What a furniture Head of Procurement pulls out of BigQuery
Ward ingests Google BigQuery and sends furniture cards scoped to procurement. Merchandising wants Ward. You sign the contract.
Ward and BigQuery for Furniture procurement
A Furniture Manufacturing & Retail operator is tracking 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.
Merchandising wants Ward. You sign the contract. Ward filters to what a Head of Procurement can act on and drops the rest.
Getting the data in. Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
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" |
What Ward delivers
- MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- Transparent pricing set by scope and store count
- 14-day insight guarantee. If Ward doesn't deliver, month two is on us
- AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Metrics Ward monitors
Furniture challenges Ward solves
- Disconnected ERP, warehouse, and POS systems
- Custom/configurable SKUs that break standard reporting
- 8–16 week lead times with no demand signal
- Raw material cost drift invisible until P&L close
- Channel mix shift between showroom, e-commerce, and wholesale
What Ward has eyes on.
Ward reads straight 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.
Coverage is store by store, category by category. Ward scans continuously inventory carrying cost, order-to-delivery cycle, gross margin by channel across 10,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the footprint is fine and knowing which seven locations are not.
Why this combination
is its own problem.
A Head of Procurement in furniture retail owns numbers that move faster than the reporting cycle that covers them. Inventory carrying cost, order-to-delivery cycle, gross margin by channel shift store by store, on a daily cycle. A monthly pack cannot represent that. Merchandising wants Ward. You sign the contract.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Google BigQuery. Ward pulls from any BigQuery dataset, gA4 event exports, ads data transfers and starts building baselines. First findings 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: operating rhythm
Cards arrive every morning and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
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
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
Furniture KPI impact
Frequently asked questions
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.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Inventory carrying cost, Order-to-delivery cycle, Gross margin by channel, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
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
See what Furniture problems Ward catches.
Root causes, not just alerts. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.