Convenience shrinkage, briefed to loss prevention
Convenience operators find shrinkage problems once the quarter has closed. A Head of LP finds them on Ward the morning they start.
What a convenience Head of LP sees in shrinkage
Convenience & C-Store changes the scale of the problem: 3,000+ SKUs, every one of your locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
Shrinkage costs you more than you think. Ward finds out where. Ward sends cards scoped to loss prevention decision-making.
Shrinkage Detection. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
How Ward hands back Shrinkage findings: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
What you get
- 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 the 6–9a daypart for the 14 Route 9 sites against the chain baseline. Two causes, one of them scheduling.
| Signal | Finding |
|---|---|
daypart_sales | 6–9a revenue −11% vs. chain, coffee units −18% |
labor_scheduling | Second associate clocks in at 7:30a, peak starts 6:40a at 9 of 14 sites |
foodservice.waste | Breakfast sandwich waste 14%, hold times past 4 hours at 6 sites |
Recommend: move the second open to 6:15a at those nine sites, cut the breakfast batch by one tray, and re-check attach in two weeks.
daypart_sales…
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 |
|---|---|---|
ncr_pos_transactions | import | 2m ago |
pdi_fuel_transactions | import | 2m ago |
verifone_forecourt_events | import | 14m ago |
ncr_planogram_audit | import | 1h ago |
retail_daypart_sales | import | 1h ago |
retail_foodservice_waste | import | 1h ago |
retail_labor_scheduling | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
ops-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fuel-team-forecourt | permit | Model::"fuel_transactions" |
Why Shrinkage matters for Convenience retail
C-store shrinkage is dominated by slow-bleed employee theft and scan avoidance, small per-transaction losses that compound across thousands of daily transactions. Ward monitors voids, no-sales, and scan-rate deviations, then correlates them with shift patterns and employee schedules to surface risk that audit cycles miss.
What Ward has eyes on.
The shrinkage model runs daily, not on a reporting calendar. It catches the pattern, accounts for the driver, and attaches what to do about it before the number reaches a review deck.
Ward scans continuously 3,000+ SKUs throughout your locations, at the store-category level rather than the chain roll-up. The metrics under watch include transactions/hour, attach rate, basket size. A roll-up hides a single-store problem inside a healthy average, which is how daypart demand variation stays invisible for a quarter.
At the metric level. Ward focuses on transaction anomaly rates (voids, no-sales, manual overrides), shift-correlated patterns, high-theft category velocity gaps, and receiving accuracy on high-value items, benchmarking each store against its own history and the estate average.
Why this combination
is its own problem.
The loss prevention problem in convenience retail is not missing data. It is that transactions/hour, attach rate, basket size live in different systems on different refresh schedules, and reconciling them is a person-week. Ward does the reconciliation and returns the two-line version.
- 01 High-margin impulse items (candy, gum) get under-counted because shrink rates are reported as percentages of large category totals.
- 02 Threshold-based void alerts catch high-dollar individual events but miss the coordinated small-dollar pattern that adds up to the real loss.
Benchmarks. C-store shrink runs 0.8-1.8% of inside-store sales, with tobacco and high-margin impulse categories driving disproportionate dollar loss. A small per-transaction void pattern (under $5) on tobacco can cost $15K-40K per store per year before it triggers traditional threshold alerts.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads your existing systems on a read-only connection. Nothing is written back. First cards arrive inside the first 48 hours.
-
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: 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.
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
Convenience KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. For Convenience retail specifically, Ward monitors 3,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Transactions/hour, Attach rate, Basket size, Planogram compliance, Daypart mix 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.
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 Transactions/hour, Attach rate, Basket size, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
Ward focuses on transaction anomaly rates (voids, no-sales, manual overrides), shift-correlated patterns, high-theft category velocity gaps, and receiving accuracy on high-value items, benchmarking each store against its own history and the estate average.
Ward flags multiple locations with a consistent pattern: tobacco void rates spike during a specific overnight shift window. The amounts are small enough to evade threshold-based alerts but consistent enough to represent significant annual loss per store. Ward attributes the pattern to specific shift schedules, and investigation confirms scan avoidance by a ring of night-shift employees across the affected stores.
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 Convenience shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
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