VP Supply Chain: furniture shrinkage, read from Google BigQuery
Ward reads any BigQuery dataset, gA4 event exports, ads data transfers from Google BigQuery, keeps a running read on shrinkage across 10,000+ furniture SKUs, and hands you the supply chain read each day.
How a furniture VP Supply Chain runs shrinkage on Google BigQuery
For Furniture Manufacturing & Retail retailers, that means monitoring 10,000+ SKUs over locations. ERP-locked production data, long lead times, and margin erosion you don't see until quarter-end. Ward connects your internal systems and surfaces what matters.
You find out about stockouts after customers do. Ward filters to what a VP Supply Chain can act on and drops the rest.
What shrinkage detection does: Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
The mechanism. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Setup: Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
Key capabilities
- Receiving dock anomaly detection
- Pattern recognition across time
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled upholstery cost of goods by BOM line against the last price file. Three quarters of the drop is material and freight, not discounting.
| Signal | Finding |
|---|---|
bom_cost_actuals | Foam and frame stock +9.2% since the March price file, never carried to list |
freight.inbound | Inbound container cost +$412 per unit-equivalent on the Vietnam lane |
channel.mix | Wholesale share up 6pp, and wholesale runs 11pp under DTC margin |
Recommend: reprice the six affected SKUs at the next list cycle, quote the alternate foam vendor, and hold wholesale allocation flat until list catches up.
bom_cost_actuals…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
epicor_production_stage_log | import | 2m ago |
epicor_bom_cost_actuals | import | 2m ago |
sap_inventory_snapshot | import | 14m ago |
netsuite_sales_orders | import | 1h ago |
retail_showroom_pos | import | 1h ago |
retail_freight_inbound | import | 1h ago |
retail_dealer_orders | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
finance-read-default | permit | Model::* |
sourcing-read-bom | permit | Model::"bom_cost_actuals" |
dealer-blocked | forbid | Model::"bom_*" |
plant-read-production | permit | Model::"production_stage_log" |
Why Shrinkage matters for Furniture retail
Furniture shrink rarely looks like theft. It shows up as freight damage, warehouse handling loss, and customer damage claims against high-ticket units where a single write-off erases the margin on several sales. Ward separates damage by cause, freight lane, DC, and manufacturing defect, so loss stops being a lump on the P&L and becomes a set of fixable process failures.
What Ward has eyes on.
Ward pulls from any BigQuery dataset, gA4 event exports, ads data transfers from Google BigQuery on a read-only connection. Nothing is written back, and your Data Platform configuration does not change. BigQuery stays the system of record.
Find the leak before it drains you. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
Every one of your locations gets its own baseline. Ward scans continuously inventory carrying cost, order-to-delivery cycle, gross margin by channel against it and surfaces only the deviations that hold up. The two that show up most in furniture retail are disconnected ERP, warehouse, and POS systems and custom/configurable SKUs that break standard reporting, and both are baseline problems before they are P&L problems.
At the metric level. Ward tracks damage rate by product category, freight lane, carrier, DC, and delivery leg, and separates manufacturing defect returns from in-transit and last-mile damage. On 3 to 6% net margins, a single damaged sectional can wipe out the profit on several clean sales, so cause attribution is where the money is.
Why this combination
is its own problem.
Shrinkage Detection produces a lot of output that is technically correct and operationally useless to supply chain. Ward filters on whether the finding changes a decision a VP Supply Chain can actually make.
- 01 Last-mile delivery damage is blended with warehouse damage, hiding which delivery teams or routes are driving claims.
- 02 High-value single-unit write-offs are averaged into a category rate, masking that a handful of hero SKUs account for most of the dollar loss.
Benchmarks. Furniture damage and shrink commonly runs 1 to 3% of goods value, with freight and last-mile handling the largest components for case goods and upholstery. Recovering even half of carrier-caused damage through documented claims typically returns 0.5 to 1 point of margin on affected categories.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Google BigQuery. Ward reads any BigQuery dataset, gA4 event exports, ads data transfers and starts building baselines. First insight cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward hands you insight cards daily, each with the cause and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many findings arrive, it is how many get acted on.
How Ward connects to Google BigQuery
Ward queries BigQuery using your existing datasets. GA4 exports, POS data, CRM exports. Ward reads it where it lives.
Setup: Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
Data Ward reads from BigQuery
Impact metrics with BigQuery
Data lake enrichment
Ward enriches BigQuery data with: Any BigQuery dataset, GA4 event exports, Weather & events, Demographics, Custom feeds
You find out about stockouts after customers do.
- ×Demand forecasts are off by 15-25% and nobody catches it until the shelf is empty
- ×Supplier fill rate problems announce themselves at the receiving dock
- ×Safety stock levels get set once a year and left alone
- ×No early warning system for supply chain disruptions
- ×Replenishment exceptions require manual triage every morning
- ✓Stockout prediction cards arrive 24-72 hours before empty shelves
- ✓Supplier fill rate tracking with automatic escalation
- ✓Dynamic safety stock recommendations based on current demand signals
- ✓Weather, event, and macro-driven demand adjustments
- ✓Replenishment exceptions auto-prioritized by revenue impact
Stockouts cost retailers $1.14 trillion in missed sales globally each year. Source: IHL Group
Furniture KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. For Furniture retail specifically, Ward monitors 10,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Inventory carrying cost, Order-to-delivery cycle, Gross margin by channel, Raw material cost variance, Custom order cycle time at the store-category level. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule. Data points include: Any BigQuery dataset, GA4 event exports, Ads data transfers, Custom ETL outputs.
Yes. Ward reads BigQuery data and combines it with contextual signals (weather, events, demographics) to generate Furniture-specific insight cards. No custom development required.
You find out about stockouts after customers do. Ward solves this with automated insight cards: Stockout prediction cards arrive 24-72 hours before empty shelves. Supplier fill rate tracking with automatic escalation. Dynamic safety stock recommendations based on current demand signals.
Ward delivers daily insight cards covering Inventory carrying cost, Order-to-delivery cycle, Gross margin by channel, tailored for Supply Chain decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks damage rate by product category, freight lane, carrier, DC, and delivery leg, and separates manufacturing defect returns from in-transit and last-mile damage. On 3 to 6% net margins, a single damaged sectional can wipe out the profit on several clean sales, so cause attribution is where the money is.
Damage write-offs on case goods climb two points over a quarter. The blended number looks like normal handling loss. Ward segments it and shows the increase is concentrated on one LTL carrier lane serving three DCs, with dining tables and dressers taking the hits. The pattern points to handling on that lane, not manufacturing. Ward opens the case with the evidence, and the claims team reroutes the lane and files carrier recovery on the documented units.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
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Root causes, not just alerts. See it on your data.
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