Shrinkage Detection for Grocery on RELEX, built for technology
The business wants AI. You sign off on the architecture. Ward picks up grocery shrinkage movement in your RELEX data early, with what caused it and the next step attached.
How a grocery Head of IT runs shrinkage on RELEX Solutions
The business wants AI. You sign off on the architecture. Ward surfaces the inputs that change a technology decision.
Shrinkage Detection is a card type Ward runs continuously. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
Grocery & Supermarket changes the scale of the problem: 30,000+ SKUs, every one of your stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
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.
Getting the data in. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.
Capabilities
- Receiving dock anomaly detection
- Pattern recognition across time
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
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.
The connection to RELEX Solutions is read-only and runs on your schedule. Ward ingests demand forecasts, space plans, and the rest of the feed, then joins it with external context your Supply Chain does not carry: weather, local events, competitor pricing.
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.
Every one of your stores gets its own baseline. Ward tracks 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.
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.
Most shrinkage projects stall at data access. This one does not, because RELEX already exposes demand forecasts, space plans, workforce forecasts through an API Ward reads directly.
- 01 Self-checkout shrinkage is misattributed to staffed-lane theft because the cause-attribution model lumps all front-end voids together.
- 02 Estate-average shrinkage hides the four to seven stores driving 40% of the loss; chain reports never name them.
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 ingests demand forecasts, space plans, workforce forecasts 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.
-
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 findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Insight cards arrive on a daily cycle 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
Impact metrics with RELEX
Data lake enrichment
Ward enriches RELEX data with: RELEX forecasts, POS actuals, Weather & events, Demographic data, Supplier performance
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
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.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Technology 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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