Assortment · Blue Yonder · Head of E-Com

Head of E-Com: assortment straight off Blue Yonder

Your online and offline data live in different worlds. Ward spots assortment movement in your Blue Yonder data and accounts for what caused it.

How a Head of E-Com runs assortment off Blue Yonder

Here is assortment planning in plain terms. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.

Your online and offline data live in different worlds. Ward filters to what a Head of E-Com can act on and drops the rest.

What Ward does with that: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

How the connection works. Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.

The short list

  • Store cluster segmentation
  • SKU rationalization recommendations
  • Whitespace opportunity detection
  • Planogram optimization inputs
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"
Assortment on Blue Yonder data, live product demo.

What Ward has eyes on.

Stock what sells. Cut what doesn't. 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 straight from demand forecasts, replenishment recommendations, allocation plans from Blue Yonder on a read-only connection. Nothing is written back, and your Supply Chain configuration does not change. Blue Yonder stays the system of record.

Why this combination
is its own problem.

Assortment Planning needs demand forecasts and replenishment recommendations at minimum. Blue Yonder carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to Blue Yonder. Ward pulls from demand forecasts, replenishment recommendations, allocation plans and starts building baselines. First findings 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: operating rhythm

    Cards arrive every morning and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.

How Ward connects to Blue Yonder

Ward layers on top of Blue Yonder demand planning and replenishment. Ward watches what Blue Yonder recommends and flags when actual diverges from plan.

Setup: Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy.

Data Ward reads from Blue Yonder

Demand forecasts
Replenishment recommendations
Allocation plans
Exception alerts

Impact metrics with Blue Yonder

Forecast Accuracy
Plan vs actual tracked
Forecasts scored against actuals with external signal overlay.
Replenishment Exceptions
Revenue-ranked triage
Exceptions auto-prioritized so high-impact ones work first.
Fill Rate
Allocation drift caught
Plan-to-demand divergence flagged before stockouts form.
Plan vs Actual Variance
Feedback loop tightened
Continuous plan-to-outcome comparison for planning teams.

Data lake enrichment

Ward enriches Blue Yonder data with: Demand forecasts, POS actuals, Weather & events, Supplier fill rates, Competitor data

Your online and offline data live in different worlds.

Pain points
  • ×Nobody can see one inventory position across every channel
  • ×Online promo performance is measured separately from in-store
  • ×Customer behavior data is siloed by channel
  • ×BOPIS/BORIS operational complexity is growing unchecked
  • ×Digital marketing attribution stops at the click
How Ward helps
  • Unified insight cards across online and in-store channels
  • Cross-channel promo effectiveness with true attribution
  • Customer journey tracking across digital and physical touchpoints
  • BOPIS fulfillment performance monitoring with exception cards
  • Full-funnel marketing attribution to in-store conversion

Retailers with unified omnichannel data see 30% higher lifetime value per customer. Source: Harvard Business Review

Frequently asked questions

Ward reads Blue Yonder outputs via API or flat file export. Compares forecasts against actuals to measure accuracy. Data points include: Demand forecasts, Replenishment recommendations, Allocation plans, Exception alerts.

Your online and offline data live in different worlds. Ward solves this with automated insight cards: Unified insight cards across online and in-store channels. Cross-channel promo effectiveness with true attribution. Customer journey tracking across digital and physical touchpoints.

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 assortment 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