VP Supply Chain: pharmacy assortment, read from Tableau
A pharmacy VP Supply Chain on Tableau should not be hunting for assortment problems. Ward raises them on a daily cycle.
Assortment Planning for Pharmacy on Tableau, scoped to supply chain
You find out about stockouts after customers do. Ward brings up the indicators that change a supply chain decision.
Here is assortment planning in plain terms. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
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.
The mechanism. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
How the connection works. Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.
What it does
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
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 Assortment matters for Pharmacy retail
The assortment decisions that pay are the ones that put OTC companions for high-volume prescriptions in stock and in the right place. Trendy wellness gets the attention; the companion aisle earns the money. Ward maps Rx-to-OTC companion patterns and recommends front-of-store assortment based on each store's actual prescription mix, not national averages.
Why this combination
is its own problem.
Assortment Planning behaves differently in pharmacy retail than it does anywhere else. The fleet shape, the SKU count, and the speed of the category all change what counts as a real indicator and what is noise. Ward is tuned to the pharmacy version.
- 01 Front-of-store planograms are set chain-wide while Rx mix varies dramatically by store demographics; diabetes-heavy and oncology-heavy stores need fundamentally different OTC depth.
- 02 OTC shelves that face the pharmacy counter outperform OTC shelves at the front-of-store entrance, but the planogram math doesn't account for this layout effect.
Benchmarks. Pharmacy front-of-store cluster variance: high-Rx-volume stores typically need 2-4x the OTC depth of low-Rx stores in matching condition categories. Cluster-aware assortment usually lifts front-end revenue per Rx customer by 8-15%.
What Ward has eyes on.
Stock what sells. Cut what doesn't. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
Coverage is store by store, category by category. Ward monitors 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 footprint is fine and knowing which seven pharmacies are not.
Ward reads Tableau rather than replacing it. Tableau Hyper extracts, underlying database (direct), published data source metadata come across on a read-only connection, get enriched with contextual data, and come back as daily cards. Your BI is untouched.
At the metric level. Ward tracks Rx-to-OTC companion purchase rates, category productivity per square foot, health condition clustering by store, and new-item triage, whether a wellness SKU can earn its space against a proven companion item.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to Tableau with read-only credentials and begins ingesting tableau Hyper extracts and underlying database (direct). No config changes on your side. First daily cards arrive inside the first 48 hours.
-
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: steady state
Ward delivers cards on a daily cycle, each with root cause and a recommended action. 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 analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
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 tracks Rx-to-OTC companion purchase rates, category productivity per square foot, health condition clustering by store, and new-item triage, whether a wellness SKU can earn its space against a proven companion item.
Stores with the highest metformin prescription volume are significantly under-assorted in diabetes management OTC, glucose monitors, test strips, diabetic-friendly snacks, foot care. They share the same planogram as stores with half the Rx volume. Ward recommends expanding diabetes OTC in high-volume stores by reallocating space from underperforming seasonal items.
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 assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
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.