Fill Rate Monitoring: Home retail, Epicor data, Merchandising decisions
Your category managers are drowning in spreadsheets. Ward spots home fill rate movement in your Epicor data in time to act, with the cause and the next step attached.
The full picture: home fill rate, Epicor data, Merchandising decisions
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.
Your category managers are drowning in spreadsheets. Ward sends daily cards scoped to merchandising decision-making.
Fill Rate Monitoring is a card type Ward runs continuously. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
Under the hood. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Getting the data in. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
What it does
- Estate-wide fill rate dashboard
- Threshold-based alerting
- Store-vs-estate benchmarking
- Category-level drill-down
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 Fill Rate matters for Home retail
A store can report 96% fill rate while missing the one fastener that completes every deck project basket. Ward monitors fill rate through a project-basket lens, flagging when project-critical items drop below threshold even if aggregate availability looks healthy.
What Ward has eyes on.
Every empty shelf is a lost sale. 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.
Ward scans continuously 50,000+ SKUs over 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 joins it with external context your ERP does not carry: weather, local events, competitor pricing.
At the metric level. Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
Why this combination
is its own problem.
Fill Rate Monitoring 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 Pro customer order fill rate is benchmarked against estate-wide DIY fill, hiding the much steeper Pro defection curve when a flagged SKU goes out of stock.
- 02 Fill rate measured at the SKU level masks project-basket completion; a deck-project basket that needs 12 SKUs has a much lower complete-basket availability than any individual SKU's availability suggests.
Benchmarks. Home improvement project-basket completion: healthy chains run 88-94% on the top-100 project baskets; below 80% on a top basket cuts category revenue 3-7% over the affected weeks. Pro customer fill rate matters disproportionately, Pros typically defect after 2-3 stockouts on flagged SKUs.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of Epicor with read-only credentials and begins ingesting sales orders and inventory. No config changes on your side. First insight cards arrive in two days.
-
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: operating rhythm
Insight cards arrive on a daily cycle 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 category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
Home KPI impact
Frequently asked questions
Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. 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 tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
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.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for Merchandising decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
Estate-wide fill rate looks healthy, but Ward's project-basket analysis shows the "deck build" basket has far lower complete-basket availability because a single specialty fastener is out of stock. A standard fill rate report would bury this item among 50,000 others. Ward surfaces it through basket completion analysis, and the supply chain team expedites the item to restore project-level availability within days.
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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