Director Store Ops: fill rate straight off SAP
Ward reads POS transactions, inventory positions, purchase orders from SAP Retail, runs fill rate on it, and hands back the store operations read each day.
Fill Rate Monitoring on SAP, for store operations
Managing 800 stores from a spreadsheet is insane. Ward brings up the readings that change a store operations decision.
Fill Rate Monitoring is a insight card type Ward runs continuously. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
How it runs. 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.
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 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.
SAP Retail was built for transactions, not for store operations decisions. The data is right there and it is in the wrong shape. Ward reads POS transactions and inventory positions and rewrites them as a decision.
What Ward has eyes on.
The fill rate model runs daily, not on a reporting calendar. It spots the pattern, attributes the cause, and attaches the next step before the number reaches a review deck.
The connection to SAP Retail is read-only and runs on your schedule. Ward pulls from POS transactions, inventory positions, and the rest of the feed, then stitches together it with external context your ERP does not carry: weather, local events, competitor pricing.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads from SAP Retail with read-only credentials and begins ingesting POS transactions and inventory positions. No config changes on your side. First daily 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 returns insight cards 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 daily cards arrive, it is how many get acted on.
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
Managing 800 stores from a spreadsheet is insane.
- ×Morning check-ins rely on phone calls and email chains
- ×No single view of which stores need attention today
- ×Labor scheduling is disconnected from demand signals
- ×Planogram compliance is checked manually, quarterly
- ×Exception management is reactive and inconsistent
- ✓Morning brief delivered at 06:47 with prioritized action list
- ✓Estate-wide heat map of store performance, updated hourly
- ✓Staffing recommendations correlated with predicted traffic
- ✓Planogram compliance anomalies detected and flagged
- ✓Consistent exception handling with recommended actions
Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research
Frequently asked questions
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.
Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.
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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