Healthcare · Hospital inventory management
Banao builds AI-driven inventory systems that track medical supplies, consumables, and pharmaceuticals across wards, theatres, and storerooms in real time — eliminating the manual count cycle that leaves procurement flying blind.
The system predicts ward-level demand by procedure type and season, triggers purchase orders before a stockout occurs, and flags near-expiry items for reallocation before they become write-offs.
Inventory AI in healthcare is not a barcode scanner and a spreadsheet. It is demand forecasting, procurement automation, and clinical-supply alignment — built around how your wards actually consume stock.
The model reads procedure schedules, seasonal admission patterns, and historical consumption by ward and care type. Forecasts update daily and drive automatic reorder triggers before shelves run short.
RFID, barcode, or GS1 integration gives procurement and ward managers a live count across storerooms, satellites, and ward pantries — without a monthly physical count.
Items approaching expiry are surfaced automatically with reallocation suggestions — transfer to a higher-consumption ward, return to the supplier, or flag for clinical prioritisation — before they become a write-off.
When stock falls below a dynamically set par level, a draft PO is generated and routed for approval. No manual trigger, no weekend stockout because the order never went in.
Lead times, fill rates, and unit-cost trends by supplier — so procurement can negotiate from data rather than from memory and reduce dependency on single-supplier arrangements.
We integrate with your existing hospital information system, ERP, and pharmacy management software. The layer adds intelligence to what you have; it does not require a platform replacement.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Hummcare
Hummcare operates a multi-site care network where procurement happens across dispersed clinical teams. Banao added a forecasting layer that connects procedure schedules to storeroom counts, surfacing reorder needs before ward managers raise an emergency request.
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens our own engineering hires. Vikaas runs our own demand-generation pipeline. We do not recommend AI for a critical process — hospital supply included — unless it has survived our own operation first.
That means the inventory forecasting logic, the alert thresholds, and the integration patterns we bring to a hospital deployment have been tested against the failure modes that matter: missed demand spikes, stale data, and procurement workflows that break under pressure.
We have walked away from hospital inventory projects before committing to a build. Here is what has stopped us:
We do not quote a hospital inventory system from a demo. We audit your actual data, your sites, and your procurement workflow first.
We audit your HIS export, pharmacy dispensing records, and ward requisition logs. We model your top-cost SKU categories, test forecast accuracy against your last 12 months of actuals, and hand back a data-readiness report and ROI estimate — yours to keep regardless of next steps. If you proceed, the Sprint fee is credited against the build.
Forecasting model, real-time stock layer, PO automation, and HIS/ERP integration. Clinical and procurement teams sign off at each stage before we proceed to the next.
Deployment to wards and storerooms, training for procurement and clinical leads, and a 90-day monitoring period during which we tune par levels and alert thresholds against real demand.
What data sources does the system need to get started?
At minimum: HIS procedure and admission records, pharmacy dispensing logs, and storeroom issue records for the past 12 months. The Discovery Sprint assesses data completeness and identifies gaps that need to be closed before the model is reliable.
Does this require replacing our pharmacy or ERP system?
No. Banao's inventory layer sits on top of your existing pharmacy management system, HIS, and ERP. We read from and write to those systems via API or scheduled extract — we do not replace them.
How does the system handle emergency or unplanned surgical demand?
Unplanned demand cannot be forecast perfectly. The model builds a safety buffer by procedure type, and the alert layer flags sudden draw-down against that buffer in real time. For true emergencies, the procurement workflow is accelerated, not replaced.
Can it manage both pharmaceutical and non-pharmaceutical consumables?
Yes. The forecasting and stock-visibility layers apply across the SKU catalogue — sterile consumables, implants, linen, and equipment parts can all be tracked in the same system, with separate par-level logic and reorder rules per category.
How long does a typical hospital inventory AI deployment take?
The Discovery Sprint is two weeks. A full build across one or two sites typically runs 12–18 weeks, depending on integration complexity and the number of active SKUs. Multi-site rollouts are phased, starting with the site that has the cleanest data.
Tell us your highest-cost inventory failures from the last quarter. We will tell you whether AI forecasting is the right fix — and what your data needs to make it work.
Book a 45-min scoping call