Blue Yonder · Head of LP

Blue Yonder plus Ward, briefed to loss prevention

Blue Yonder data, read for loss prevention. Ward accounts for what moved and what to do, on a daily cycle.

Ward and Blue Yonder for Head of Loss Prevention

Shrinkage costs you more than you think. Ward finds out where. Ward brings up the indicators that change a loss prevention decision.

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

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"
on Blue Yonder data, live product demo.

What Ward delivers

  • 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

Data Ward reads from Blue Yonder

Demand forecasts
Replenishment recommendations
Allocation plans
Exception alerts

Why this combination
is its own problem.

A Head of LP rarely logs into Blue Yonder. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.

What Ward has eyes on.

The connection to Blue Yonder is read-only and runs on your schedule. Ward ingests demand forecasts, replenishment recommendations, and the rest of the feed, then stitches together it with external context your Supply Chain does not carry: weather, local events, competitor pricing.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to Blue Yonder. Ward ingests demand forecasts, replenishment recommendations, allocation plans 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: calibration

    Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.

  3. 03

    Weeks 4 to 12: steady state

    Ward delivers cards every morning, each with the driver and the next step. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.

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

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 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.

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 your stores are hiding.

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