Demand · Epicor · Head of LP

Epicor plus Ward: demand for a Head of LP

Ward reads straight from sales orders, inventory, purchase orders from Epicor, runs demand on it, and delivers the loss prevention read daily.

Demand Forecasting on Epicor, for loss prevention

Demand Forecasting. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.

Shrinkage costs you more than you think. Ward finds out where. Ward writes the finding at the altitude a Head of LP works at.

Under the hood. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.

The connection itself. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.

Key capabilities

  • Weather-driven adjustment
  • Event and holiday modeling
  • Automatic reorder point recalculation
  • Store-SKU-day level precision
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"
Demand on Epicor data, live product demo.

What Ward has eyes on.

Ward pulls 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 cards. Your ERP is untouched.

See demand before it arrives. 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.

A Head of LP does not need the demand 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 the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to Epicor. Ward ingests sales orders, inventory, purchase orders and starts building baselines. First 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 daily cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: steady state

    Ward returns insight cards daily, each with the driver 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

Shrinkage costs you more than you think. Ward finds out where.

Pain points
  • ×Shrinkage lands as a year-end surprise
  • ×Cannot distinguish theft from spoilage from admin error
  • ×High-shrinkage stores only identified during audits
  • ×No correlation between operational changes and loss patterns
  • ×Exception-based reporting misses slow-bleed patterns
How Ward helps
  • Store-level shrinkage tracking with cause attribution
  • Anomaly detection flags stores deviating from estate average
  • Receiving dock discrepancy patterns identified automatically
  • Correlation analysis links operational changes to loss shifts
  • Trend analysis catches slow-bleed patterns audits miss

US retail shrinkage hit $112.1 billion in 2022. Up 19.4% year over year. Source: National Retail Federation

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.

Shrinkage costs you more than you think. Ward finds out where. Ward solves this with automated insight cards: Store-level shrinkage tracking with cause attribution. Anomaly detection flags stores deviating from estate average. Receiving dock discrepancy patterns identified automatically.

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 demand 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