Demand · RELEX · Head of LP

RELEX plus Ward: demand for a Head of LP

Shrinkage costs you more than you think. Ward finds out where. Ward detects demand movement in your RELEX data and explains what caused it.

How a Head of LP runs demand off RELEX Solutions

Shrinkage costs you more than you think. Ward finds out where. Ward pulls forward the readings that change a loss prevention decision.

Here is demand forecasting in plain terms. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.

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

Getting the data in. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.

What it does

  • 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 RELEX data, live product demo.

What Ward has eyes on.

The connection to RELEX Solutions is read-only and runs on your schedule. Ward reads straight from demand forecasts, space plans, and the rest of the feed, then cross-references it with external context your Supply Chain does not carry: weather, local events, competitor pricing.

The demand model runs daily, not on a reporting calendar. It catches the pattern, traces the cause, and attaches what to do about it before the number reaches a review deck.

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: read-only connection

    Ward connects to RELEX Solutions with read-only credentials and begins ingesting demand forecasts and space plans. No config changes on your side. First insight cards arrive in two days.

  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 cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: operating rhythm

    Cards arrive daily 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 RELEX Solutions

Ward integrates with RELEX for unified retail planning. Ward monitors RELEX forecasts against actuals and surfaces planning exceptions as insight cards.

Setup: Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.

Data Ward reads from RELEX

Demand forecasts
Space plans
Workforce forecasts
Promotion plans
Replenishment orders

Impact metrics with RELEX

Forecast Accuracy
Drift flagged per store
Accuracy decay caught before it compounds across locations.
Space Productivity
Planogram gaps exposed
Space plans checked against actual sell-through per fixture.
Replenishment Efficiency
Manual triage reduced
Exceptions ranked by revenue impact and urgency.
Fresh Waste
Perishable ordering tightened
Hyperlocal weather and events sharpen fresh item forecasts.

Data lake enrichment

Ward enriches RELEX data with: RELEX forecasts, POS actuals, Weather & events, Demographic data, Supplier performance

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 RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound. Data points include: Demand forecasts, Space plans, Workforce forecasts, Promotion plans, Replenishment orders.

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