Looker plus Ward: fill rate for a Head of LP
Ward pulls from looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio, runs fill rate on it, and hands back the loss prevention read on a daily cycle.
How a Head of LP runs fill rate off Looker / Looker Studio
Fill Rate Monitoring, in one sentence. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
Shrinkage costs you more than you think. Ward finds out where. Ward raises the readings that change a loss prevention decision.
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.
The connection itself. Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
What it does
- Category-level drill-down
- Estate-wide fill rate dashboard
- Threshold-based alerting
- Store-vs-estate benchmarking
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
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 |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Why this combination
is its own problem.
Fill Rate Monitoring needs looker API for query results and underlying database (direct) at minimum. Looker / Looker Studio carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
What Ward has eyes on.
Ward ingests looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio on a read-only connection. Nothing is written back, and your BI configuration does not change. Looker stays the system of record.
Every empty shelf is a lost sale. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Looker / Looker Studio. Ward pulls from looker API for query results, underlying database (direct), lookML model metadata 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: operating rhythm
Insight cards arrive each day 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 Looker / Looker Studio
Ward does not replace Looker. Ward watches the same data Looker visualizes and proactively alerts when something changes. Your dashboards stay. Ward adds intelligence.
Setup: Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
Data Ward reads from Looker
Impact metrics with Looker
Data lake enrichment
Ward enriches Looker data with: Looker query results, Underlying database, Weather & events, Competitor data, Customer segments
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
Frequently asked questions
Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses. Data points include: Looker API for query results, Underlying database (direct), LookML model metadata.
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.
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 fill rate problems Ward catches.
Root causes, not just alerts. See it on your data.
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