Ward runs assortment on your SAP furniture data
Furniture retailers on SAP run assortment through Ward. 10,000+ SKUs across your locations, keeps a running read on around the clock.
Assortment Planning for Furniture Manufacturing & Retail, running on SAP
A Furniture Manufacturing & Retail operator is monitoring 10,000+ SKUs across 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.
Assortment Planning is a insight card type Ward runs continuously. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
What Ward does with that: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
How the connection works. Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
What it does
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
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.
What Ward has eyes on.
The connection to SAP Retail is read-only and runs on your schedule. Ward pulls from POS transactions, inventory positions, and the rest of the feed, then cross-references it with external context your ERP does not carry: weather, local events, competitor pricing.
The assortment model runs daily, not on a reporting calendar. It picks up the pattern, attributes the driver, and attaches what to do about it before the number reaches a review deck.
Ward tracks 10,000+ SKUs over 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.
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.
Why this combination
is its own problem.
SAP Retail is the system of record for most furniture operators of your size, which means the signals Ward needs are already there. The gap is not data collection. It is that nobody has time to read POS transactions and inventory positions every morning throughout every store.
- 01 Online demand is treated as separate from the floor decision, when web sell-through is the best signal for what deserves a physical slot.
- 02 Hero pieces are kept on the floor out of habit long after their velocity has faded, crowding out the groupings that would turn faster.
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 the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to SAP Retail. Ward reads straight from POS transactions, inventory positions, purchase orders and starts building baselines. First cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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 findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Insight cards arrive each day 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 SAP Retail
Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
Data Ward reads from SAP
Impact metrics with SAP
Data lake enrichment
Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates
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.
Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.
Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Furniture-specific insight cards. No custom development required.
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
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Root causes, not just alerts. See it on your data.
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