Ward runs assortment on your Tableau pharmacy data
Your Tableau data already carries the assortment signal. Ward reads straight from it, attributes the cause, and attaches the next step.
Assortment Planning for Pharmacy & Health, running on Tableau
Here is assortment planning in plain terms. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
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.
What Ward does with that: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
The connection itself. 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.
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.
Every one of your pharmacies gets its own baseline. Ward scans continuously Rx fill rate, OTC attach rate, expiry waste % against it and brings up only the deviations that hold up. The two that show up most in pharmacy retail are seasonal illness demand and rx-to-OTC conversion, and both are baseline problems before they are P&L problems.
The connection to Tableau is read-only and runs on your schedule. Ward reads tableau Hyper extracts, underlying database (direct), and the rest of the feed, then stitches together it with external context your BI does not carry: weather, local events, competitor pricing.
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.
Why this combination
is its own problem.
A generic assortment model applied to a pharmacy fleet produces alerts nobody trusts. The thresholds are wrong, because pharmacy baselines are wrong for it. Ward learns the baseline from your own pharmacies instead of importing one.
- 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 the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Tableau. Ward reads tableau Hyper extracts, underlying database (direct), published data source metadata and starts building baselines. First cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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 cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward delivers findings daily, each with root cause and what to do about it. 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
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.
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.
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