PREDICT
Strategic Planning and Forecasting
Generate accurate demand forecasts and develop long-term plans for pharmaceutical capacity, inventory, and network investments under uncertainty.
Understanding the Problem
Pharmaceutical companies approaching $1.75 trillion in prescription drug sales face unprecedented complexity balancing supply chain resilience, regulatory compliance, and profitability. Strategic planning requires multi-horizon forecasting from short-term production to multi-year capacity investments, while managing $300+ billion in revenues expected to lose exclusivity. Companies must navigate geopolitical headwinds, pricing shifts, and technology evolution while ensuring uninterrupted supply of life-saving medications.
THE CHALLENGE
What Makes it Hard
Strategic planning in pharma involves generating demand forecasts and optimizing long-term decisions about capacity, inventory, and network investments across multiple time horizons while incorporating uncertainty in clinical trials, regulatory approvals, competitor actions, and market dynamics.
WHO FACES IT
Multi-horizon forecasting across vastly different time scales with long lead times (12-18 months) and perishable inventory
Network design decisions balancing contradictory objectives: minimizing CapEx while maintaining resilience, centralizing for efficiency while ensuring geographic diversification
Scenario analysis requiring evaluation of hundreds or thousands of potential futures, creating combinatorial explosion of analysis requirements
BUSINESS IMPACT
Reduce forecast errors by 50% (Novo Nordisk) and inventory waste by 20-30%: digital twin planning saved one biotech €30M and 3 years.
Forecast Error
50%
Reduction[1]
Novo Nordisk achieved 50% forecast error reduction through AI-driven forecasting systems (Syren Cloud).
Inventory Waste
20-30%
Reduction[2]
Decision intelligence systems deliver 20-30% inventory waste reduction with 5-15% service level improvements (Aera).
Strategic Planning
€30M
Savings[3]
French biotech saved €30M and 3 years through digital twin scenario planning, de-risking their clinical plan (InSilicoTrials).
How We Solve It
Multi-stage stochastic mixed-integer programming formulations model here-and-now decisions (facility construction, equipment purchases, contracts) alongside recourse decisions (production quantities, inventory, distribution flows) that adapt to realized demand and supply conditions. The objective minimizes expected total cost across scenarios including capital expenditure, operating costs, inventory holding, and shortage penalties, subject to production capacity, inventory balance, demand satisfaction, batch size, shelf-life, and regulatory constraints.
Hybrid Compute
What We Bring
Scenario generation representing uncertainty through scenario trees with information revelation over time
Multi-echelon network optimization with complex bill-of-materials structures and demand correlation
Stochastic programming with recourse for operational decisions adapting to realized conditions
Capital investment evaluation with quantified risk metrics across different future scenarios
FUTURE POSSIBILITIES
The
Quantum Horizon
Quantum computing for pharmaceutical strategic planning remains speculative in the near-term. Classical optimization methods (commercial MILP solvers, decomposition algorithms, simulation-optimization) are mature and highly effective for industrial-scale problems.
Exploratory Work
The most realistic path to quantum advantage involves hybrid algorithms applied to very large scenario-based problems, but requires further hardware development and algorithmic innovation. Organizations should focus investment on data infrastructure, advanced analytics capabilities, and organizational change management rather than quantum computing in the near to medium term (5-10 years). Even in optimistic scenarios, hybrid quantum-classical approaches will likely dominate rather than pure quantum solutions.
Current Research Directions
Quantum annealing for scenario-based stochastic optimization (D-Wave research on 15,000 scenario problems)
Quantum amplitude estimation for Monte Carlo acceleration in risk metric computation
Hybrid quantum-classical approaches for very large scenario sets where classical methods struggle
Interested in quantum research?
Explore proof-of-concept implementations with our team.
Ready to solve this problem?
Talk to our experts about how Strangeworks can help with strategic planning and forecasting.
SOURCES