Automated Investigational Product (IP) Supply Planning Workflow

Manual translation of visit schedules, dosing, and randomization into kit specifications is a primary source of costly delays and waste. A custom workflow ingests the finalized protocol (e.g., from a Veeva Vault Protocol) and uses orchestrated agents to parse visit windows, treatment arms, and blinding rules, generating precise, machine-readable IP specifications for the labeling vendor and IVRS/IWRS. This eliminates weeks of back-and-forth between clinical, supply chain, and vendor teams.

Static, spreadsheet-based forecasting leads to overproduction (wasting millions in perishable IP) or underproduction (delaying patient enrollment). A custom workflow integrates protocol-driven demand with real-time enrollment data from the EDC and site activation status. Reinforcement learning agents model attrition rates and country-level rollout, dynamically adjusting production orders and triggering inter-depot transfers. This shifts IP planning from a fixed budget line to a variable, optimized cost center.

Manual IP reconciliation across sites, depots, and returns is a high-burden, audit-risk activity. The workflow automates this by creating a digital twin of the IP supply chain. Each kit is tracked via integration with the IVRS and logistics provider APIs. Browser agents automatically reconcile shipments against dispensing logs, flag discrepancies, and generate pre-populated accountability reports for monitors. This creates a continuous, defensible audit trail, reducing pre-inspection preparation by hundreds of hours.

Value is lost if the automation exists in a silo. The architecture is built to orchestrate actions across the fragmented clinical tech stack. Using tools like LangGraph, agents are designed to interact with IVRS/IWRS (e.g., Oracle Siebel CTMS), EDC systems, clinical supply management platforms, and ERP systems (SAP) via APIs or, where APIs are lacking, secure browser automation. This turns the workflow into the central nervous system for IP operations, not just another point tool.

The biggest cost in clinical trials is delay. This workflow provides a strategic lever by enabling 'what-if' analysis. Before protocol finalization, supply chain agents can simulate the operational and cost impact of different visit schedules, randomization ratios, or global rollout sequences. This allows study leadership to make design trade-offs with full visibility into supply risk and cost, turning the supply function into a proactive strategic partner in protocol feasibility.

Deploying this workflow doesn't require a big-bang replacement. A credible implementation uses a pilot protocol to build and validate the core orchestration engine (e.g., using LangChain or CrewAI), focusing first on automated specification generation. Subsequent phases add forecasting, then reconciliation, and finally full ERP integration. This phased approach delivers ROI within a single trial, de-risks the build, and creates a reusable asset for all future studies.

The workflow begins by ingesting the finalized protocol document to extract and structure all IP-related elements. A specialized agent parses visit schedules, dosing regimens, randomization plans (stratified, block), and kit configurations (blinded, open-label). This component transforms unstructured protocol text into a structured data model, eliminating manual data entry errors and establishing the single source of truth for all downstream supply calculations.

A forecasting agent uses the structured visit schedule, enrollment projections, and site activation timelines to simulate IP demand over the trial's lifecycle. It applies configurable buffer logic (e.g., for screen failures, lost kits, over-recruitment) and accounts for regional lead times and expiry dates. This component replaces error-prone spreadsheet models, generating a time-phased demand forecast that directly feeds into manufacturing and procurement systems.

This component automatically drafts the technical specifications for the Interactive Voice/Web Response System (IVRS/IWRS) based on the parsed randomization and kit design. It defines drug assignment rules, kit numbering logic, re-supply triggers, and emergency unblinding procedures. By generating system-ready specs, it eliminates weeks of manual back-and-forth between clinical, stats, and vendor teams, accelerating system build and UAT.

The core orchestration layer manages the end-to-end supply chain. It triggers purchase orders to CMOs, coordinates with central and local depots, and monitors shipment status. An exception-handling agent routes alerts for delays, temperature excursions, or site stock-outs, triggering predefined mitigation workflows (e.g., cross-shipment from another depot) and escalating only complex issues to human planners. This component ensures proactive supply continuity.

Post-distribution, this engine automates IP accountability. It reconcishes shipments against IVRS dispensing data and site-reported inventory, flagging discrepancies for investigation. It generates reconciliation reports for monitors and prepares destruction orders for expired or returned IP. This closed-loop component ensures regulatory compliance (21 CFR Part 312), reduces manual reconciliation labor, and creates a complete audit trail for regulatory inspection.

A centralized dashboard provides real-time visibility into global IP inventory levels, expiry horizons, and cost exposure. Crucially, it includes an amendment impact analyzer. When a protocol amendment is drafted, this component simulates the impact on existing IP stock, required re-kitting, and new manufacturing orders, providing a cost and timeline assessment before the amendment is finalized. This enables data-driven governance and prevents costly, reactive supply chain shocks.