Stockout Prediction: Pharmacy retail, Tableau data, Supply Chain decisions
A pharmacy VP Supply Chain on Tableau should not be hunting for stockout problems. Ward brings up them every morning.
How a pharmacy VP Supply Chain runs stockout on Tableau
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.
Pharmacy & Health changes the scale of the problem: 20,000+ SKUs, every one of your pharmacies. Regulated inventory, seasonal demand spikes, and front-of-store optimization. Ward handles the complexity so your pharmacists focus on patients.
You find out about stockouts after customers do. Ward returns cards scoped to supply chain decision-making.
What Ward does with that: 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 connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.
The short list
- Automated replenishment recommendations
- Supplier-aware lead time modeling
- Priority ranking by revenue impact
- Reduce lost sales by catching gaps early
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.
Why this combination
is its own problem.
Tableau is the system of record for most pharmacy operators of your size, which means the indicators Ward needs are already there. The gap is not data collection. It is that nobody has time to read tableau Hyper extracts and underlying database (direct) every morning throughout every store.
- 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 Ward has eyes on.
Ward ingests tableau Hyper extracts, underlying database (direct), published data source metadata from Tableau on a read-only connection. Nothing is written back, and your BI configuration does not change. Tableau stays the system of record.
The stockout model runs every morning, not on a reporting calendar. It flags the pattern, attributes root cause, and attaches the next step before the number reaches a review deck.
Coverage is store by store, category by category. Ward watches Rx fill rate, OTC attach rate, expiry waste % throughout 20,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven pharmacies are not.
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.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Tableau. Ward pulls from tableau Hyper extracts, underlying database (direct), published data source metadata and starts building baselines. First findings land inside 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: steady state
Ward hands back findings every morning, each with what caused it and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many findings arrive, it is how many get acted on.
How Ward connects to Tableau
Ward does not replace Tableau. Ward adds the proactive layer Tableau lacks. When a metric moves, Ward explains why and recommends action.
Setup: Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.
Data Ward reads from Tableau
Impact metrics with Tableau
Data lake enrichment
Ward enriches Tableau data with: Tableau data sources, Underlying database, Weather & events, Competitor pricing, Customer data
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 connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context. Data points include: Tableau Hyper extracts, Underlying database (direct), Published data source metadata.
Yes. Ward reads Tableau 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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