Ward runs demand on your Epicor home data
Your Epicor data already carries the demand data point. Ward ingests it, traces root cause, and attaches a recommended action.
The home demand stack on Epicor
Demand Forecasting. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
For Home Improvement retailers, that means monitoring 50,000+ SKUs throughout stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
What Ward does with that: Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
The connection itself. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
What it does
- Store-SKU-day level precision
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled the spring pre-build against the last three seasons and the weather signal for the 22 Southeast stores.
| Signal | Finding |
|---|---|
seasonal_prebuild | Mulch on-hand hit 61% of plan the week temps broke 70°F |
dc_push | DC push started 9 days after the first-warm-week trigger, LY it was 2 |
attach.project | Soil and edging attach fell 18% at stores that gapped on mulch |
Recommend: tie the push trigger to the 10-day forecast instead of the calendar week, pre-position two truckloads at the 8 stores that gapped, and re-set the attach endcap.
seasonal_prebuild…
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_pos_transactions | import | 2m ago |
epicor_special_orders | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
retail_seasonal_prebuild | import | 1h ago |
retail_sku_velocity | import | 1h ago |
retail_pro_account_sales | import | 1h ago |
retail_weather_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
pro-team-read-accounts | permit | Model::"pro_account_sales" |
vendor-blocked | forbid | Model::"labor_*" |
supply-read-orders | permit | Model::"special_orders" |
Why Demand matters for Home retail
Home improvement demand is the most weather-dependent in retail, a warm spring can shift seasonal demand forward by weeks across a large chain. Ward integrates 10-day weather forecasts, historical correlations, and housing market indicators to predict demand at a granularity that static seasonal plans can't match.
What Ward has eyes on.
Every one of your stores gets its own baseline. Ward watches project basket value, seasonal accuracy, long-tail turn against it and surfaces only the deviations that hold up. The two that show up most in home retail are project basket identification and seasonal pre-positioning, and both are baseline problems before they are P&L problems.
Ward reads straight from Epicor rather than replacing it. Sales orders, inventory, purchase orders come across on a read-only connection, get enriched with contextual data, and come back as daily cards. Your ERP is untouched.
See demand before it arrives. 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 integrates hyperlocal weather data, housing market indicators (home sales, building permits), seasonal project activation curves, and Pro customer pipeline data. Forecast accuracy is measured by department and weather-sensitivity tier.
Why this combination
is its own problem.
Epicor is the system of record for most home operators of your size, which means the indicators Ward needs are already there. The gap is not data collection. It is that nobody has time to read sales orders and inventory every morning over every store.
- 01 Weather forecasts get used as 7-day signals when 14-21 day project-planning windows are what actually drive purchasing decisions.
- 02 Seasonal calendars are set chain-wide but the actual project-season start varies 4-8 weeks across regions; Northeast deck season is meaningfully later than Southeast.
Benchmarks. Home improvement seasonal forecast accuracy: 22-35% MAPE on weather-sensitive categories (paint, lawn/garden, outdoor power) is healthy. A 5-point accuracy gain typically reduces seasonal markdown by 20-30% and recovers 1-3% category revenue from better stock positioning.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Epicor. Ward ingests sales orders, inventory, purchase orders and starts building baselines. First daily 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 daily cards are directionally right and the thresholds are still moving.
-
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 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
Home KPI impact
Frequently asked questions
Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level. For Home retail specifically, Ward monitors 50,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Project basket value, Seasonal accuracy, Long-tail turn, Pro customer share, Attachment rate at the store-category level. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
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 Home-specific insight cards. No custom development required.
Ward integrates hyperlocal weather data, housing market indicators (home sales, building permits), seasonal project activation curves, and Pro customer pipeline data. Forecast accuracy is measured by department and weather-sensitivity tier.
February temperatures run well above normal across the Midwest. Ward detects early-spring project categories activating weeks ahead of plan while winter products decelerate faster than expected. Ward issues demand adjustment cards for Midwest stores: accelerate spring resets, cut winter closeout buys, and increase DC allocation of seasonal products. Stores that act capture early-season revenue that would have stocked out under the original plan.
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 Home demand problems Ward catches.
Root causes, not just alerts. See it on your data.
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