Stockout Prediction for Pharmacy merchandising
Ward raises stockout findings scoped to merchandising, over every one of your pharmacy pharmacies.
Stockout Prediction on a pharmacy store base, scoped to merchandising
Your category managers are drowning in spreadsheets. Ward delivers cards scoped to merchandising decision-making.
What stockout prediction does: Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
Applied to Pharmacy & Health, the surface area is 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.
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.
The stockout model runs each day, not on a reporting calendar. It flags the pattern, attributes the driver, and attaches what to do about it before the number reaches a review deck.
Ward keeps a running read on 20,000+ SKUs throughout your pharmacies, at the store-category level rather than the chain roll-up. The metrics under watch include Rx fill rate, OTC attach rate, expiry waste %. A roll-up hides a single-store problem inside a healthy average, which is how seasonal illness demand stays invisible for a quarter.
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.
The merchandising problem in pharmacy retail is not missing data. It is that Rx fill rate, OTC attach rate, expiry waste % live in different systems on different refresh schedules, and reconciling them is a person-week. Ward does the reconciliation and delivers the two-line version.
- 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.
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 whatever holds your transaction and inventory data. Ward starts building baselines the same day. First daily cards land within 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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: operating rhythm
Insight cards arrive each day 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.
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
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.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Rx fill rate, OTC attach rate, Expiry waste %, tailored for Merchandising 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
See what Pharmacy stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
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