Fill Rate · Epicor · Head of Procurement

Fill Rate Monitoring + Epicor: Built for Head of Procurement

Most retailers find Fill Rate problems too late. Ward delivers automated insight cards. What changed, why, and what to do. While there's still time to act. Your Procurement team has the data. What they don't have is bandwidth to find what's buried in it.

Fill Rate Monitoring powered by Epicor

Fill Rate Monitoring is the process of ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.

When connected to Epicor, Ward reads sales orders, inventory, purchase orders and enriches them with contextual signals to generate fill rate insight cards. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.

How Ward delivers Fill Rate insight cards: Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.

Key capabilities

  • Estate-wide fill rate dashboard
  • Threshold-based alerting
  • Store-vs-estate benchmarking
  • Category-level drill-down
app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did Store 37 miss target last week?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled Store 37’s last 28 days against the chain baseline. Two root causes — both compounding.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster — staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83% — backroom replenishment lag at 2–4p
promo.liftBOGO 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.

8 parallel queries 3 sources cited confidence 0.92
Show me how to fix the staffing mismatch.
You · 9:43 AM
Labor Agent · drafting schedule diff
Querying labor_scheduling
Ask anything — Ward routes to the right agent. Cmd+K

Dashboards

Pinned views built from saved data-lake queries.

Revenue vs. forecast +4.2% WoW
Gross margin % −3.2pp
Fill rate — fresh 83%
Shrink — West region +0.8pp

Models

Browse, search, and manage data–lake model definitions for your tenant.

NameNamespaceVersion
retail_pos_transactionsretail1.0
retail_inventory_snapshotretail1.2
retail_labor_schedulingretail1.0
retail_promo_calendarretail1.1
retail_supplier_performanceretail1.0
sap_inventory_shrinkagesap1.0
ga4_daily_eventsmarketing1.0
meta_ads_ad_levelmarketing1.0

Sources

Connect external systems to the data lake.

NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Architecture

Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.

Your systems · read-only
SAP Retail
Snowflake
BigQuery
Shopify
Toast POS
Ward Gateway
TLS 1.3 · AES-256
Querying live · data stays put
Federated answers
SELECT * FROM sap.pos
JOIN snow.inventory
WHERE store_id = 37
→ insight cards
Ward Data Lake
→ baselined per store
TLS 1.3 in transit AES-256 at rest Read-only credentials SOC 2 II in progress VPC peering · PrivateLink

Pipelines

Move data from sources into models on a schedule.

NameSourceModelStatusSchedule
sync_sap_pos_transactionssap_pos_transactionspos_transactionsenabledhourly
sync_sap_labor_schedulingsap_labor_schedulinglabor_schedulingenableddaily
sync_sap_inventory_shrinkagesap_inventory_shrinkageinventory_shrinkageenableddaily
sync_retail_inventory_weeklyretail_inventory_weeklyinventory_weeklyenabledweekly
sync_retail_google_ads_dailyretail_google_ads_dailygoogle_ads_dailyenableddaily
sync_retail_ga4_website_dailyretail_ga4_website_dailyga4_website_dailyenableddaily

Streams

Real-time ingestion pipelines.

0events / min
0streams active
0% delivered
  • pos.txn store_037 — basket $42.18
  • inv.move dc_west → store_104
  • labor.clock store_022 shift_start
  • pos.txn store_211 — basket $19.04

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-shrinkagepermitModel::"shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"

Entities

Principals and resources referenced by Cedar policies.

Entity UIDTypeTenant
Tenant::"acme"Tenantacme
Model::"sap.pos_transactions"Modelacme
Model::"sap.inventory_shrinkage"Modelacme
Model::"sap.labor_scheduling"Modelacme
Model::"retail.toast_pos_daily"Modelacme
Model::"retail.ga4_website_daily"Modelacme

Providers

Manage LLM API keys and the model profiles that use them.

API Keys Model Profiles
NameProviderUsed byCreated
anthropic-defaultAnthropic3 profilesApr 22
openai-defaultOpenAI2 profilesApr 22
gemini-defaultGemini1 profileApr 22
ollama-onpremOllama2 profilesApr 22

LLM-agnostic. Bring your own key, route per task. No lock-in.

Settings

Manage your dashboard preferences and account.

Appearance
Theme • Light ° Dark

Light and dark themes are available. Your choice is remembered per browser.

Account
NameAdmin
Emailadmin@acme.io
Tenantacme-retail
Live product demo — Ward analyzing retail data in real time.

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

Sales orders
Inventory
Purchase orders
Customer accounts
Pricing tiers
Vendor performance

Impact metrics with Epicor

Seasonal Accuracy
Pre-buy timing sharpened
Weather and event signals calibrate seasonal positioning.
Project Basket Value
Cross-sell patterns found
Project purchasing sequences reveal attachment opportunities.
Vendor Fill Rate
Degradation caught early
Fill rate drops flagged before shelf impact materializes.
Inventory Carrying Cost
Slow-movers identified
Demand-aligned ordering frees capital tied in dead stock.

Data lake enrichment

Ward enriches Epicor data with: Sales orders, Weather & events, Contractor/Pro data, Competitor pricing, Vendor scorecards

Merchandising wants Ward. You sign the contract.

Pain points
  • ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
  • ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
  • ×Multi-year commits with auto-renew. No exit if the pilot stalls
  • ×Renewals come back 30% higher with no leverage and no benchmark
  • ×Security and DPA reviews start after the team has already committed
How Ward helps
  • MSA, DPA, SOC 2 II, and architecture review available before signature
  • Month-to-month contracts. No multi-year lock-in. No auto-renew traps
  • Transparent pricing tied to scope and store count, not seats or tokens
  • 14-day insight guarantee. If Ward doesn't deliver, month two is on us
  • Reference customers and a 850+ store live pilot operator you can interview

Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner

What a Ward insight card looks like

Ward · Fill Rate06:47 AM

Estate fill rate at 94.2%, up 1.2pp vs last week. Stores 22 and 37 dropped below 85% threshold. Fresh produce is the driver.

✓ Action recommendedEpicor data

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.

Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 II, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing tied to scope and store count, not seats or tokens.

First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust 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.

Ward
Insight
Dispatch
Feedback
Evaluate
Learn
01

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action. Not just a chart to interpret.

Real-time detection Root cause + recommendation
02

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel: mobile push, text, or email. Not every insight needs a ticket. When one does, it has an owner.

Tickets created automatically Dispatched to the right person
03

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal. Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached
04

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored
05

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

Cycle repeats, sharper each time
$1.8T
Projected global AI market by 2030
0
×
Customer acquisition lift for data‑driven orgs
0
+
Foundation models shipped since 2022
0
Guarantees any single model stays on top

See what fill rate problems Ward catches.

Root causes, not just alerts. See it on your data.

Get a demo

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.

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