Network Operations Command Center
One operating truth — unified from LIMS, inventory & logistics across both reference labs, satellites and collection centers. Nothing is replaced; Flux AI sits on top.
⚡ Predicted Actions ranked by ₹ impact · 92% avg confidence
🏥 Network Status days-of-cover
Network demand · last 30 days
Reagent & Consumable Demand Forecast
Per test × analyzer × lab. Learns seasonality, day-of-week and campaign spikes. Solid = actual · dashed = forecast · band = confidence interval.
Driven by daily LIMS test demand (captured continuously) and reconciled to month-end closing stock — no real-time consumption feed required.
SKU forecast
Inventory & Expiry — FEFO Control
Two failure modes, one screen. Stockout risk Expiry risk Healthy
Network redistribution suggested — save ₹2.1L + avert a stockout
Elecsys Vitamin D 25-OH: 220 tests' worth expiring unused at Delhi NCR in 18 days, while Mumbai is forecast to stock out in 11 days. Move 1 lot Delhi NCR → Mumbai instead of scrapping one and air-freighting the other. Net: zero wastage, zero stockout, no emergency PO.
Approve redistribution →Reagent inventory · all labs A-class SKUs
| Reagent / Kit | Analyzer | Lab | On hand | Days cover | Next expiry | Status |
|---|---|---|---|---|---|---|
| Vitamin D 25-OH | Roche Elecsys | Mumbai | 3 kits | 11 | 4 Sep 2026 | Stockout 11d |
| Vitamin D 25-OH | Roche Elecsys | Delhi NCR | 9 kits | 41 | 4 Jul 2026 | Expiry 18d |
| CELLPACK DCL | Sysmex XN | Mumbai | 6 packs | 16 | 8 Jul 2026 | Expiry 22d |
| Dengue NS1 Ag | ELISA | Jaipur | 120 tests | 6 | 2 Dec 2026 | Surge — reorder |
| HbA1c cartridge | Bio-Rad D-100 | Mumbai | 14 boxes | 19 | 1 Nov 2026 | Healthy |
| TSH (3rd gen) | Roche Elecsys | Delhi NCR | 7 kits | 13 | 20 Oct 2026 | Watch |
| BacT/ALERT bottles | bioMérieux | Mumbai | 240 btl | 24 | 15 Jan 2027 | Healthy |
| IHC antibody panel | Ventana | Mumbai | 5 vials | 9 | 30 Aug 2026 | Stockout 9d |
Cold Chain & Specimen Integrity — the inbound specimen flow
Specimens move inbound from dozens of cities to the reference labs under 2–8 °C and a clock. Hemolysis is the #1 cause of rejection — Flux AI flags at-risk consignments in transit, before the analyzer does.
🌡️ Consignment NSK-114 — temperature trace Nashik → Mumbai
🚚 In-transit consignments live
| Route | ETA | Temp | Rejection risk |
|---|---|---|---|
| Nashik → Mumbai (NSK-114) | 32 min | 9.0°C | High 73% |
| Jaipur → Delhi NCR | 1h 10m | 5.2°C | Low 4% |
| Ahmedabad → Mumbai | 2h 40m | 4.6°C | Low 3% |
| Pune → Mumbai | 48 min | 7.8°C | Watch 21% |
| Surat → Mumbai | 3h 05m | 6.1°C | Low 6% |
AI Assistant — ask your data in plain language
A private, on-prem GenAI assistant on top of the same unified layer — LIMS, inventory, procurement, logistics & reports. No dashboards to learn, no SQL. Ask in English (or Hindi). Nothing leaves the client's infrastructure.
💬 Flux Assistant private · on-prem LLM · role-based access
🧠 GenAI capabilities on the same platform
Demo responses are scripted on representative client data. In a deployment the assistant is grounded on the client's own systems with retrieval + citations, so answers are traceable to source records.
How we validate — & the value
Nobody trusts the AI on day one. It earns trust on the client's data, in shadow mode, against success metrics the client sets.
✅ Validation pipeline
Backtest on history
Train on 18–24 months; test on the most recent 3 months it never saw. Report MAPE, forecast bias, and stockout-catch rate per A-class SKU.
Shadow mode (parallel run)
6–8 weeks running alongside today's process, changing nothing. Planners see "model said X, reality was Y" weekly. Zero operational risk.
Champion / challenger
Switch a subset of SKUs / one lab to AI-recommended ordering vs. the old method. Success metrics agreed up front. Only SKUs that beat baseline graduate.
Human-in-the-loop
System proposes, the team disposes. Every recommendation approvable/overridable; overrides logged and learned from. On-prem, read-only, fully explainable — NABL-friendly.
📊 Value delivered representative engagement
(≈10% → ≈5%)
on key SKUs
tied in inventory
(MAPE, A-class)
/ re-draw rate
breaches cut
Figures from a representative diagnostics-network engagement, benchmarked against published industry data (reagents ≈50% of lab opex; ~10% commodity wastage; hemolysis = leading rejection cause).
How a typical engagement starts — a 4–6 week, on-premise PoC
One use case, one success metric agreed up front, deployed on the client's own servers. Lowest data lift, fastest measurable ₹ — and the natural foundation for every other module.
📅 The 4–6 week proof of concept
Deploy Flux AI on-prem. Read-only connectors to LIMS + inventory. Pick 2–3 high-value reagent categories and agree the success metric.
Train on 18–24 months of history; test on unseen months. Run alongside today's process — predicting, changing nothing. Report MAPE, bias, stockout-catch.
Switch the chosen SKUs to AI-recommended ordering vs. the old method, like-for-like. Measure against the agreed metric, human-in-the-loop throughout.
Results readout vs. baseline — wastage, stockouts, accuracy, ₹ impact. Clear go / no-go and a roadmap to expand modules & labs.
🎯 Scope
- One use case: reagent demand forecasting + expiry control
- 2–3 high-value reagent categories (e.g. immunoassay, hematology)
- One reference lab (add the second for the network-balancing story)
- One agreed success metric — e.g. cut wastage X% / catch ≥Y% of stockouts
🔌 What we need
- Read-only access to LIMS + inventory/ERP
- 18–24 months of historical consumption & orders
- Batch/expiry & vendor lead-time data
- A named operations sponsor + ~2 hrs/week of a planner's time
✅ What you get
- Flux AI deployed on your infrastructure — data never leaves
- Measured accuracy & ₹-impact, validated on your data
- No disruption — sits on existing systems, workflows unchanged
- No-regret exit: if it doesn't beat your process on the metric, nothing is lost
Why start here
Demand forecasting + expiry control needs the least data, carries zero operational risk (on-prem, read-only, shadow-first), and returns the fastest, hardest-to-argue rupee value — turning wastage straight into margin. Once it's proven on your numbers, the cold-chain, network-balancing and GenAI-assistant modules build on the exact same unified data layer.
Typical next step: scoping workshop →Illustrative mock-up populated with representative client data. In a deployment, Flux AI runs on the client's own infrastructure and is trained on the client's own data. · Trinesis Technologies · Flux AI