Shrinkage Detection for Pharmacy on Tableau, built for merchandising
Ward ingests tableau Hyper extracts, underlying database (direct), published data source metadata from Tableau, watches shrinkage across 20,000+ pharmacy SKUs, and hands back the merchandising read each day.
Shrinkage Detection for Pharmacy on Tableau, scoped to merchandising
In a Pharmacy & Health fleet the job 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.
Your category managers are drowning in spreadsheets. Ward hands back findings scoped to merchandising decision-making.
Shrinkage Detection, in one sentence. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
Under the hood. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
The connection itself. Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.
What it does
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
- Cause-level shrinkage attribution
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 Shrinkage matters for Pharmacy retail
Regulated inventory has compliance-driven tracking, but front-of-store categories like cosmetics, vitamins, and baby care are among the most shoplifted in retail. Ward monitors front-of-store shrinkage patterns, flagging anomalous loss rates and identifying which departments and time windows drive the variance.
Why this combination
is its own problem.
A generic shrinkage model applied to a pharmacy footprint 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 Premium skincare and cosmetics are often the highest dollar-shrink categories per square foot, but routine LP focus stays on tobacco and OTC pain relief because of historical theft data.
- 02 Shift-change windows are predictable theft opportunities; chain LP rarely correlates loss timing with staffing rotations.
Benchmarks. Pharmacy front-of-store shrink runs 1.5-3.5% with premium skincare, fragrance, and OTC pain relief among the worst offenders. ORC events typically concentrate 3-7 SKUs per store and cause 30-60% of category-level shrink in affected stores.
What Ward has eyes on.
Ward watches 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.
The connection to Tableau is read-only and runs on your schedule. Ward ingests tableau Hyper extracts, underlying database (direct), and the rest of the feed, then stitches together it with external context your BI does not carry: weather, local events, competitor pricing.
Find the leak before it drains you. 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.
At the metric level. Ward tracks front-of-store loss rates by category, time-of-day concentration patterns, high-risk SKU identification, and receiving discrepancy rates, correlating shrinkage with staffing levels and store layout.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to Tableau with read-only credentials and begins ingesting tableau Hyper extracts and underlying database (direct). No config changes on your side. First cards arrive inside the first 48 hours.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward hands you findings on a daily cycle, each with the driver and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many cards arrive, it is how many get acted on.
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 identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. 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 compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
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 tracks front-of-store loss rates by category, time-of-day concentration patterns, high-risk SKU identification, and receiving discrepancy rates, correlating shrinkage with staffing levels and store layout.
Ward identifies a cluster of stores with premium skincare shrinkage rates far above the estate average. The loss concentrates on the same resalable SKUs during an afternoon shift-change window when the cosmetics counter is briefly unstaffed. Ward recommends targeted staffing coverage during the transition and case-locking the highest-theft SKUs, producing a significant shrinkage reduction within weeks.
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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