Ward runs pricing on your SAP pharmacy data
Your SAP data already carries the pricing data point. Ward reads it, attributes the driver, and attaches a recommended action.
Price Optimization for Pharmacy & Health, running on SAP
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.
What price optimization does: Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
What Ward does with that: Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
Key capabilities
- Real-time elasticity measurement
- Category-level price sensitivity
- Competitive price monitoring
- Margin-volume tradeoff modeling
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 Pricing matters for Pharmacy retail
Front-of-store pricing is a lever most pharmacy chains underuse. Vitamin shoppers compare prices carefully, but someone grabbing Band-Aids while waiting for a prescription has almost zero sensitivity. Ward maps elasticity by category and purchase context to identify margin opportunities that don't affect customer perception.
Why this combination
is its own problem.
Price Optimization needs POS transactions and inventory positions at minimum. SAP Retail carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
- 01 Rx-attached front-end purchases have near-zero price elasticity but get priced under the same rules as standalone front-end visits.
- 02 Cosmetics and beauty rely on perceived value tied to brand presentation; price changes that ignore the brand context can collapse category trust quickly.
Benchmarks. Pharmacy front-of-store gross margins range from 25% (commodity OTC) to 60%+ (cosmetics, fragrance, specialty wellness). Rx-attached front-end conversion runs 25-45%; the conversion-weighted margin per Rx customer is often 2-3x the standalone front-end visit.
What Ward has eyes on.
Ward reads POS transactions, inventory positions, purchase orders from SAP Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. SAP stays the system of record.
Price on elasticity you can measure. 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 scans continuously 20,000+ SKUs over 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 tracks Rx-to-OTC attachment pricing sensitivity, category-level elasticity by purchase context, competitive price gaps on high-awareness categories, and basket value impact from cross-category price changes.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to SAP Retail. Ward reads POS transactions, inventory positions, purchase orders 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 insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands back daily cards daily, each with what caused it and a recommended move. 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 SAP Retail
Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
Data Ward reads from SAP
Impact metrics with SAP
Data lake enrichment
Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates
Pharmacy KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. 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 continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.
Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Pharmacy-specific insight cards. No custom development required.
Ward tracks Rx-to-OTC attachment pricing sensitivity, category-level elasticity by purchase context, competitive price gaps on high-awareness categories, and basket value impact from cross-category price changes.
Ward segments front-of-store into sensitivity tiers: high (vitamins, pain relief, compared against Amazon), moderate (cosmetics, baby care), and low (first aid, greeting cards, seasonal). Ward recommends holding prices on high-sensitivity items while increasing low-sensitivity categories. Pilot stores show zero volume impact on adjusted items with measurable margin gains per Rx customer visit.
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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