Your supply chain already
knows what it needs.
Now it can say it out loud — 72 hours before the requisition hits.
Forecast reads every saline bag pulled from a crash cart, every surgical staple restocked at 3 a.m., and tells you exactly what each department will need before the shortage becomes a crisis.
Stockout events prevented across active deployments
Emergency purchase orders eliminated this quarter
Recovered annually per health system from waste reduction
72-hour forecast accuracy across all item categories
What manual forecasting costs
while you read this page.
Lost to waste, stockouts & emergency freight — 850-bed system
since you opened this page · $1.29 / second
Annual exposure
$4,06,30,000
$47,800 per bed per year
Three approaches.
One clear outcome.
Every row is a decision your supply chain makes daily. Winning cells show where Forecast eliminates the gap.
| Metric | ManualSpreadsheet Forecasting | Legacy ERPMMIS / EHR Modules | Forecast ML Engine · 72h Ahead |
|---|---|---|---|
Forecast Accuracy 72-hour prediction window vs. actual consumption | 52–61% Static PAR levels | 71–78% Lagged ERP data | 97.3% ML + real-time consumption |
Stockout Reduction Reduction from baseline frequency | 0% Reactive only | –28% Rule-based alerts | –95% Predictive prevention |
Integration Speed Time to first live forecast post-deployment | N/A No integration | 6–18 months Custom dev required | 14 days Pre-built EHR/ERP connectors |
Lead Time Reduction Average days eliminated from replenishment cycle | 0 days No prediction | 1–2 days Basic reorder points | 72 hours Orders before requisition |
Expired Inventory Write-off reduction from baseline | Baseline $5.6M avg annual | –20% Some visibility | –87.6% Real-time expiry alerts |
Total Cost of Ownership Annual platform cost per 500-bed system | $0 licensing High hidden labor cost | $500K–$1M+ Plus $1M+ implementation | ROI positive In under 4 months |
Pharmacy Formulary Adaptive response to demand changepoints | Manual review 25.9% of top drugs affected | Quarterly updates Slow to adapt | Continuous ML Detects shifts in hours |
Data sourced from peer-reviewed deployments and CSMP research. Results vary by system size and integration depth.
The systems that moved first
are already compounding.
Every hospital below was manually forecasting twelve months ago. They are not anymore.
Midwest Regional Medical Center
Chicago, IL
Stockout frequency
Midwest Regional Medical Center
Stockout frequency
Pacific Coast Health System
Emergency freight spend
Southeastern University Hospital
Expired inventory write-off
Great Lakes Memorial
Manual audit hours/week
Mountain West Health Partners
Forecast accuracy
Northeast Academic Medical Center
OR supply waste
Gulf Coast Regional Hospital
Backorder resolution time
Capital Health Network
Formulary gap events
Midwest Regional Medical Center
Stockout frequency
Pacific Coast Health System
Emergency freight spend
Southeastern University Hospital
Expired inventory write-off
Great Lakes Memorial
Manual audit hours/week
Mountain West Health Partners
Forecast accuracy
Northeast Academic Medical Center
OR supply waste
Gulf Coast Regional Hospital
Backorder resolution time
Capital Health Network
Formulary gap events
Your supply chain already
knows what it needs.
Now it can say it out loud.
The demo sandbox loads pre-anonymized hospital data — real consumption patterns, real stockout events, real savings calculations. No setup. No sales call. No waiting.
HIPAA-compliant · SOC 2 Type II · No PHI in demo environment