Furniture shrinkage from Epicor, briefed to finance
Find the leak before it drains you. Ward runs it on Epicor data over your furniture estate, scoped to finance.
Shrinkage Detection for Furniture on Epicor, scoped to finance
Applied to Furniture Manufacturing & Retail, the surface area is 10,000+ SKUs throughout 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 filters to what a CFO 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.
Under the hood. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Setup: Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
Capabilities
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
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 ingests sales orders, inventory, purchase orders from Epicor on a read-only connection. Nothing is written back, and your ERP configuration does not change. Epicor 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 monitors 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 finance. Ward filters on whether the finding changes a decision a CFO can actually make.
- 01 Damage is booked as one shrink line with no cause split, so a carrier handling problem looks identical to a manufacturing defect and neither gets fixed.
- 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 Epicor. Ward reads straight from sales orders, inventory, purchase orders 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: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Findings 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 Epicor
Ward integrates with Epicor for home improvement, furniture, and building supply retailers. Inventory, purchasing, production, and sales data power insight cards.
Setup: Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
Data Ward reads from Epicor
Impact metrics with Epicor
Data lake enrichment
Ward enriches Epicor data with: Sales orders, Weather & events, Contractor/Pro data, Competitor pricing, Vendor scorecards
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 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.
Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse. Data points include: Sales orders, Inventory, Purchase orders, Customer accounts, Pricing tiers, Vendor performance.
Yes. Ward reads Epicor data and combines it with contextual signals (weather, events, demographics) to generate Furniture-specific insight cards. No custom development required.
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 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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