VP Merchandising: pharmacy fill rate, read from Tableau
Ward ingests tableau Hyper extracts, underlying database (direct), published data source metadata from Tableau, tracks fill rate over 20,000+ pharmacy SKUs, and hands back the merchandising read each day.
Fill Rate Monitoring for Pharmacy on Tableau, scoped to merchandising
Here is fill rate monitoring in plain terms. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
In a Pharmacy & Health estate the job 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.
Your category managers are drowning in spreadsheets. Ward hands back findings scoped to merchandising decision-making.
How Ward delivers Fill Rate findings: Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Setup: Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.
What it does
- Estate-wide fill rate dashboard
- Threshold-based alerting
- Store-vs-estate benchmarking
- Category-level drill-down
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.
Ward ingests Tableau rather than replacing it. Tableau Hyper extracts, underlying database (direct), published data source metadata come across on a read-only connection, get enriched with contextual data, and come back as daily cards. Your BI is untouched.
Every empty shelf is a lost sale. 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.
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.
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 VP Merchandising in pharmacy retail owns numbers that move faster than the reporting cycle that covers them. Rx fill rate, OTC attach rate, expiry waste % shift store by store, every morning. A monthly pack cannot represent that. Your category managers are drowning in spreadsheets.
- 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 Tableau. Ward pulls from tableau Hyper extracts, underlying database (direct), published data source metadata and starts building baselines. First daily 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: operating rhythm
Findings 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 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
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
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 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.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Rx fill rate, OTC attach rate, Expiry waste %, tailored for Merchandising 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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