Averaging last year's demand was costing them $19M a year.
Placing Consigned Inventory Against Modelled Demand
“We know roughly what each site uses, so we stock to the average. Then half our locations run short while the other half sits on stock nobody will ever use.”
A US orthopedic bracing and mobility device manufacturer distributed consigned inventory across a national hospital network. Every location had the right average. Almost none of them had the right stock. Half the locations ran short while the other half sat on stock nobody would ever use. The affected department carried 20% of total revenue.
DATA4AI consolidated consumption, billing, transfer, and location records into a single view, then ran statistical testing across seasonality, location, SKU value, and demand variability. Monte Carlo methods and supply chain simulations produced SKU-level, location-level placement models. We piloted the model in Colorado, validated the outcomes, and rolled it out across the national US network.
- $19M annual loss identified in the stock-and-bill operation
- $4M returned to profit within six months of rollout
- 20% of total revenue affected, now placed against modelled demand
- Colorado pilot to national rollout inside two quarters
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Detailed methodology, results, and implementation notes as a PDF.
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