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  7. Assortment
ERP · Epicor

Run assortment straight off Epicor

Stock what sells. Cut what doesn't. Ward runs it directly off your Epicor feed, read-only, with no changes to your ERP.

Request access Epicor connector reference
Read-only by default No copy of your data Ward AI drafts, you approve
01 · The connection

How Ward attaches to Epicor.

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

app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

AI Insights

Agents run against your baselines overnight. These are what they flagged without being asked.

3 flagged 8 queries run 3 sources swept 04:00, acme-retail
Act now labor_efficiency 0.94

Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak

Schema Scout · routed to Merchandising Agent Pin Ask Ward Investigate
Review inventory.fresh 0.89

Fresh fill 83%, backroom replenishment lag at 2–4p

Promo Agent · routed via Merchandising Pin Ask Ward Investigate
Watch promo.lift 0.81

BOGO crackers cannibalized Brand Y by 28%, net category +6%

Margin Agent · routed via Finance Pin Ask Ward Investigate
Recommended

Re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

Sources

Connect external systems. Ward AI maps the schema and drafts the cleaning rules.

Search sources…
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

Pipelines

Move data from sources into models on a schedule. Describe the sync; Ward AI builds it.

NameSourceModelStatusSchedule
sync_sap_pos_transactionssap_pos_transactionspos_transactionsenabledhourly
sync_sap_inventory_shrinkagesap_inventory_shrinkageinventory_shrinkageenableddaily
sync_sap_labor_schedulingsap_labor_schedulinglabor_schedulingenableddaily
sync_retail_inventory_weeklyretail_inventory_weeklyinventory_weeklyenabledweekly
sync_retail_google_ads_dailyretail_google_ads_dailygoogle_ads_dailyenableddaily
sync_retail_meta_ads_dailyretail_meta_ads_dailymeta_ads_dailyenableddaily
Sources, then what Ward AI drafted from them, then what it computed. Click between the panels.
What Ward reads
Sales ordersInventoryPurchase ordersCustomer accountsPricing tiersVendor performance

Ward integrates with Epicor for home improvement, furniture, and building supply retailers. Inventory, purchasing, production, and sales data power insight cards.

Scope

Credentials are read-only and locked to the objects you name. Ward cannot reach a table you did not grant it, and write access is opt-in per object rather than implied by the connection.

Residency

Ward queries Epicor in place and keeps no second copy. Your region, your retention policy, and no staging warehouse in the middle.

Drift

Every connector carries a schema contract. A source that changes shape fails the contract and stops the pipeline, rather than quietly writing wrong rows until someone notices the numbers moved.

02 · What it computes

Stock what sells. Cut what doesn't.

Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.

Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

  • Store cluster segmentation
  • SKU rationalization recommendations
  • Whitespace opportunity detection
  • Planogram optimization inputs

Definitions resolve through the semantic layer, so a number here reconciles with the dashboard your team already publishes instead of disagreeing with it quietly. How the layer works →

Sample output

Cluster B stores (urban, high-traffic) underperforming on premium snacks vs Cluster A by 34%. Assortment gap: 12 SKUs missing.

20% of SKUs typically drive 80% of revenue, but most retailers stock the long tail anyway. — Bain & Company

03 · Scope and audit

What the agent may read, and what it did last Tuesday.

Agents on your own data fail the security review, not the demo. The model is never the problem. The problem is that nobody can say what the agent is allowed to reach, what it already did, or what happens when it is wrong.

01

The agent sees the Epicor objects you declared on the source and nothing else. Scope is enforced at query time, not in the client.

02

Read-only until you grant a write action explicitly, per object. A grant leaves a record naming the agent, the caller and the rows it touched.

03

One audit log covers every person and every agent in the tenant, so a reviewer answers one question against one system rather than correlating two.

Governance and policy →
How a scope gets set

Describe the job in plain English. Ward AI proposes the agent and the exact permissions it would need against your Epicor objects, and the proposal sits there until a person approves it. Nothing runs on a draft.

Every finding it returns carries the SQL that produced it. If the query is wrong you can see that it is wrong, which is the only way a data team ever trusts one of these.

04 · Where it runs

The same pipeline, in three operating contexts.

What assortment planning off Epicor looks like once it is live. Retail is where Ward is deployed, not what Ward is.

Grocery

30,000+ SKUs · stores

A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores that match the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram leaves revenue on the table in urban stores while it ties up slow-moving inventory in rural ones.

The grocery build →

Convenience

3,000+ SKUs · locations

A standardized planogram runs across all 500 locations. Ward identifies distinct store clusters, highway/travel, urban commuter, residential, university-adjacent, each overindexing on different categories. Ward recommends reallocating shelf space per cluster to match actual demand. Pilot stores show meaningful revenue uplift from better product-location matching with zero cost increase: same SKU count, just the right ones in the right stores.

The convenience build →

Fashion

15,000+ SKUs · locations

A denim buyer has 200 styles to allocate across 90 stores. Ward reveals that urban flagships convert best with wide assortment at shallow depth, while suburban stores need fewer core styles with full size runs. The current uniform allocation starves variety in urban stores and creates size gaps in suburban ones. A cluster-specific matrix reduces markdown risk while lifting full-price sell-through.

The fashion build →
05 · Who consumes it

You stand it up. They read the output.

Your platform team owns the connection, the definitions and the policy. These are the teams that consume what comes out of it.

VP Merchandising

Merchandising

Cluster-aware SKU rationalization with whitespace gaps flagged automatically.

What they see →

Head of E-Com

E-Commerce

Online vs offline assortment fit, scored weekly.

What they see →

Director Store Ops

Store Operations

Per-cluster planograms tied to local sell-through.

What they see →
06 · What changes

What connecting Epicor actually moves.

MeasureEffectWhy
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.
Questions

What a technical evaluation asks first.

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

Ward reads Sales orders, Inventory, Purchase orders, Customer accounts, Pricing tiers and Vendor performance. Credentials are read-only and scoped to the objects you name, so Ward cannot reach a table you did not grant it.

No. Ward queries Epicor in place and holds no second copy of your data. Nothing is exported to run Assortment Planning, and your residency and retention policy are unchanged by connecting Ward.

Every connector carries a schema contract. When a source changes shape the contract fails loudly and the pipeline stops, rather than writing wrong rows into your warehouse for a week before anyone notices the numbers moved.

Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

Yes. Every figure Ward reports carries the query that produced it, resolved through the semantic layer, so it reconciles with the dashboard your team already publishes rather than disagreeing with it quietly.

Only what you declared. An agent is scoped to named tables and cannot reach past them, it is read-only unless you grant a write action explicitly, and every query, tool call and result is logged against the agent identity that made it.

Typically VP / Director of Merchandising, Head of E-Commerce / Digital and Director of Store Operations. They read the output; your platform team owns the connection, the definitions and the policy that produced it.

A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores that match the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram leaves revenue on the table in urban stores while it ties up slow-moving inventory in rural ones.

Explore

Assortment Planning on another system.

SAP Oracle Shopify NetSuite Snowflake BigQuery Blue Yonder RELEX Looker Power BI Tableau NCR BigCommerce Lightspeed

What else Ward computes off Epicor.

Stockout Prediction Shrinkage Detection Price Optimization Demand Forecasting Promo Effectiveness Customer Behavior Fill Rate Monitoring
Epicor connector → Assortment overview → All 540+ connectors → Every pairing →

Point Ward at your Epicor and see what comes back.

Deploying is free. You pay 5% of the model compute it uses, and nothing else. No call required to find out whether this works on your data.

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