We use the power of Deterministic and Probabilistic Mathematical Modeling to solve operational, logistical, and strategic problems, answering your business's most critical questions.
They allow us to estimate what will happen in the future or under unobserved conditions.
Financial Projections: Forecasting stock prices, inflation, GDP growth, or interest rates.
Weather and Climate Forecasting: Predicting short-term weather patterns or long-term climate trends (climate change).
Spread Prediction: Modeling the spread of diseases (epidemiology), wildfires, or environmental pollutants.
Demand Analysis: Predicting the quantity of a product or service that consumers will require in the future.
They seek to find the best possible solution (maximum benefit or minimum cost/effort) within a set of constraints.
Logistics and Supply Chains: Determining the most efficient route for transporting goods (Traveling Salesman Problem), the optimal location of warehouses, or the ideal inventory management.
Resource Allocation: Distributing limited resources (money, time, personnel) to different activities to maximize profitability or impact (Linear Programming).
Portfolio Design: Selecting the combination of financial assets that maximizes return for a given level of risk.
Production Scheduling: Deciding when and how much to produce to minimize operating costs.
They allow for virtual experimentation with complex systems without incurring real costs or risks.
Scenario Testing (Stress Testing): Simulating the impact of extreme conditions (economic crisis, infrastructure failure) on the stability of a company or system.
Product Design: Modeling the performance of a new product (e.g., car aerodynamics or bridge strength) before building physical prototypes.
Complex Systems Analysis: Simulating the behavior of networks (telecommunications, energy) or ecosystems to identify vulnerabilities.
Actuarial Risk: Modeling the probability of uncertain events (death, illness) to calculate insurance premiums and financial reserves.
They help to understand the fundamental relationships between variables and to extract knowledge from data.
Parameter Identification: Determining the unknown values or constants of a system from experimental or observational data.
Cause-Effect Relationship: Using statistical models (regression) to establish the influence of one variable on another (e.g., how does advertising affect sales?).
Systems Control: Designing control systems that automatically adjust parameters to maintain a target variable within a desired range (e.g., temperature control in a reactor).
Scientific Discovery: Formulating theoretical models (in physics, biology, chemistry) that explain observed natural phenomena.