Pharmacy assortment from Oracle, briefed to finance
Your P&L surprises are born on the store floor. Ward spots pharmacy assortment movement in your Oracle data early, with what caused it and what to do about it attached.
Assortment Planning for Pharmacy on Oracle, scoped to finance
Your P&L surprises are born on the store floor. Ward sends daily cards scoped to finance decision-making.
What assortment planning does: 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 over 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 reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
What you get
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
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.
Ward pulls from sales audit, inventory positions, allocation from Oracle Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. Oracle stays the system of record.
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 raises 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.
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.
Oracle Retail 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 sales audit and inventory positions every morning over every store.
- 01 New wellness/supplement items get added to chase trend without displacing slow tail SKUs, so net assortment productivity goes negative.
- 02 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.
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: read-only connection
Ward connects to Oracle Retail with read-only credentials and begins ingesting sales audit and inventory positions. No config changes on your side. First findings arrive in two days.
-
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: operating rhythm
Daily 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.
How Ward connects to Oracle Retail
Ward integrates with Oracle Retail Merchandising (RMFCS), Oracle Retail Demand Forecasting, and Oracle Retail Analytics. Full stack visibility.
Setup: Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Data Ward reads from Oracle
Impact metrics with Oracle
Data lake enrichment
Ward enriches Oracle data with: Sales audit data, Weather & events, Competitor pricing, Demographic data, Supplier scorecards
Your P&L surprises are born on the store floor.
- ×Margin erosion only surfaces at month-end close
- ×Inventory carrying costs are a black box
- ×Working capital tied up in slow-moving stock nobody is watching
- ×Same-store sales comps lack decomposition into actionable drivers
- ×Capex decisions for store remodels lack unit-economics evidence
- ✓GMROI tracking by category with weekly insight cards
- ✓Inventory carrying cost alerts when capital efficiency drops
- ✓Working capital optimization recommendations based on turnover trends
- ✓SSS decomposition into traffic, conversion, and basket components
- ✓Store-level unit economics cards for capex prioritization
Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. 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 reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments. Data points include: Sales audit, Inventory positions, Allocation, Replenishment, Demand forecasts, Price management.
Yes. Ward reads Oracle data and combines it with contextual signals (weather, events, demographics) to generate Pharmacy-specific insight cards. No custom development required.
Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.
Ward delivers daily insight cards covering Rx fill rate, OTC attach rate, Expiry waste %, tailored for Finance 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
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