Stockout Prediction for Convenience & C-Store on Power BI
Convenience retailers on Power BI run stockout through Ward. 3,000+ SKUs across your locations, monitors around the clock.
The convenience stockout stack on Microsoft Power BI
Stockout Prediction is a card type Ward runs continuously. Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
Applied to Convenience & C-Store, the surface area is 3,000+ SKUs across locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
How it runs. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
How the connection works. Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.
The short list
- Supplier-aware lead time modeling
- Priority ranking by revenue impact
- Reduce lost sales by catching gaps early
- Automated replenishment recommendations
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 Stockout matters for Convenience retail
The c-store value proposition is instant availability, a customer who can't find their energy drink drives to the next location, not to the next aisle. Ward models hourly sell-through by daypart, traffic flow, weather, and local events to predict which SKUs will empty before the next delivery window.
What Ward has eyes on.
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.
Ward pulls from Power BI rather than replacing it. Power BI REST API datasets, underlying SQL/Azure data, dataflow outputs come across on a read-only connection, get enriched with contextual data, and come back as findings. Your BI is untouched.
The stockout model runs each day, not on a reporting calendar. It flags the pattern, explains what caused it, and attaches a recommended action before the number reaches a review deck.
At the metric level. Requires hourly velocity modeling across dayparts, delivery window alignment, planogram compliance tracking, and weather-adjusted demand curves for beverage and impulse categories.
Why this combination
is its own problem.
Most stockout 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 Daily order quantities use chain-average lift factors, missing site-specific events (concerts, sports, construction reroutes) that can swing demand 50-200%.
- 02 DSD direct-store-delivery vendors operate on a fixed cycle; when they short-ship, the gap doesn't surface until the next visit.
Benchmarks. C-store top-50 SKUs cover 35-55% of inside-store revenue. Healthy availability on the top-50 runs 95-98%; each percentage drop maps to roughly 0.4-0.7% inside-store revenue loss because of basket-walk-away.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Microsoft Power BI. Ward reads power BI REST API datasets, underlying SQL/Azure data, dataflow outputs 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: 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 hands you insight cards each day, each with the driver and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many cards arrive, it is how many get acted on.
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
Convenience KPI impact
Frequently asked questions
Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice. 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 analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
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.
Requires hourly velocity modeling across dayparts, delivery window alignment, planogram compliance tracking, and weather-adjusted demand curves for beverage and impulse categories.
Ward detects energy drink velocity running well above normal at university-adjacent stores during homecoming weekend, an event its model picked up from local data. Standard delivery won't replenish until Monday. Ward issues stockout prediction cards for the affected stores and recommends emergency redistribution from lower-velocity suburban locations to protect weekend revenue.
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 stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
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