Grocery shrinkage, read from SAP Retail
Find the leak before it drains you. Ward runs it on SAP data across your grocery fleet, without a warehouse project first.
Shrinkage Detection for Grocery & Supermarket, running on SAP
For Grocery & Supermarket retailers, that means continuous review 30,000+ SKUs across stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
Here is shrinkage detection in plain terms. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
How it runs. 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 from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
What you get
- 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.
Why this combination
is its own problem.
Shrinkage Detection behaves differently in grocery retail than it does anywhere else. The fleet shape, the SKU count, and the speed of the category all change what counts as a real data point and what is noise. Ward is tuned to the grocery version.
- 01 Estate-average shrinkage hides the four to seven stores driving 40% of the loss; chain reports never name them.
- 02 Self-checkout shrinkage is misattributed to staffed-lane theft because the cause-attribution model lumps all front-end voids together.
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 Ward has eyes on.
Ward reads straight from POS transactions, inventory positions, purchase orders from SAP Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. SAP 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.
Coverage is store by store, category by category. Ward watches fill rate, shrinkage %, fresh waste % across 30,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven stores are not.
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.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to SAP Retail. Ward ingests POS transactions, inventory positions, purchase orders and starts building baselines. First 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
Findings 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 SAP Retail
Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
Data Ward reads from SAP
Impact metrics with SAP
Data lake enrichment
Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates
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 from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.
Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
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
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Root causes, not just alerts. See it on your data.
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