VP Supply Chain: pharmacy fill rate, read from Oracle Retail
Ward ingests sales audit, inventory positions, allocation from Oracle Retail, scans continuously fill rate throughout 20,000+ pharmacy SKUs, and hands you the supply chain read every morning.
The full picture: pharmacy fill rate, Oracle data, Supply Chain decisions
Fill Rate Monitoring, in one sentence. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
A Pharmacy & Health operator is watching 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 surfaces the inputs that change a supply chain decision.
What Ward does with that: Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
How the connection works. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Capabilities
- Category-level drill-down
- Estate-wide fill rate dashboard
- Threshold-based alerting
- Store-vs-estate benchmarking
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 Fill Rate matters for Pharmacy retail
Illness demand is hyperlocal and fast-moving, one zip code can spike while another 10 miles away sits at baseline. A single week of empty shelves during a surge pushes customers to Amazon or a competitor. Ward monitors front-of-store fill rate with a focus on illness-sensitive categories, flagging stores depleting faster than standard replenishment can cover.
What Ward has eyes on.
The fill rate model runs each day, not on a reporting calendar. It spots the pattern, explains root cause, and attaches a recommended action before the number reaches a review deck.
Ward keeps a running read on 20,000+ SKUs throughout your pharmacies, at the store-category level rather than the chain roll-up. The metrics under watch include Rx fill rate, OTC attach rate, expiry waste %. A roll-up hides a single-store problem inside a healthy average, which is how seasonal illness demand stays invisible for a quarter.
Ward ingests 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.
At the metric level. Ward focuses on illness-sensitive category availability, seasonal product positioning timing, companion availability alongside high-Rx-volume items, and endcap fill rate as a proxy for promotional execution.
Why this combination
is its own problem.
A generic fill rate model applied to a pharmacy estate 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 Endcap and promotional positioning misexecution costs more than central-warehouse stockouts because it cuts through the highest-velocity surface area, but compliance is checked weekly at best.
- 02 Estate-wide OTC fill rate looks healthy while specific zip codes hit zero on illness-surge SKUs; the regional signal is buried in the chain average.
Benchmarks. Pharmacy front-of-store fill rate: 95-98% chain average, dropping to 75-85% during illness surges without active region monitoring. Each fill-rate point lost on top-100 OTC SKUs ties to roughly 0.4-0.7% category revenue erosion; on illness-surge categories the multiplier is 2-3x.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Oracle Retail. Ward reads sales audit, inventory positions, allocation and starts building baselines. First insight 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 findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands you daily cards each day, each with the cause and a recommended move. 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 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 monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. 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 tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
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 focuses on illness-sensitive category availability, seasonal product positioning timing, companion availability alongside high-Rx-volume items, and endcap fill rate as a proxy for promotional execution.
Ward detects fill rates on infant fever reducers and pediatric electrolytes dropping below threshold at stores in a suburban corridor, correlating with a local RSV spike. Standard replenishment is days away. Ward issues an emergency alert recommending immediate transfers from nearby stores with excess inventory and a forward-buy trigger for the coming weeks of elevated demand.
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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