IQ

> What the Vortex IQ ShipBob health audit checks: ShipBob: SLA, Stockouts, Capacity & Cross-Channel Leak

**[Nerve Centre KPIs](/nerve-centre/kpi-cards/shipbob) · [Audit Profile](/nerve-centre/kpi-cards/shipbob/audit) · [Sentiment Settings](/nerve-centre/kpi-cards/shipbob/sentiment)**

ShipBob is the merchant's physical operations layer. Findings here either cost the merchant a customer (SLA miss, late delivery, stockout) or burn margin (carrier degradation, FC overload, return-rate spike). Cross- references commerce siblings (Shopify / BigCommerce / Adobe / Amazon) and ad-platform siblings (Google Ads / Amazon Ads) to compute revenue-at-risk and to auto-pause spend when the goods can't actually ship.

## What this audit checks

### Authentication & access

* PAT still valid (auth on /1.0/channel)
* Channel(s) accessible - agency view shows >0 channels
* Sandbox vs production mode flagged correctly (avoid demo data leaking to live findings)

### SLA & on-time delivery (the customer-promise area)

* Orders breaching SLA in flight (any > 0 = active customer-experience risk)
* SLA compliance by warehouse \< 90% (which FCs are missing the promise)
* OTD rate trending below 90% for 3+ days
* Delayed orders > 10% in last 7 days
* Pick/pack time > 24h at any FC

### Stockout-with-demand (the highest-leverage cross-channel area)

* OOS SKUs where committed > 0 - actively losing sales NOW
* Cross-channel: OOS SKU appears in active amazon\_ads campaign - \$ wasted
* Cross-channel: OOS SKU appears in active google\_ads campaign - \$ wasted
* Cross-channel: OOS SKU listed on shopify/bigcommerce DTC product page without 'sold out' state
* Days-of-cover \< 14 on top-velocity SKUs (reorder-window trigger)
* Inventory ageing > 120d on flagship SKUs (slow-mover cash drag)

### Warehouse capacity & throughput

* FC capacity utilisation > 85% (overload signal)
* Warehouse backlog > 100 orders / FC (live bottleneck)
* Warehouse error rate > 3% (quality issue at FC)
* Throughput drop > 25% WoW at any FC
* Single-FC concentration on top-velocity SKUs (resilience risk)

### Carrier health

* Carrier OTD \< 85%
* Carrier last-mile time > 12h
* Carrier exception rate spike > 2σ vs 30d baseline
* Single-carrier dependency > 80% of volume on a region (resilience risk)

### Returns intelligence

* Return rate > 8% overall
* Per-SKU return rate > 15% (defective product signal)
* Return reason cluster shift - new dominant reason emerging WoW
* Return processing time > 7 days (reverse-logistics bottleneck)
* Return rate spike > 2σ on a specific region (regional product/sizing issue)

### Cost & margin

* Shipping cost / order up > 10% vs prior period
* Fulfilment cost / order > \$10 at any FC
* Cost-as-% of revenue > 15% (margin pressure)
* High-cost shipment outliers > 2× avg (investigate routing)

### Agency / multi-merchant signals (only when channel\_count > 1)

* Per-channel fulfilment rate \< 95% (which client needs help)
* Per-channel OTD \< 90% (which brand is suffering)
* Per-channel SLA \< 90% (account-management priority)
* Per-channel inventory risk heatmap (cross-brand stock health)

## Severity thresholds

| Signal                               | Warn | Critical |
| ------------------------------------ | ---- | -------- |
| `orders_breaching_sla`               | 1    | 5        |
| `sla_compliance`                     | 95   | 90       |
| `on_time_delivery_rate`              | 95   | 90       |
| `warehouse_capacity_pct`             | 75   | 85       |
| `warehouse_error_rate_pct`           | 2    | 3        |
| `pickpack_time_hrs`                  | 18   | 24       |
| `stockout_with_demand_count`         | 1    | 5        |
| `days_of_cover`                      | 21   | 14       |
| `inventory_ageing_days`              | 90   | 120      |
| `carrier_otd_pct`                    | 90   | 85       |
| `return_rate_pct`                    | 5    | 8        |
| `per_sku_return_rate_pct`            | 10   | 15       |
| `shipping_cost_per_order_change_pct` | 5    | 10       |
| `cost_pct_of_revenue`                | 12   | 15       |

## Data sources

* `GET https://{api_host}/1.0/channel` - Auth + multi-channel discovery
* `GET https://{api_host}/1.0/location` - FC inventory, capacity, status
* `GET https://{api_host}/1.0/inventory` - Per-SKU per-FC inventory snapshots (the OOS join key)
* `GET https://{api_host}/1.0/order` - Order status + age (SLA evaluation)
* `GET https://{api_host}/1.0/shipment` - Shipment timing, carrier, cost, OTD calculation
* `GET https://{api_host}/1.0/tracking` - Live tracking events + last-mile timing
* `GET https://{api_host}/1.0/return` - Return rate + reasons + processing time
* `GET https://{api_host}/1.0/receiving` - Inbound POs - feeds inventory ageing + stock-on-water signals