Ward monitors demand so your Pharmacy team can act on it early.
Most Pharmacy retailers find demand problems in the post-mortem. Ward finds them while you can still act.
Why demand matters
in pharmacy retail.
No other vertical faces disease seasonality the way pharmacy does, flu, allergy, cold seasons, and vaccination drives create demand waves that vary by region and severity every year. Ward integrates public health signals with historical patterns to forecast front-of-store OTC demand at a granularity traditional models miss.
Benchmarks. Pharmacy seasonal forecast accuracy: 25-40% MAPE during steady demand, blowing out to 60%+ during illness surges without disease-signal integration. Operators using public health data typically reduce surge MAPE by 15-30 points and capture 10-25% more peak-week revenue.
Allergy season pre-positioning, Southeast region
Ward detects early pollen counts running well above seasonal norms in the Southeast, weeks earlier than the prior year. Historical correlation predicts a surge in allergy OTC demand shortly after pollen peaks. Ward issues demand adjustment cards for stores in the region recommending endcap resets and forward buys on top allergy SKUs. Stores that act on the recommendation significantly outperform those relying on last year's seasonal plan.
Three pitfalls Ward catches
in pharmacy demand.
- 01 Seasonal demand curves are set off prior-year peaks regardless of whether the actual pollen, flu, or COVID activity matches; the lag costs 2-6 weeks of missed peak revenue.
- 02 Vaccination drives create predictable companion-OTC spikes (Tylenol, fluids, tissues) that aren't modeled in standard forecasts.
- 03 Regional outbreak data lives in public health systems; pharmacy forecasting often runs on chain-aggregated signals that wash out the regional variance.
How Ward runs demand
for pharmacy retailers.
-
01
Integrate disease surveillance feeds
Ward joins CDC ILI, pollen, UV, and local public health data to OTC demand history at the region-store-day grain.
-
02
Use Rx as a leading OTC indicator
Ward maps high-confidence Rx-to-OTC pairs and uses script velocity as a 24-72 hour leading signal for companion product demand.
-
03
Issue regional demand adjustment cards
When surveillance signals exceed seasonal norms, Ward triggers pre-position recommendations 1-2 weeks ahead of expected peak.
What a Ward card looks like.
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…
Dashboards
Pinned views built from saved data-lake queries.
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" |
Pharmacy demand:
the shift.
- ×Seasonal illness demand
- ×Rx-to-OTC conversion
- ×Expiry management
- ✓Store-SKU-day level precision
- ✓Weather-driven adjustment
- ✓Event and holiday modeling
Pharmacy KPI impact.
Regulated inventory is outside Ward's optimization scope. Impact concentrates on front-of-store categories, OTC adjacency, and seasonal wellness.
Questions about pharmacy demand.
No other vertical faces disease seasonality the way pharmacy does, flu, allergy, cold seasons, and vaccination drives create demand waves that vary by region and severity every year. Ward integrates public health signals with historical patterns to forecast front-of-store OTC demand at a granularity traditional models miss.
Ward detects early pollen counts running well above seasonal norms in the Southeast, weeks earlier than the prior year. Historical correlation predicts a surge in allergy OTC demand shortly after pollen peaks.
Ward integrates epidemiological signals (CDC ILI, pollen indices, UV index), Rx script volume as a leading OTC demand indicator, and local health demographic profiles. Forecast accuracy is measured separately for illness-driven and baseline demand because the error profiles differ fundamentally.
First demand insight cards arrive within 48 hours. Stable pharmacy baselines form within two weeks.
More Pharmacy insight cards.
Pharmacy retailers: see what demand problems Ward catches.
Root causes, not just alerts. See it on your data.
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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.