Convenience fill rate from Power BI, briefed to technology
Ward pulls from power BI REST API datasets, underlying SQL/Azure data, dataflow outputs from Microsoft Power BI, tracks fill rate throughout 3,000+ convenience SKUs, and hands back the technology read daily.
Fill Rate Monitoring for Convenience on Power BI, scoped to technology
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.
The business wants AI. You sign off on the architecture. Ward delivers cards scoped to technology decision-making.
Fill Rate Monitoring. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
What Ward does with that: Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Setup: 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
- Threshold-based alerting
- Store-vs-estate benchmarking
- Category-level drill-down
- Estate-wide fill rate dashboard
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.
A Head of IT in convenience retail owns numbers that move faster than the reporting cycle that covers them. Transactions/hour, attach rate, basket size shift store by store, every morning. A monthly pack cannot represent that. The business wants AI. You sign off on the architecture.
- 01 DSD vendor stockouts (Frito, Coca-Cola, Anheuser) sit outside the central inventory system and only surface as POS gaps after the fact.
- 02 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.
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 reads straight 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 daily cards. Your BI is untouched.
The fill rate model runs daily, not on a reporting calendar. It flags the pattern, accounts for the cause, and attaches a recommended move before the number reaches a review deck.
Coverage is store by store, category by category. Ward keeps a running read on transactions/hour, attach rate, basket size over 3,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven locations are not.
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 sends findings on a daily cycle, each with root cause and what to do about it. 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
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. 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.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Technology 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
See what Convenience fill rate problems Ward catches.
Root causes, not just alerts. See it on your data.
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