How We Approach Problems
Three steps. Understand the system, model it, then optimize.
Understand
Map your operation: assets, workflows, constraints, and decision points. Find the real problem, not the symptom. Define what success looks like.
Model
Pull together operational and market data. Build a simulation model of your system. Test it against historical performance. Run stress tests and scenario analyses.
Optimize
Surface insights you can act on. Recommend improvements with clear confidence ranges. Support your team through implementation. Update the model as conditions change.
What We Build
Six types of experiential simulations, each tailored to a specific operational challenge.
Supply Chain Simulation
Model logistics networks, find bottlenecks, test disruption scenarios. For lithium, minerals, food systems.
Outcome: 15 to 30 percent cost reduction
Asset Operations Simulation
Model production lines and equipment, optimize maintenance, increase throughput. For manufacturing, oil and gas, facilities.
Outcome: 20 to 35 percent efficiency gain
Extraction and Yield Simulation
Model extraction processes, balance yield against environmental impact. For lithium, oil and gas, agriculture.
Outcome: 10 to 25 percent yield increase
Network and Infrastructure Simulation
Model multi-facility coordination, distribution, load balancing. For automotive, aeronautics, energy.
Outcome: 15 to 40 percent distribution cost reduction
Portfolio and Risk Simulation
Cross-system risk modeling, scenario stress-testing, capital allocation. For large operators across all verticals.
Outcome: Better allocation decisions, lower risk exposure
Value Chain Simulation
End-to-end integration from raw material to customer. For real estate, food systems, automotive.
Outcome: 20 to 50 percent margin improvement
Industries We Serve
Proven results across hard-to-optimize industrial domains.
Lithium and Critical Minerals
Mine-to-market modeling
Oil and Gas
Extraction and logistics
Manufacturing
Production and throughput
Aeronautics and Aerospace
Supply chain and program risk
Automotive and Mobility
EV transition and supply
Real Estate and Built Environment
Development and value
Agriculture and Food Systems
Crop-to-market pathways
From Problem to Insight
How we turn operational complexity into clear decisions.
| Your Challenge | Root Cause | What We Model | Your Outcome |
|---|---|---|---|
| High operating costs, unclear drivers | Disconnected data, black-box processes | System integration, sensitivity analysis | 20 to 35 percent cost reduction |
| Supply disruption risk | No visibility into tail scenarios | Stress-test model, scenario analysis | Resilience plan with contingencies |
| Slow decision-making | Spreadsheets, no scenario speed | Interactive simulation dashboard | Days instead of weeks |
| Poor capital allocation ROI | Isolated assumptions, missing dependencies | System-wide optimization | 15 to 40 percent better returns |
| Regulatory and ESG uncertainty | Ad hoc compliance, no integration | Scenario modeling with compliance workflow | Credible, auditable plans |
| Market and demand risk | Linear forecasts, single-point estimates | Probabilistic demand scenarios | Better inventory and pricing strategy |
Measurable Results Across Sectors
Real outcomes from real engagements. Numbers, not promises.
Lithium Supply Chain
Challenge: Volatile pricing, complex extraction-to-customer logistics.
Solution: Supply chain simulation with price, yield, and logistics scenarios.
Outcome: $4.2M annual cost reduction, 15 percent margin improvement.
Aerospace Tier-1 Supplier
Challenge: Schedule risk on critical program, supplier lead-time uncertainty.
Solution: Program risk simulation with supply chain integration.
Outcome: Identified $80M schedule risk, $25M mitigation plan, delivered on time.
Automotive OEM
Challenge: EV transition, battery supply chain complexity, working capital bloat.
Solution: Multi-year transition simulation with supply chain redesign.
Outcome: $200M structural cost reduction over 5 years, supply resilience.
Real Estate Developer
Challenge: Long development cycle, phasing risk, financing structure uncertainty.
Solution: Development timeline and value simulation, multi-phase optimization.
Outcome: 18 months faster stabilization, 360bps IRR improvement.
FPSO Production Simulation
Challenge: Complex offshore production with variable reservoir decline, weather downtime, and maintenance scheduling constraints.
Solution: FPSO experiential simulation integrating reservoir performance, processing capacity, and logistics with Equinor.
Outcome: In development. Modeling production curves and maintenance windows to optimize offloading schedules.
Algorithmic Stocks Predictive Modelling
Challenge: Market volatility, fragmented signals, and the need for real-time predictive analytics across multiple asset classes.
Solution: Predictive modelling simulation with Bloomberg integrating macro signals, sentiment analysis, and scenario stress-testing.
Outcome: In development. Building probabilistic forecasting models for multi-asset portfolio strategies.
Reservoir Remaining Productive Life
Challenge: Aging reservoirs with uncertain decline curves, changing economic thresholds, and investment timing decisions.
Solution: Reservoir productive life simulation with ConocoPhillips modeling decline scenarios, water cut progression, and economic limits.
Outcome: Test & Validation. Refining decline curve models and integrating real-time production data for life-of-field optimization.
16-Cylinder Engine Simulation
Challenge: Complex thermal dynamics, fuel efficiency optimization, and emissions compliance for high-performance powertrains.
Solution: Full-cycle engine simulation with BMW modeling combustion dynamics, thermal load distribution, and performance mapping.
Outcome: 12 percent efficiency improvement, emissions reduced by 18 percent, 6-month faster prototyping cycle.