Home demand without leaving SAP
Your SAP data already carries the demand data point. Ward reads it, accounts for what caused it, and attaches a recommended move.
How Ward turns SAP data into home demand
Demand Forecasting. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
In a Home Improvement footprint the job is 50,000+ SKUs across 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 reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
The short list
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
- Store-SKU-day level precision
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.
Why this combination
is its own problem.
Demand Forecasting behaves differently in home retail than it does anywhere else. The estate shape, the SKU count, and the speed of the category all change what counts as a real signal and what is noise. Ward is tuned to the home version.
- 01 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.
- 02 Housing market indicators (permits, sales) are leading signals for project demand but rarely make it into pharmacy or operational forecasting workflows.
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 Ward has eyes on.
Coverage is store by store, category by category. Ward watches project basket value, seasonal accuracy, long-tail turn throughout 50,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven stores are not.
Ward reads straight from POS transactions, inventory positions, purchase orders from SAP Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. SAP stays the system of record.
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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads from SAP Retail with read-only credentials and begins ingesting POS transactions and inventory positions. No config changes on your side. First daily cards arrive inside the first 48 hours.
-
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: steady state
Ward hands back findings every morning, each with root cause and a recommended action. 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.
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
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 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 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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