A discovery brief from Enmovil

The signal is live. The batch schedule isn't keeping up.

A species spikes. The compounding batch can't respond fast enough. The patient sees a stockout.

Blue Rabbit reads 70,000+ vet prescription signals in near-real-time. The gap: when a species or therapeutic category spikes, the compounding batch schedule doesn't respond fast enough. Demand signal decays before it shapes a plan. The patient sees the stockout.

Source: Wedgewood's Blue Rabbit acquisition (June 2023) extended prescription signal coverage to 70,000+ vet clients.

Signal vs. plan

The gap.

Blue Rabbit reads the signal. The question is whether the planning engine closes the loop fast enough.

Hypothesis: the constraint isn't signal, it's loop-closure tempo. Blue Rabbit generates near-real-time prescription demand across 70,000+ vet clients. If the compounding batch schedule refreshes weekly or monthly, signal decays before it shapes a plan. The gap shows up as raw API overhang, batch underutilization, or service misses on chronic-condition compounds.
EDP (Demand Planning)

% of compounded prescriptions shipped within commitment window at species-and-SKU forecast accuracy > 90%, measured weekly per species, per therapeutic category.

Numerator: prescriptions shipped on time with compound on hand at forecast accuracy.
Denominator: prescriptions ordered that week.

If this number slips, raw-API inventory climbs or service slips. The scaling thesis lives here.

70,000+
Vet clients on Wedgewood + Blue Rabbit.
Daily
Blue Rabbit signal cadence.
7.6%
Compounding pharmacy market CAGR, 2026 to 2035.
503B
FDA-Registered Outsourcing Facility status.

A hypothesis, not a characterization. We'd love to test it against your measured state.

One platform, every decision layer

What Enmovil is.

The intelligence layer that sits above SAP, not in place of it.

PREDICT

Available for expansion

  • Forecast & Demand Intelligence
  • Inventory Planning & Optimisation
  • Scenario Modelling
  • Warehouse & Node Operations

PLAN

Best-fit wedge for this conversation

  • Demand Planning at SKU-species granularity
  • Sales & Operations Planning (S&OP)
  • Raw-material + Capacity Planning
  • Blue Rabbit signal to batch schedule loop

EXECUTE

Available for expansion

  • Multimodal Orchestration
  • Control Tower & Resilience
  • Smart RFQ / Bidding
  • ePOD & Freight Settlement
CADDIE AI: the cross-suite intelligence layer spanning Predict, Plan, and Execute. Conversational. Agentic. Sits above SAP S/4HANA and your pharmacy ERP, reclaims planner hours that long-tail demand consumes.
25+ Fortune 100 customers · 150+ team · 10+ years · ISO 27001 · SOC 2 Type II · SAP-ready · AI-native
The stack view

CADDIE AI above SAP, not in place of it.

The cross-suite intelligence layer planners and ops leads actually query.

CADDIE AI · conversational · agentic · cross-suite
Predict
Plan
Execute
SAP S/4HANA · pharmacy ERP · Blue Rabbit prescription platform · 503B compliance stack
Vet clinic · Compounding site · Distributor · Patient signals, real-time
No rip and replace. Blue Rabbit stays. The ERP stays. CADDIE AI queries across the pillars in your language: "show me every species-chronic compound where next week's demand signal breaks the standing batch plan, and the API reorder needed."
Proof-by-geometry

How we've helped a peer.

A Fortune 500 SAP-native manufacturer. Long-tail demand. Planning engine rebuild. Selected over SAP, Kinaxis, EY, Blue Yonder.

Before

40-minute reports. 16-hour overnight forecast.

  • Planning reports took 40 minutes per query.
  • Forecast jobs ran 16 hours overnight, refreshed once daily.
  • Signal-to-plan lag measured in days.

After

90-minute SAP-to-decision loop.

  • Reports down to 5 to 6 minutes, writeback-ready.
  • Forecast jobs retired. Agentic planning on demand.
  • Single Command Hub. One KPI view across the network.

90 min

SAP extract to decision to writeback.

6x faster

Reports 40 min to 5 to 6 min.

16 hrs

Overnight forecast job retired.

Selected

Over SAP, Kinaxis, EY, Blue Yonder.

Different product category. The geometry translates. Percentages we'd validate together.
For the working session

Questions we'd bring into the room.

Specific. If one lands wrong, we want to know.

Loop tempo

  • A Blue Rabbit spike hits Tuesday morning, an equine chronic shortage SKU. Hours to a batch decision? Days? Next weekly cycle?
  • Which species-therapeutic category has the widest gap between signal refresh and batch plan refresh? Who owns closing that gap today?

Where the signal degrades

  • Of the prescription volume through Blue Rabbit, what share drives a batch decision directly versus rolling into a monthly S&OP aggregate that smooths it out?
  • Where between vet order and compounding batch does species-level granularity collapse? Data handoff or planning model?

Stockouts and fill rate

  • When a chronic-condition compound goes short, how fast does planning know? How many batch cycles to recover fill rate?
  • Which metric is furthest from target: forecast accuracy, fill rate, inventory turns, or service rate on shortage SKUs?

Forward

  • If Blue Rabbit drove species-level batch decisions daily this quarter, what's the first P&L line that moves? Who notices first?
Who's building this

Enmovil · the technology thought partner for autonomous supply chains.

One AI-native platform. Demand sensing, inventory, dispatch, execution, freight settlement, sustainability reporting. All on one intelligence layer that sits above the systems you already own.

Under orchestration
~100K
Trucks per day across the customer base
Daily ETA accuracy
99%
Road, rail, ocean, air
Forecast accuracy
97%
Demand sensing on deployed customers
Logistics cost savings
8 to 15%
Measured across deployments
Integration
3 to 4wks
On existing SAP, Oracle, TMS, WMS. No migration.
Deployed at
Dispatch Planning
Multimodal Logistics
Multimodal Orchestration
Dispatch Planning
Fleet Management
Logistics Orchestration
Logistics Resilience
Inventory Management
Freight Settlement
Dispatch Planning
Transport Management
Export Planning
Runs under GDPR and SOC 2. Data ingestion via API, EDI, or bulk upload. Enterprise SSO. Deploys over existing SAP ECC 6.0 and above.
What changes

Blue Rabbit drives species-level batch decisions daily. Raw API holds at signal-appropriate levels. A species spikes, the batch responds, the patient gets the compound on time.

The ask: 60 to 90 minutes with Demand Planning and whoever owns the Blue Rabbit handoff.

01

A loop-closure read

Your signal-to-batch tempo mapped against daily loop closure on a comparable long-tail platform. You tell us where we're wrong.

02

A stack fit test

Where agentic planning sits relative to your ERP, Blue Rabbit, and 503B stack. No rip and replace, just the intelligence layer above.

03

A loop-closure hypothesis

A one-pager naming the species segment and the metric daily closure would move first: fill rate, forecast accuracy, or inventory turns.

Three questions we'd ask before the session

Where would the first visible improvement show up if the loop closed daily?

What's the sharpest planning pain the 70K scale creates right now?

Who needs to be in the room?

Predict.Plan.Execute.
Not a pitch. A working session.globalsales@enmovil.inenmovil.ai

CONFIDENTIAL · Enmovil × Wedgewood Pharmacy Demand Planning discovery. Prepared for the American Supply Chain Summit 2026, Dallas. Third-party trademarks belong to their respective owners. Not affiliated with or endorsed by Wedgewood Pharmacy or Covetrus.