Assortment Planning: Pharmacy retail, Oracle data, Supply Chain decisions
A pharmacy VP Supply Chain on Oracle should not be hunting for assortment problems. Ward brings up them daily.
Assortment Planning for Pharmacy on Oracle, scoped to supply chain
Applied to Pharmacy & Health, the surface area is 20,000+ SKUs throughout 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 delivers findings scoped to supply chain decision-making.
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
What Ward does with that: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Getting the data in. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Capabilities
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
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.
The connection to Oracle Retail is read-only and runs on your schedule. Ward reads straight from sales audit, inventory positions, and the rest of the feed, then combines it with external context your ERP does not carry: weather, local events, competitor pricing.
The assortment model runs each day, not on a reporting calendar. It picks up the pattern, traces the cause, and attaches a recommended action before the number reaches a review deck.
Every one of your pharmacies gets its own baseline. Ward tracks Rx fill rate, OTC attach rate, expiry waste % against it and surfaces 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.
Running Ward on Oracle in a pharmacy estate skips the usual first step. There is no ingestion project, because Oracle Retail already holds sales audit, inventory positions, allocation. Ward reads straight from what is there.
- 01 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.
- 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 daily cards arrive within 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 cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands back insight cards every morning, 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 daily cards arrive, it is how many get acted on.
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
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 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.
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
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