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Preventing Stockouts on Fast-Moving Pharmaceutical SKUs
A pharmaceutical distributor supplying 900 pharmacies holds inventory against a forecast maintained in spreadsheets and updated monthly.
Runs onSanchayHow the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Fast-moving SKUs stock out between forecast refreshes, sending pharmacies to competing distributors.
- Safety stock is set by habit at a flat percentage rather than by demand variability per SKU.
- Nobody measures forecast accuracy, so the forecast is never corrected.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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1
Per-SKU Forecasting
Demand is projected per SKU using its own history and seasonality rather than a category average.
Input Signal:
Real-time operational telemetry & queue
Reasoning Pattern:
MCP grounded vector inference
Governance Gate:
Policy constrained with audit write-back
Verified Business Outcomes
- Stockouts on fast-moving SKUs caught before they occur rather than after.
- Safety stock sized per SKU rather than by a single blanket percentage.
- Forecast accuracy measured and improving rather than assumed.
Capabilities this relies on
- forecasting
- system connectors
- scenario simulation
- alert routing
- anomaly detection
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