MATHEMATICAL MODELLING
Model the options. Understand the trade-offs.
Simulation, optimisation and quantitative modelling for decisions involving uncertainty, competing objectives and operational constraints. We define the assumptions, test scenarios and explain what the results mean for your decision.
- Scenarios, constraints and uncertainty
- Simulation and optimisation
Define the variables, constraints and assumptions.
We define the objectives, decision variables, constraints and assumptions before selecting a modelling approach. The model represents the aspects of the process relevant to the decision rather than unnecessary detail.
- Scenario analysis and simulation
- Statistical modelling and uncertainty analysis
- Optimisation under practical constraints
Example project scopes
Scenario analysis, simulation and optimisation can compare alternatives under stated assumptions. Results are examined for sensitivity, uncertainty and the practical consequences of changing inputs.
- Evaluate staffing and demand scenarios to understand queue growth and capacity.
- Compare scheduling or resource-allocation choices under operational constraints.
- Build a cost and capacity model with sensitivity analysis for changing inputs.
Deliverables and handover
Deliverables include the model and its assumptions, scenario definitions, reproducible results and an explanation of limitations. The findings support judgement; they do not remove uncertainty from the decision.
Scope: A model evaluates outcomes under stated assumptions; it does not remove uncertainty.
DELIVERABLES
What we deliver.
- Documented model
- Input and assumption definitions
- Scenario comparisons
- Sensitivity analysis and handover
HOW WE WORK
From requirements to handover.
- Frame the decision
- Assemble assumptions and evidence
- Construct and validate against suitable baselines
- Explore scenarios and hand over reasoning
ENGAGEMENT DETAILS
Planning your project.
Can a model predict the future?
It explores outcomes under stated assumptions.
What if we have limited data?
Known inputs and assumptions are separated, and useful missing evidence is identified.
Can it be a practical tool?
Yes, where repeated use justifies a workbook or application.
Is this only for large organisations?
No. Scope depends on the decision and its complexity.
LET’S DISCUSS YOUR REQUIREMENTS