Automation Workflow for Calculating and Maximizing Return on R&D Investment (ROI)

This workflow automates the business intelligence layer of drug discovery, directly linking scientific progress to financial outcomes. It ingests data from ELNs, project management, and market intelligence systems—including development stage, probability of success (PoS) estimates, projected costs, and market size—to run Monte Carlo simulations. The system calculates the expected net present value (eNPV) and risk-adjusted return for each candidate and portfolio strategy, enabling R&D leadership to kill underperforming projects earlier and reallocate resources to maximize portfolio value.

Implementation integrates with systems like Veeva, Planisware, and custom data lakes, using orchestration frameworks like LangGraph to manage the simulation pipeline. The architecture includes approval gates at the portfolio committee level, with dashboards built in Tableau or Power BI. Controls ensure data lineage for auditability, and the system is designed for incremental rollout, starting with a single therapeutic area. This turns portfolio management from a quarterly spreadsheet exercise into a continuous, simulation-driven operating model.

This orchestration workflow automates the critical bottleneck of translating scientific progress into financial projections, replacing manual, spreadsheet-based portfolio reviews. It ingests structured data from ELNs, clinical systems, and market databases to calculate Net Present Value (NPV) and risk-adjusted return for every candidate. The system identifies underperforming projects for deprioritization and simulates portfolio scenarios, enabling R&D leadership to allocate capital toward programs with the highest expected value, directly maximizing the economic return of the discovery portfolio.

Implementation integrates with systems like Veeva, SAP, and Cortellis for live data. The orchestrator, built on LangGraph, manages state and exception routing between specialized agents. A governance layer requires human approval for major portfolio shifts, with all simulations and rationale logged for audit. This creates a repeatable, data-driven operating model for portfolio management that reduces subjective debate and surfaces financial tradeoffs weeks earlier.

This workflow automates the business intelligence layer of discovery, ingesting data on development costs, probability of success (PoS) estimates, market size, and competitive intelligence to run Monte Carlo simulations. It replaces manual, spreadsheet-based portfolio reviews with a dynamic system that continuously re-evaluates the projected ROI of pipeline candidates. The operational upside comes from enabling R&D leadership to kill underperforming projects earlier, reallocate resources to higher-value programs, and maximize the economic value of the discovery portfolio, directly linking scientific progress to financial outcomes.

Implementation follows a phased delivery: Phase 1 integrates cost and PoS data from internal systems like electronic lab notebooks (ELNs) and project management tools. Phase 2 builds the simulation engine and connects to external market data APIs. Phase 3 implements the governance layer with approval gates in systems like Jira or ServiceNow and dashboards in Power BI or Tableau. Critical controls include exception routing for high-risk portfolio changes, audit trails for all financial assumptions, and scheduled retraining of predictive models as new clinical data arrives to ensure ROI projections remain accurate.

This automation directly addresses the high-stakes governance challenge of allocating billions in R&D spend. It replaces fragmented spreadsheet models and manual data aggregation with a continuous, auditable pipeline. By ingesting live data from ELNs, clinical systems, and market intelligence platforms, it calculates Net Present Value and risk-adjusted ROI for every candidate, flagging projects that fall below portfolio thresholds. The system automates the repetitive work of financial modeling and data synthesis, creating operational upside through faster, more objective portfolio decisions.

Implementation requires integrating with source systems like Veeva RIM, SAP for costs, and Cortellis for competitive intelligence. The orchestration layer, built on LangGraph, sequences data validation, simulation runs, and threshold checks. Critical controls include human-in-the-loop review gates for marginal projects, model explainability reports for auditability, and phased rollout starting with a single therapeutic area. This architecture shifts portfolio management from a quarterly review to a continuous, data-driven operating model, directly linking scientific progress to financial outcomes.