Pharmacy assortment from Oracle, briefed to technology
Stock what sells. Cut what doesn't. Ward runs it on Oracle data across your pharmacy footprint, scoped to technology.
The full picture: pharmacy assortment, Oracle data, Technology decisions
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 business wants AI. You sign off on the architecture. Ward writes the finding at the altitude a Head of IT works at.
Here is assortment planning in plain terms. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Under the hood. 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 assortment model runs daily, not on a reporting calendar. It catches the pattern, traces the driver, and attaches what to do about it 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 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.
Ward reads Oracle rather than replacing it. Sales audit, inventory positions, allocation come across on a read-only connection, get enriched with contextual data, and come back as findings. Your ERP 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.
Why this combination
is its own problem.
A generic assortment model applied to a pharmacy store base 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 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 New wellness/supplement items get added to chase trend without displacing slow tail SKUs, so net assortment productivity goes negative.
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 plugs into Oracle Retail with read-only credentials and begins ingesting sales audit and inventory positions. 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 findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands back daily cards every morning, each with the driver and a recommended action. 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
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
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.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering Rx fill rate, OTC attach rate, Expiry waste %, tailored for Technology 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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Root causes, not just alerts. See it on your data.
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