Convenience fill rate from Power BI, briefed to procurement
Every empty shelf is a lost sale. Ward runs it on Power BI data over your convenience footprint, scoped to procurement.
The full picture: convenience fill rate, Power BI data, Procurement decisions
Merchandising wants Ward. You sign the contract. Ward writes the finding at the altitude a Head of Procurement works at.
Fill Rate Monitoring, in one sentence. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
In a Convenience & C-Store store base the job is 3,000+ SKUs over locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
The mechanism. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
The connection itself. 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
- Store-vs-estate benchmarking
- Category-level drill-down
- Estate-wide fill rate dashboard
- Threshold-based alerting
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 Fill Rate matters for Convenience retail
With replenishment only 2-3 times per week, a Tuesday stockout might not resolve until Thursday. Ward monitors sell-through velocity between delivery windows and predicts which items will deplete before the next drop, giving operators time to adjust orders or arrange emergency fills on high-margin categories.
Why this combination
is its own problem.
Fill Rate Monitoring needs power BI REST API datasets and underlying SQL/Azure data at minimum. Microsoft Power BI carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
- 01 Average daily depletion masks the daypart spike that empties shelves before the next delivery; high-margin tobacco and beverages can run out by midweek even when the daily total is on plan.
- 02 DSD vendor stockouts (Frito, Coca-Cola, Anheuser) sit outside the central inventory system and only surface as POS gaps after the fact.
Benchmarks. C-store inside fill rate: top-50 SKUs at 95-98% is healthy. Below 92% on high-margin items (tobacco, beverages, premium beer) maps to roughly 0.6-1.0% category revenue erosion per missing point. Between-delivery depletion is the single biggest fill rate driver in c-store.
What Ward has eyes on.
Ward keeps a running read on 3,000+ SKUs across 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 reads power BI REST API datasets, underlying SQL/Azure data, dataflow outputs from Microsoft Power BI on a read-only connection. Nothing is written back, and your BI configuration does not change. Power BI stays the system of record.
The fill rate model runs every morning, not on a reporting calendar. It picks up the pattern, traces the driver, and attaches what to do about it before the number reaches a review deck.
At the metric level. Ward tracks inter-delivery depletion velocity, delivery-window-aware stockout prediction, high-margin category availability, and planogram compliance as a proxy for visual availability.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads from Microsoft Power BI with read-only credentials and begins ingesting power BI REST API datasets and underlying SQL/Azure data. No config changes on your side. 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: steady state
Ward delivers insight cards daily, each with the cause and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many findings 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
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
Convenience KPI impact
Frequently asked questions
Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. 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 tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
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.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks inter-delivery depletion velocity, delivery-window-aware stockout prediction, high-margin category availability, and planogram compliance as a proxy for visual availability.
Mid-week with the next delivery two days out, Ward detects dozens of stores on pace to stock out on top tobacco SKUs, a category representing a major share of inside gross profit. Ward issues fill rate alerts with recommended emergency orders from the nearest distribution point. Store managers receive automated alerts with pre-built order lists.
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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