SIMULATE

Risk Management and Assessment

Systematically identify, quantify, and mitigate risks across aerospace/defense and pharmaceutical supply chains, R&D portfolios, and manufacturing operations.

Understanding the Problem

Risk management and assessment is critical for aerospace/defense and pharmaceutical industries where failures result in catastrophic safety incidents, regulatory penalties, and massive financial losses. Both industries operate in highly regulated environments with complex supply chains, long development cycles, and stringent safety requirements. Single aerospace supply chain failures can cost $11+ billion annually, while 90% of pharmaceutical drug developments fail with Phase III trials costing $12-50M each.

THE CHALLENGE

What Makes it Hard

Organizations must assess and manage risks across multi-tier supply chains with limited visibility, navigate complex regulatory requirements with severe penalties, and optimize resource allocation across long-cycle programs vulnerable to changing conditions. Aerospace faces 65% workforce shortages and critical material dependencies on 100,000+ suppliers, while pharma battles 90% R&D failure rates across 123 identified risk dimensions.

WHO FACES IT

Risk managers and compliance officersSupply chain executives and procurement leadersR&D portfolio managers and program directorsQuality assurance and regulatory affairs teamsSafety engineers and operations managers

Complex supply networks with multi-tier supplier relationships and limited end-to-end visibility

High-stakes safety-critical applications where failures have catastrophic human and financial consequences

Massive datasets requiring sophisticated analysis for pattern recognition and risk identification

BUSINESS IMPACT

Address $11B+ in aerospace supply chain costs and $1-2B drug development investments: quantum-enhanced optimization improves Sharpe ratios 10-15% with 25% faster convergence.

Supply Chain Cost

$11B+

Aerospace 2025^[1]

Aerospace supply chain bottlenecks cost airlines $11B+ in 2025: fuel ($4.2B), maintenance ($3.1B), leasing ($2.6B) (IATA).

Quantum Improvement

10-15%

Sharpe Ratio Gain^[2]

QAOA demonstrates 10-15% Sharpe ratio improvement and 25% faster convergence vs classical optimization (IEEE).

Drug Development

$1-2B

Per Approved Drug^[3]

Drug development takes 10-15 years at $1-2B per approved drug, with only 10-15% advancing to approval (PMC/NIH).

How We Solve It

Implement quantitative risk assessment frameworks combining robust optimization (MILP, stochastic programming) for supply chain diversification and portfolio selection with quantum-enhanced machine learning for high-dimensional risk prediction. Quantum algorithms provide superior constraint handling for regulatory compliance problems and faster scenario analysis for stress testing, while digital twins enable risk-free validation of mitigation strategies.

Heterogeneous
Hybrid Compute

What We Bring

Robust MILP optimization for supply chain risk assessment and network design under uncertainty

Stochastic programming for R&D portfolio optimization across 123 risk dimensions

CVaR optimization for tail risk management in safety-critical applications

Quantum-enhanced pattern recognition for fraud detection and supply chain vulnerability analysis

FUTURE POSSIBILITIES

The
Quantum Horizon

Turkish bank Yapı Kredi used D-Wave quantum annealing to identify SME network failure points, exploring thousands of scenarios impractical for classical computing. Italian bank Intesa Sanpaolo's quantum ML fraud detection outperformed traditional methods analyzing hundreds of thousands of transactions. McKinsey estimates $400-600 billion potential value from quantum computing in financial risk management by 2035.

Exploratory Work

Quantum Risk Management Framework (QRMF) encompasses quantum amplitude estimation for VaR/CVaR, entanglement-based systemic risk analytics achieving 34.7% cascade reduction, and constraint-aware portfolio optimization with 23% drawdown improvements. The UK government committed £162M for quantum technology in crime and fraud prevention. Applications extend from financial risk to aerospace supplier network analysis and pharmaceutical clinical trial risk assessment.

Current Research Directions

D-Wave Constrained Quadratic Model (CQM) achieving 100% constraint satisfaction for complex regulatory requirements versus 82-96% for conventional methods

Variational quantum circuits (VQC) for fraud detection and credit risk assessment with higher accuracy using fewer features

Quantum amplitude estimation for VaR/CVaR calculations with potential quadratic speedup over classical Monte Carlo