Head of LP: grocery shrinkage, read from RELEX Solutions
Find the leak before it drains you. Ward runs it on RELEX data throughout your grocery fleet, scoped to loss prevention.
How a grocery Head of LP runs shrinkage on RELEX Solutions
For Grocery & Supermarket retailers, that means continuous review 30,000+ SKUs over stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
Shrinkage costs you more than you think. Ward finds out where. Ward writes the finding at the altitude a Head of LP works at.
Shrinkage Detection is a card type Ward runs continuously. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
What Ward does with that: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
How the connection works. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
Capabilities
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
- Cause-level shrinkage attribution
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO 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.
labor_scheduling…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
relex_replenishment_plan | import | 1h ago |
blue_yonder_forecast_daily | import | 1h ago |
retail_fresh_waste_daily | import | 1h ago |
retail_promo_calendar | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fresh-team-west | permit | Model::"fresh_waste_daily" |
Why Shrinkage matters for Grocery retail
Grocery shrinkage splits into theft, spoilage, and admin error, but most retailers can't distinguish the cause until physical inventory. Ward separates these at the store-department level by cross-referencing receiving logs, POS velocity, waste scans, and inventory snapshots to produce cause-attributed shrinkage cards.
What Ward has eyes on.
Every one of your stores gets its own baseline. Ward monitors fill rate, shrinkage %, fresh waste % against it and raises only the deviations that hold up. The two that show up most in grocery retail are fresh waste & spoilage and on-shelf availability gaps, and both are baseline problems before they are P&L problems.
Ward pulls from demand forecasts, space plans, workforce forecasts from RELEX Solutions on a read-only connection. Nothing is written back, and your Supply Chain configuration does not change. RELEX stays the system of record.
Find the leak before it drains you. 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.
At the metric level. Ward decomposes shrinkage into theft, spoilage, vendor fraud, and admin error, then tracks inventory-to-sales ratios, receiving accuracy, and waste scan compliance by department. Rising shrinkage in a single department usually signals a process failure, not organized crime.
Why this combination
is its own problem.
RELEX Solutions is the system of record for most grocery operators of your size, which means the signals Ward needs are already there. The gap is not data collection. It is that nobody has time to read demand forecasts and space plans every morning throughout every store.
- 01 Self-checkout shrinkage is misattributed to staffed-lane theft because the cause-attribution model lumps all front-end voids together.
- 02 Spoilage in DSD-stocked categories (bread, dairy, beer) is invisible because the vendor owns the markdown decision.
Benchmarks. US grocery shrink averages 2.0-3.0% of sales, with fresh departments running 4-7% and center store closer to 1-2%. A multi-vertical operator is usually surprised to learn shrink concentrates in three departments per store, not chain-wide, which means three targeted fixes recover more than blanket LP spend.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to RELEX Solutions. Ward reads demand forecasts, space plans, workforce forecasts and starts building baselines. First findings land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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.
-
03
Weeks 4 to 12: steady state
Ward sends cards daily, each with the driver and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.
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
Impact metrics with RELEX
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.
- ×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
- ✓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
Grocery KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. For Grocery retail specifically, Ward monitors 30,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Fill rate, Shrinkage %, Fresh waste %, Promo lift, Basket size at the store-category level. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
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.
Yes. Ward reads RELEX data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
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.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
Ward decomposes shrinkage into theft, spoilage, vendor fraud, and admin error, then tracks inventory-to-sales ratios, receiving accuracy, and waste scan compliance by department. Rising shrinkage in a single department usually signals a process failure, not organized crime.
One store flags elevated shrinkage well above the estate average for two consecutive periods. Traditional LP assumes shoplifting. Ward traces the majority of variance to receiving dock discrepancies in frozen foods, vendor deliveries consistently short against POs. One process fix, mandatory blind receiving, brings the store back in line within six weeks.
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.
Related solutions
See what Grocery shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
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