Clinical Supply Chain Resilience

The Scenario

The alert. A Phase III clinical trial — AURORA-7, 30 sites across 10 countries — receives an optimization signal: EU Depot B is projected to hit investigational product safety stock threshold in 9 days. 42 patients across 8 European sites are at risk. 126 doses are in jeopardy. Financial exposure: $340,000.

Then the compounding factors detonate:

• Over-enrollment — Site 9 in Munich has 30 patients versus 24 planned (+25%), only 6 days of stock left
• Comparator delay — supplier reports a 10-day delay on the next batch
• Temperature excursion — shipment to Site 12 recorded 11.2°C for 47 minutes (spec: 2–8°C), 24 units on QA hold
• Labeling constraint — Depot C has stock but cannot label for the German market (5-day minimum turnaround)
• Storage failure — site coordinator in Prague texts that a freezer failure has cut capacity from 80 to 50 units

Evidence assembly — 14 systems, 90 minutes. Eight specialized agents collect 27 evidence records from IRT, CTMS, ERP, depot warehouse, carrier feeds, QMS, email, SMS, and photo OCR. An email from the Frankfurt depot manager reveals a labeling alternative: PharmLabel GmbH can turn German labels in 3 days instead of 5. A photo from Site 12 confirms lot expiry via OCR at 95% confidence. Every record is classified by certainty tier — Fact, Derived, Hypothesis, or Conjecture — so decision-makers know exactly how much to trust each signal.

Six recovery scenarios generated. The recommended combination — reroute 120 units through PharmLabel GmbH, pull back 36 units from Site 7’s oversupply, and release the excursion lot per stability data — delivers 96% patient-dose protection with 60 units of waste avoided.

Five human approval gates clear in 2 hours:

• Supply Lead — authorizes inter-depot transfer
• Clinical Ops Lead — confirms patient dosing protection plan
• QA Disposition Owner — releases excursion lot citing stability report MRD-STAB-2024-003
• Depot Coordinator — books ColdChain Express with temperature monitoring
• Country Operations Reviewer — approves PharmLabel GmbH, initiates customs pre-clearance

Execution. 3 shipments, 6 inventory moves, 4 site communications, and 10 tracking checkpoints dispatched through May 30.

Trust receipt. 27 records cited, 5 SOPs referenced, 5 approvals granted, 3 assumptions documented, 2 rejected alternatives preserved with rationale, 4 residual risks identified. Audit-ready from the moment of closure.

Projected impact: 126 doses protected across 42 patients, $285,000 in trial delay costs avoided, 60 units of waste prevented. Confidence: 89%.

The Problem

Evidence is everywhere — and nowhere. When an optimization engine flags a projected shortage, the real work begins — and it doesn’t happen in one system. Evidence is scattered across IRT, CTMS, ERP, depot warehouse systems, carrier feeds, QMS, email inboxes, and SMS threads. A depot manager’s email reveals a labeling bottleneck. A site coordinator’s text message reports a freezer failure. A photo from the field confirms a lot expiry date.

Approvals are informal. Decisions are undocumented. The supply team must gather this evidence, evaluate recovery scenarios, coordinate approvals across clinical ops, QA, logistics, regulatory, and country operations — and execute with full audit traceability. Today, this work lives in spreadsheets, email chains, and conference calls. When the disruption is resolved, the institutional knowledge of how it was resolved evaporates — until the next disruption, when the same scramble begins from zero.

OpsIQ is not a supply optimization engine. Optimization engines like N-SIDE tell you what the math says. OpsIQ is the operational recovery layer that sits above optimization — it gathers evidence, coordinates the humans, explains the options, executes the approved plan, and preserves an audit-ready trace of every decision.

Who This Is For

Clinical supply leaders, clinical operations executives, QA disposition owners, and regulatory/country operations reviewers in pharma and medtech who need clinical trial supply recovery that is evidence-backed, multi-stakeholder governed, and audit-ready.

What You’ll Experience

• 7-Stage Guided Recovery — alert intake with risk classification, impact analysis across patients/sites/countries, parallel evidence gathering from 14 systems, multi-scenario recovery options with trade-off scoring, human approval coordination with evidence packets, execution planning with shipment instructions and tracking checkpoints, and trust receipt generation
• 8 Specialized Agent Types — each with defined authority levels and guardrails:
  • Information Collection (autonomous) — polls 14 structured systems, classifies evidence by certainty tier
  • Clinical Study Leader Outreach (supervised) — templated email/SMS/call escalation to site coordinators
  • Unstructured Knowledge Extraction (supervised) — extracts actionable intelligence from forwarded emails, SMS, and uploaded photos via OCR
  • Signal & Matching (autonomous) — detects enrollment-demand mismatches, shipment delays, excursion rate trends
  • Recommendation (human-required) — generates evidence-backed recovery options with cited evidence and documented rejected alternatives
  • Execution (human-required) — operates only within approved scope, generates audit trail for every action
  • Learning (supervised) — compares predictions to outcomes, identifies systematic biases
  • Strategy (human-required) — recommends structural changes based on accumulated learning
• Evidence Classification (IBL Certainty Tiers) — Fact (~98% confidence), Derived (~87%), Hypothesis (~65%), Conjecture (~50%) — so decision-makers know exactly how much to trust each signal
• Multi-Scenario Recovery Options — recovery paths scored against patient protection, waste avoidance, execution timeline, regulatory risk, and operational confidence — with recommended combinations and documented rejected alternatives
• 5 Human Approval Gates — supply lead, clinical ops lead, QA disposition owner, depot coordinator, and country operations reviewer — each with evidence packets, policy grounding, and downstream actions unlocked upon approval
• Knowledge & Operations Fabric — dynamic graph of the study universe: 68 nodes (depots, countries, sites, products, suppliers, lots, lanes, disruption events) connected by 42 edges with real-time alert mapping
• Trust Receipts — audit-ready recovery packages with evidence trail, SOP grounding, human approvals with timestamps and rationale, assumptions documented, rejected alternatives preserved, residual risks identified, and projected impact quantified
• Natural Language Copilot — evidence-backed reasoning: “If Site 9 runs short, when should we shift stock from Site 7 given transport delays?”, “Which countries are most exposed if Depot B misses release?”, “Show the fastest plan that does not use product under excursion review”
• Command Center Dashboard — executive metrics (patient-doses protected, waste exposure, trial days at risk, recovery confidence), agent activity feed, and leading indicators (enrollment variance, shipment delay trends, excursion rates, IRT alert volume)
• Projected vs. Actual Learnings — 10 metrics comparing forecasts to outcomes, surfacing where the system over- or under-predicted and feeding continuous improvement