Convenience shrinkage from Power BI, briefed to supply chain
Ward ingests power BI REST API datasets, underlying SQL/Azure data, dataflow outputs from Microsoft Power BI, keeps a running read on shrinkage throughout 3,000+ convenience SKUs, and sends the supply chain read on a daily cycle.
The full picture: convenience shrinkage, Power BI data, Supply Chain decisions
A Convenience & C-Store operator is watching 3,000+ SKUs throughout locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
You find out about stockouts after customers do. Ward writes the finding at the altitude a VP Supply Chain 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.
How Ward delivers Shrinkage cards: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Setup: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
What it does
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
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.
Coverage is store by store, category by category. Ward monitors transactions/hour, attach rate, basket size throughout 3,000+ 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 locations are not.
The connection to Microsoft Power BI is read-only and runs on your schedule. Ward pulls from power BI REST API datasets, underlying SQL/Azure data, and the rest of the feed, then joins it with external context your BI does not carry: weather, local events, competitor pricing.
The shrinkage model runs daily, not on a reporting calendar. It spots the pattern, attributes the cause, and attaches the next step before the number reaches a review deck.
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.
Most shrinkage projects stall at data access. This one does not, because Power BI already exposes power BI REST API datasets, underlying SQL/Azure data, dataflow outputs through an API Ward reads directly.
- 01 Vendor-direct receiving for tobacco and beer happens outside the POS; shrink in those categories surfaces only at periodic counts.
- 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: connect
Read-only credentials to Microsoft Power BI. Ward pulls from power BI REST API datasets, underlying SQL/Azure data, dataflow outputs and starts building baselines. First daily cards 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: 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 Microsoft Power BI
Ward sits alongside Power BI. Your dashboards visualize. Ward detects and explains what changed. No dashboard login needed for your morning brief.
Setup: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
Data Ward reads from Power BI
Impact metrics with Power BI
Data lake enrichment
Ward enriches Power BI data with: Power BI datasets, Underlying SQL/Azure data, Weather & events, Demographics, Custom feeds
You find out about stockouts after customers do.
- ×Demand forecasts are off by 15-25% and nobody catches it until the shelf is empty
- ×Supplier fill rate problems announce themselves at the receiving dock
- ×Safety stock levels get set once a year and left alone
- ×No early warning system for supply chain disruptions
- ×Replenishment exceptions require manual triage every morning
- ✓Stockout prediction cards arrive 24-72 hours before empty shelves
- ✓Supplier fill rate tracking with automatic escalation
- ✓Dynamic safety stock recommendations based on current demand signals
- ✓Weather, event, and macro-driven demand adjustments
- ✓Replenishment exceptions auto-prioritized by revenue impact
Stockouts cost retailers $1.14 trillion in missed sales globally each year. Source: IHL Group
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.
Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched. Data points include: Power BI REST API datasets, Underlying SQL/Azure data, Dataflow outputs.
Yes. Ward reads Power BI data and combines it with contextual signals (weather, events, demographics) to generate Convenience-specific insight cards. No custom development required.
You find out about stockouts after customers do. Ward solves this with automated insight cards: Stockout prediction cards arrive 24-72 hours before empty shelves. Supplier fill rate tracking with automatic escalation. Dynamic safety stock recommendations based on current demand signals.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Supply Chain 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
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