Epicor plus Ward: fill rate for a VP Supply Chain
Every empty shelf is a lost sale. Ward runs it off Epicor, scoped to what a VP Supply Chain owns.
Fill Rate Monitoring on Epicor, for supply chain
Here is fill rate monitoring in plain terms. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
You find out about stockouts after customers do. Ward filters to what a VP Supply Chain can act on and drops the rest.
How Ward hands back Fill Rate cards: Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
How the connection works. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
Capabilities
- Store-vs-estate benchmarking
- Category-level drill-down
- Estate-wide fill rate dashboard
- Threshold-based alerting
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
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 |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
What Ward has eyes on.
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.
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.
Why this combination
is its own problem.
Fill Rate Monitoring 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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to Epicor with read-only credentials and begins ingesting sales orders and inventory. 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 returns insight cards every morning, 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 insight cards 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
Frequently asked questions
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.
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.
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 fill rate problems Ward catches.
Root causes, not just alerts. See it on your data.
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Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.