Stockout · Epicor · Head of IT

Head of IT: stockout straight off Epicor

Know before the shelf empties. Ward runs it off Epicor, scoped to what a Head of IT owns.

Stockout Prediction on Epicor, for technology

What stockout prediction does: Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.

The business wants AI. You sign off on the architecture. Ward writes the finding at the altitude a Head of IT works at.

What Ward does with that: Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.

How the connection works. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.

Capabilities

  • Reduce lost sales by catching gaps early
  • Automated replenishment recommendations
  • Supplier-aware lead time modeling
  • Priority ranking by revenue impact
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

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

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

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::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"
Stockout on Epicor data, live product demo.

Why this combination
is its own problem.

A Head of IT does not need the stockout model explained. They need to know which stores moved, why, and what to do by end of day. Ward writes the finding at that altitude.

What Ward has eyes on.

Know before the shelf empties. 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 reads sales orders, inventory, purchase orders from Epicor on a read-only connection. Nothing is written back, and your ERP configuration does not change. Epicor stays the system of record.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to Epicor. Ward reads straight from 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.

  2. 02

    Weeks 2 to 3: baselines

    Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the insight cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: steady state

    Ward hands you insight cards every morning, each with root cause and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many 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

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

The business wants AI. You sign off on the architecture.

Pain points
  • ×Business sponsor already chose the vendor. You inherit the security review
  • ×Every AI vendor wants write access and a copy of the production data
  • ×Model lock-in means rewriting the stack when GPT or Claude moves again
  • ×Audit trail is an afterthought. Compliance has nothing to pull on
  • ×Data lake project keeps getting bumped for the next thing the business wants
How Ward helps
  • Federated query: data stays in your warehouse. No copies, no shadow lake
  • Read-only credentials. Cedar policies enforce least-privilege per agent
  • LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
  • Every query, every model, every source logged. SIEM-ready audit output
  • VPC peering, PrivateLink, SOC 2 II. Your security review is short

74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner

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.

The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.

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.

See what stockout problems Ward catches.

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

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info