Genentech: oncology and specialty therapy planning

Case study · Life sciences

Genentech deployed the RoadMap GPS Suite to unify short-term demand forecasts with long-range supply plans across oncology, neurology, and transplant anti-infective therapy portfolios, eliminating the planning disconnects that drove inventory imbalances and capital misallocation.

The challenge

Genentech manages demand across three high-complexity therapy areas, each with distinct forecasting dynamics. Oncology faces variable patient starts, biosimilar erosion, and indication expansions. Neurology requires tracking chronic patient populations with high persistency variability and managed care influence. Transplant anti-infectives carry event-driven demand, hospital procurement volatility, and tight shelf-life constraints.

The core problem was structural: monthly operational forecasts were built independently from multi-year financial and capacity plans. This fragmentation caused excess inventory in some SKUs, stockout risk in others, misaligned capital planning, and inconsistent revenue outlooks across the portfolio.

The solution

We implemented the RoadMap GPS Planning System in three phases. Phase 1 consolidated commercial, shipment, and demand signal inputs into a unified statistical baseline, surfacing management bias and override impact. Phase 2 reconciled near-term demand with the long-range financial plan, introduced scenario planning for oncology pipeline assets, and added dynamic forecasting bands for high-volatility SKUs. Phase 3 established ongoing governance through Forecast Value Added tracking, override stop-light highlighting, and a structured S&OP/IBP cadence.

• Advanced statistical forecasting: time series, seasonality, lifecycle, launch and loss-of-exclusivity curves
• Automatic roll-up from SKU to brand to portfolio to therapeutic area
• Scenario modeling for pipeline assets and LOE events
• Cross-functional governance: override visibility, FVA tracking, executive-ready reporting
• Consensus demand planning workflows across commercial, finance, and supply chain

Results

• Improved forecast alignment across all three therapeutic areas
• Reduced planning silos between commercial and supply chain teams
• Greater visibility into demand volatility drivers by SKU and portfolio
• Improved long-range revenue predictability for financial planning
• Reduced inventory imbalances through better demand signal integration
• More informed capital allocation decisions at the portfolio level
• Improved executive confidence in forecast outputs
• Stronger decision support for portfolio expansion planning

Key takeaways

• Separating short-term and long-range planning creates structural misalignment. Operational forecasts and strategic capacity plans must reconcile at a shared data layer.
• Pharmaceutical portfolios require therapy-specific modeling. A single statistical method cannot handle oncology launches, chronic neurology demand, and event-driven transplant procurement in the same way.
• Override governance is as important as the statistical model. Tracking FVA and flagging high-override SKUs systematically improves forecast quality over time.
• An IBP cadence anchored to a unified forecast reduces the cost of misalignment across commercial, supply chain, and finance teams.