What a furniture CFO sees in assortment first
Ward pulls forward assortment findings scoped to finance, throughout every one of your furniture locations.
Assortment Planning for Furniture finance
For Furniture Manufacturing & Retail retailers, that means continuous review 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.
Your P&L surprises are born on the store floor. Ward hands you findings scoped to finance decision-making.
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
How Ward hands back Assortment findings: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Key capabilities
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
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 Assortment matters for Furniture retail
Showroom floor space is the most expensive shelf in retail, and every piece on it competes for placement against the online-only long tail. Ward measures sell-through and margin per square foot of floor by showroom cluster, so you can decide which pieces earn a physical slot, which move to web-only, and where a gap in the assortment is costing you the sale.
Why this combination
is its own problem.
Assortment Planning behaves differently in furniture retail than it does anywhere else. The store base shape, the SKU count, and the speed of the category all change what counts as a real indicator and what is noise. Ward is tuned to the furniture version.
- 01 Hero pieces are kept on the floor out of habit long after their velocity has faded, crowding out the groupings that would turn faster.
- 02 One national floor plan ignores that urban and suburban clusters buy completely different styles, so every store carries someone else's slow movers.
Benchmarks. Furniture floor productivity varies widely: top-quartile showrooms generate several times the revenue per square foot of the bottom quartile on the same footprint. Rationalizing the slowest 15 to 20% of floor SKUs into web-only and backfilling with cluster-matched groupings commonly lifts floor revenue 5 to 12%.
What Ward has eyes on.
Ward watches 10,000+ SKUs throughout your locations, at the store-category level rather than the chain roll-up. The metrics under watch include inventory carrying cost, order-to-delivery cycle, gross margin by channel. A roll-up hides a single-store problem inside a healthy average, which is how disconnected ERP, warehouse, and POS systems stays invisible for a quarter.
Stock what sells. Cut what doesn't. 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.
At the metric level. Ward measures sell-through, margin, and revenue per floor square foot by SKU and showroom cluster, scores online-versus-showroom fit per piece, and flags whitespace where demand exists but no product is placed. Floor space is finite and costly, so the unit of analysis is productivity per slot, not raw units sold.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward pulls from your existing systems on a read-only connection. Nothing is written back. First insight cards arrive in two days.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the daily cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands back findings every morning, each with what caused it and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.
Your P&L surprises are born on the store floor.
- ×Margin erosion only surfaces at month-end close
- ×Inventory carrying costs are a black box
- ×Working capital tied up in slow-moving stock nobody is watching
- ×Same-store sales comps lack decomposition into actionable drivers
- ×Capex decisions for store remodels lack unit-economics evidence
- ✓GMROI tracking by category with weekly insight cards
- ✓Inventory carrying cost alerts when capital efficiency drops
- ✓Working capital optimization recommendations based on turnover trends
- ✓SSS decomposition into traffic, conversion, and basket components
- ✓Store-level unit economics cards for capex prioritization
Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group
Furniture KPI impact
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.
Ward delivers daily insight cards covering Inventory carrying cost, Order-to-delivery cycle, Gross margin by channel, tailored for Finance decision-making. Each card includes what changed, why it matters, and what to do next.
Ward measures sell-through, margin, and revenue per floor square foot by SKU and showroom cluster, scores online-versus-showroom fit per piece, and flags whitespace where demand exists but no product is placed. Floor space is finite and costly, so the unit of analysis is productivity per slot, not raw units sold.
A regional chain runs the same floor plan across every showroom. Ward clusters showrooms by demographic and traffic and shows that urban locations turn contemporary upholstery far faster than the traditional case goods taking up a third of their floor, while suburban stores show the reverse. Ward recommends a cluster-specific floor: move slow case goods to web-only in urban stores and add two contemporary groupings. Floor productivity per square foot rises without adding space.
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 assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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