Stockout Prediction for Pharmacy on SAP, built for supply chain
Know before the shelf empties. Ward runs it on SAP data across your pharmacy estate, scoped to supply chain.
How a pharmacy VP Supply Chain runs stockout on SAP Retail
You find out about stockouts after customers do. Ward filters to what a VP Supply Chain can act on and drops the rest.
Here is stockout prediction in plain terms. Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
For Pharmacy & Health retailers, that means monitoring 20,000+ SKUs across pharmacies. Regulated inventory, seasonal demand spikes, and front-of-store optimization. Ward handles the complexity so your pharmacists focus on patients.
Under the hood. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
Getting the data in. 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
- Priority ranking by revenue impact
- Reduce lost sales by catching gaps early
- Automated replenishment recommendations
- Supplier-aware lead time modeling
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled cough and cold against the illness curve for the 38 Northeast pharmacies. Front of store is running two weeks behind the demand signal.
| Signal | Finding |
|---|---|
otc_sales_daily | Cough/cold units +31% WoW, on-shelf availability down to 88% |
replenishment | Vendor lead time 6 days, reorder points still on the summer baseline |
front_store.attach | Wellness attach on flu-season fills 19% vs. 34% chain best |
Recommend: raise cough/cold reorder points at all 38 stores now, pull the wellness endcap forward two weeks, and add the attach prompt at the counter.
otc_sales_daily…
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 |
|---|---|---|
mckesson_wholesale_orders | import | 2m ago |
cardinal_lot_expiry | import | 2m ago |
sap_pos_transactions | import | 14m ago |
sap_inventory_snapshot | import | 1h ago |
retail_otc_sales_daily | import | 1h ago |
retail_front_store_margin | import | 1h ago |
retail_planogram_audit | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-front-store | permit | Model::"front_store_margin" |
ops-read-default | permit | Model::* |
phi-forbid-all | forbid | Model::"patient_*" |
supply-read-expiry | permit | Model::"lot_expiry" |
Why Stockout matters for Pharmacy retail
Ward doesn't touch regulated Rx inventory, but front-of-store OTC demand can spike dramatically at the zip-code level when illness season hits. Ward models these surges using CDC surveillance data, local school absenteeism signals, and historical seasonal patterns to predict OTC demand 48-72 hours before it arrives.
What Ward has eyes on.
Every one of your pharmacies gets its own baseline. Ward watches Rx fill rate, OTC attach rate, expiry waste % against it and pulls forward only the deviations that hold up. The two that show up most in pharmacy retail are seasonal illness demand and rx-to-OTC conversion, and both are baseline problems before they are P&L problems.
The connection to SAP Retail is read-only and runs on your schedule. Ward reads POS transactions, inventory positions, and the rest of the feed, then cross-references it with external context your ERP does not carry: weather, local events, competitor pricing.
The stockout model runs on a daily cycle, not on a reporting calendar. It picks up the pattern, explains what caused it, and attaches a recommended move before the number reaches a review deck.
At the metric level. Ward focuses on illness-driven demand modeling, OTC-Rx correlation (Rx script spikes predict companion OTC demand within 48 hours), seasonal product velocity, and supplement trend detection.
Why this combination
is its own problem.
Stockout Prediction needs POS transactions and inventory positions at minimum. SAP Retail carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
- 01 Rx script volume is a leading indicator of OTC companion demand (Tamiflu Rx → cough/cold OTC) but most chains don't link the two systems.
- 02 Allergy and cold demand is treated as one season; pollen peaks shift 2-4 weeks each year and the static plan misses the actual peak.
Benchmarks. Pharmacy front-of-store seasonal categories can swing 200-500% during peak illness weeks. Operators using disease surveillance signals typically capture 20-40% more peak-week revenue than chains relying on prior-year calendars alone.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to SAP Retail. Ward pulls from POS transactions, inventory positions, purchase orders and starts building baselines. First daily cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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
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
You find out about stockouts after customers do.
- ×Demand forecasts are off by 15-25% and nobody catches it until the shelf is empty
- ×Supplier fill rate problems announce themselves at the receiving dock
- ×Safety stock levels get set once a year and left alone
- ×No early warning system for supply chain disruptions
- ×Replenishment exceptions require manual triage every morning
- ✓Stockout prediction cards arrive 24-72 hours before empty shelves
- ✓Supplier fill rate tracking with automatic escalation
- ✓Dynamic safety stock recommendations based on current demand signals
- ✓Weather, event, and macro-driven demand adjustments
- ✓Replenishment exceptions auto-prioritized by revenue impact
Stockouts cost retailers $1.14 trillion in missed sales globally each year. Source: IHL Group
Pharmacy KPI impact
Frequently asked questions
Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice. For Pharmacy retail specifically, Ward monitors 20,000+ SKUs across your pharmacies and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Rx fill rate, OTC attach rate, Expiry waste %, Script count, Front-store margin at the store-category level. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
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 Pharmacy-specific insight cards. No custom development required.
You find out about stockouts after customers do. Ward solves this with automated insight cards: Stockout prediction cards arrive 24-72 hours before empty shelves. Supplier fill rate tracking with automatic escalation. Dynamic safety stock recommendations based on current demand signals.
Ward delivers daily insight cards covering Rx fill rate, OTC attach rate, Expiry waste %, tailored for Supply Chain decision-making. Each card includes what changed, why it matters, and what to do next.
Ward focuses on illness-driven demand modeling, OTC-Rx correlation (Rx script spikes predict companion OTC demand within 48 hours), seasonal product velocity, and supplement trend detection.
Ward's disease surveillance model detects elevated ILI rates in several metro areas days before competitors react. Ward issues stockout prediction cards with store-level uplift estimates and recommended emergency orders. Stores are fully stocked when the wave hits, capturing share from competitors scrambling with empty shelves.
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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