Stockout insight cards for Pharmacy & Health.
Ward delivers stockout findings as insight cards with recommended actions.
Why stockout matters
in 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.
Industry 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.
Flu wave front-of-store prep, 600-store chain
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.
What Ward actually tracks
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.
Data signals
POS at SKU-store-day for OTC, Rx fill volume by category, CDC and local public health feeds, school district absenteeism, pollen and UV indices.
Three pitfalls Ward catches
in pharmacy stockout.
- 01 OTC ordering follows national seasonal calendars, missing the regional 1-3 week lead/lag that disease surveillance exposes.
- 02 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.
- 03 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.
How Ward runs stockout
for pharmacy retailers.
-
01
Layer disease surveillance onto OTC forecasts
Ward joins CDC ILI rates, local school absenteeism, and pollen indices to OTC demand history at the regional and store level.
-
02
Connect Rx-to-OTC companion patterns
Ward identifies 50-150 high-confidence Rx-to-OTC pairs (e.g., Tamiflu → cough/cold) and uses Rx script velocity as a 24-72 hour OTC leading indicator.
-
03
Issue regional pre-position cards
Cards recommend emergency orders, endcap resets, and forward buys for affected regions ahead of the surge.
What a Ward card looks like.
23 SKUs trending toward zero-on-hand within 48 hours. Replenishment recommendation attached. Priority: dairy and produce categories.
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…
Dashboards
Pinned views built from saved data-lake queries.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
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 |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
Light and dark themes are available. Your choice is remembered per browser.
Pharmacy stockout:
the shift.
- ×Seasonal illness demand
- ×Rx-to-OTC conversion
- ×Expiry management
- ✓Reduce lost sales by catching gaps early
- ✓Automated replenishment recommendations
- ✓Supplier-aware lead time modeling
Questions about pharmacy stockout.
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.
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.
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.
First stockout insight cards arrive within 48 hours. Robust pharmacy baselines form within two weeks. Regulated inventory is outside Ward's optimization scope. Impact concentrates on front-of-store categories, OTC adjacency, and seasonal wellness.
Pharmacy stockout
by data source.
More Pharmacy insight cards.
Pharmacy retailers: see what stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.