Price Optimization for Home on Epicor, built for supply chain
Ward reads sales orders, inventory, purchase orders from Epicor, scans continuously pricing across 50,000+ home SKUs, and hands you the supply chain read each day.
The full picture: home pricing, Epicor data, Supply Chain decisions
Here is price optimization in plain terms. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
Home Improvement changes the scale of the problem: 50,000+ SKUs, every one of your stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
You find out about stockouts after customers do. Ward filters to what a VP Supply Chain can act on and drops the rest.
The mechanism. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
How the connection works. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
The short list
- Competitive price monitoring
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
- Category-level price sensitivity
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 Pricing matters for Home retail
A 2x4 has two completely different demand curves depending on who's buying it. Pro customers compare lumber prices daily; DIY customers barely notice per-board differences. Ward segments price elasticity by customer type so recommendations respect Pro sensitivity while capturing margin on DIY transactions.
What Ward has eyes on.
Ward monitors 50,000+ SKUs across your stores, at the store-category level rather than the chain roll-up. The metrics under watch include project basket value, seasonal accuracy, long-tail turn. A roll-up hides a single-store problem inside a healthy average, which is how project basket identification stays invisible for a quarter.
The connection to Epicor is read-only and runs on your schedule. Ward pulls from sales orders, inventory, and the rest of the feed, then cross-references it with external context your ERP does not carry: weather, local events, competitor pricing.
The pricing model runs every morning, not on a reporting calendar. It detects the pattern, traces the driver, and attaches what to do about it before the number reaches a review deck.
At the metric level. Ward tracks Pro vs DIY elasticity segmentation, commodity price benchmarking, project basket sensitivity (total project cost matters more than item prices), and seasonal demand multipliers on pricing power.
Why this combination
is its own problem.
Price Optimization produces a lot of output that is technically correct and operationally useless to supply chain. Ward filters on whether the finding changes a decision a VP Supply Chain can actually make.
- 01 Project basket pricing focuses on individual SKU margins; customers care about total project cost, so a uniform 5% increase on all components is more dangerous than a targeted 15% increase on the basket-anchor item.
- 02 Seasonal pricing power isn't modeled; the same SKU has materially different elasticity in peak project season versus the shoulder months.
Benchmarks. Home improvement Pro elasticity on commodity items typically -1.8 to -3.0; DIY on the same SKUs runs -0.3 to -0.8. Pro accounts represent 25-45% of revenue at 4-8x the basket size; protecting Pro pricing while capturing DIY margin is usually worth 200-400 bps of gross.
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 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: 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 you findings daily, each with the cause and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many findings arrive, it is how many get acted on.
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
You find out about stockouts after customers do.
- ×Demand forecasts are off by 15-25% and nobody catches it until the shelf is empty
- ×Supplier fill rate problems announce themselves at the receiving dock
- ×Safety stock levels get set once a year and left alone
- ×No early warning system for supply chain disruptions
- ×Replenishment exceptions require manual triage every morning
- ✓Stockout prediction cards arrive 24-72 hours before empty shelves
- ✓Supplier fill rate tracking with automatic escalation
- ✓Dynamic safety stock recommendations based on current demand signals
- ✓Weather, event, and macro-driven demand adjustments
- ✓Replenishment exceptions auto-prioritized by revenue impact
Stockouts cost retailers $1.14 trillion in missed sales globally each year. Source: IHL Group
Home KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. 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 continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
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.
You find out about stockouts after customers do. Ward solves this with automated insight cards: Stockout prediction cards arrive 24-72 hours before empty shelves. Supplier fill rate tracking with automatic escalation. Dynamic safety stock recommendations based on current demand signals.
Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for Supply Chain decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks Pro vs DIY elasticity segmentation, commodity price benchmarking, project basket sensitivity (total project cost matters more than item prices), and seasonal demand multipliers on pricing power.
Ward reveals that Pro account customers show steep price elasticity on framing lumber while DIY customers are nearly inelastic on the same SKU. Ward recommends maintaining aggressive Pro pricing through the loyalty tier while implementing modest increases on non-loyalty transactions. The increase is invisible to DIY weekend-project buyers but protects the Pro relationship and delivers meaningful annual margin improvement.
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 pricing problems Ward catches.
Root causes, not just alerts. See it on your data.
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