Grocery fill rate from SAP, briefed to loss prevention
A grocery Head of LP on SAP should not be hunting for fill rate problems. Ward brings up them every morning.
The full picture: grocery fill rate, SAP data, Loss Prevention decisions
Shrinkage costs you more than you think. Ward finds out where. Ward filters to what a Head of LP can act on and drops the rest.
What fill rate monitoring does: Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
A Grocery & Supermarket operator is continuous review 30,000+ SKUs throughout stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
Under the hood. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
What you get
- Estate-wide fill rate dashboard
- Threshold-based alerting
- Store-vs-estate benchmarking
- Category-level drill-down
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 |
relex_replenishment_plan | import | 1h ago |
blue_yonder_forecast_daily | import | 1h ago |
retail_fresh_waste_daily | import | 1h ago |
retail_promo_calendar | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fresh-team-west | permit | Model::"fresh_waste_daily" |
Why Fill Rate matters for Grocery retail
Estate-wide fill rate averages mask critical variation, a chain at 94% overall can have dozens of stores hemorrhaging revenue below 88%. Ward monitors fill rate at the store-category-hour level, because a produce section that empties by 4 PM is a fundamentally different problem than one consistently understocked.
What Ward has eyes on.
Every one of your stores gets its own baseline. Ward monitors fill rate, shrinkage %, fresh waste % against it and surfaces only the deviations that hold up. The two that show up most in grocery retail are fresh waste & spoilage and on-shelf availability gaps, and both are baseline problems before they are P&L problems.
Ward reads straight from POS transactions, inventory positions, purchase orders from SAP Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. SAP stays the system of record.
The fill rate model runs daily, not on a reporting calendar. It catches the pattern, explains root cause, and attaches a recommended action before the number reaches a review deck.
At the metric level. Ward tracks on-shelf availability, backroom-to-shelf replenishment speed, DC delivery reliability, and intra-day depletion curves. The critical insight is separating supply problems from execution problems, since the fix is completely different.
Why this combination
is its own problem.
A Head of LP in grocery retail owns numbers that move faster than the reporting cycle that covers them. Fill rate, shrinkage %, fresh waste % shift store by store, every morning. A monthly pack cannot represent that. Shrinkage costs you more than you think. Ward finds out where.
- 01 Backroom-full-but-shelf-empty is a labor problem mistaken for a supply problem because both report as out-of-stock.
- 02 Daily fill rate snapshots miss the afternoon emptying pattern; a 96% open-of-day reading can drop to 84% by 6 PM in produce.
Benchmarks. Healthy grocery on-shelf availability: 96-98% center store, 92-95% perishable, 88-92% during the closing daypart. Each percentage-point drop on top-100 SKUs ties to roughly 0.3-0.5% category revenue erosion; on milk, eggs, and bread the multiplier is 2-3x because of basket abandonment.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to SAP Retail with read-only credentials and begins ingesting POS transactions and inventory positions. No config changes on your side. First daily cards arrive in two days.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Insight cards arrive every morning 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 SAP Retail
Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
Data Ward reads from SAP
Impact metrics with SAP
Data lake enrichment
Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates
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
Grocery KPI impact
Frequently asked questions
Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. For Grocery retail specifically, Ward monitors 30,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Fill rate, Shrinkage %, Fresh waste %, Promo lift, Basket size 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 reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.
Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
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.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks on-shelf availability, backroom-to-shelf replenishment speed, DC delivery reliability, and intra-day depletion curves. The critical insight is separating supply problems from execution problems, since the fix is completely different.
Ward's morning fill rate card shows the estate is healthy overall, but flags seven stores below threshold. It attributes root cause for each: late DC deliveries for some (already en route), a supplier fill rate issue on dairy for others, and an afternoon depletion pattern in produce at two stores suggesting insufficient replenishment labor during the mid-shift window. The VP acts on the labor issues and monitors the rest in under five minutes.
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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