Simulation creates a simplified working model of an operation so alternative conditions can be tested before the live system is changed. Spreadsheets often miss the effects of queues, timing, feedback, dependencies and rare events when these features interact. Marketways represents those interactions and compares scenarios under consistent assumptions. Clients can examine capacity, policy or process choices without first exposing customers or operations to the experiment.
The decision this method supports
We use Simulation & Scenario Analysis to help clients answer: How does the result change when important conditions, sequences or disruptions change?
How the method works
Simulation recreates important behaviour of a system so different conditions can be explored without changing the real operation. Scenario analysis develops coherent alternative conditions and asks how the decision performs in each. The methods are useful when interactions, timing or rare disruptions make a static calculation inadequate.
A business example
A hospital may simulate patient arrivals, treatment times, staff availability and bed constraints. The model can compare staffing arrangements during ordinary demand and a severe surge before the hospital changes the live operation.
How the client uses the result
Simulation allows leaders to test operating choices without disrupting the live business. It is valuable when queues, timing, dependencies or rare events make the consequences of a change difficult to calculate directly.
What we deliver
We produce forecast ranges, scenarios, risk distributions, prioritised alternatives or an optimised plan. A manager should understand which assumptions drive the result and what conditions would trigger a different action.
Limits and complementary methods
Simulation can test complex conditions safely, but the model remains a simplified representation and requires validation against the real operation.
Selected methods and techniques
We select from these established methods according to the decision, evidence and operating conditions.
- Discrete-event simulation: Build and run a model in which individual cases move through events, activities, queues and resources over time. Define arrivals, routing, service-time distributions, resource schedules, priorities, dependencies and exception rules, initialise the model, verify its logic and compare its baseline behaviour with observed data. Run each scenario enough times to show variation, warm-up effects and uncertainty rather than relying on one simulated path. Discrete-event simulation is the performed modelling method; Process Simulation is the populated analytical output, and a Process Simulation / Capacity Model is the documented client package.
- Incentive scenario simulation: Simulate the payouts and behavioural consequences of alternative incentive designs under a range of performance scenarios and plausible behavioural responses. It specialises simulation for reward design. It shows the cost, distribution and sensitivity of payouts before a design is adopted; actual responses may differ.
- Monte Carlo simulation: Calculate many possible outcomes by repeatedly drawing plausible values for uncertain inputs. The resulting spread shows what the specified model can produce; its usefulness depends on the input assumptions and how the inputs are connected.
- Queueing analysis: Estimate congestion and service performance when arrivals compete for limited people, vehicles, bays or other service capacity. Specify the arrival and service patterns, queue discipline, number and availability of servers, capacity, priorities, abandonment and relevant time horizon; then estimate waiting time, queue length, utilisation and service-level risk. Check whether steady-state or time-varying assumptions fit the operation and compare material results with observed data. Spare average capacity does not guarantee short waits when arrivals or service times are variable.
- Reverse stress testing: Begin with a precisely defined unacceptable outcome and work backwards to plausible combinations of assumptions, events or failures that would produce it. Identify the nearest or most decision-relevant breach conditions, propagation route and possible controls. It locates vulnerability boundaries; it does not forecast that the conditions will occur.
- Scenario analysis: Construct coherent alternative descriptions of how material conditions could combine, then evaluate consequences and decisions within each. Keep assumptions internally consistent, include a clear reference case and assign probabilities only when there is a defensible basis. Scenarios explore possibilities; they are not forecasts and cannot claim to cover every unknown.
- Scenario consequence modelling: Estimate operational, financial or other consequences of a specified scenario by tracing its assumptions through explicit relationships and dependencies. Keep scenario inputs, transmission logic and outcome uncertainty visible. The result is conditional on the scenario and does not state its probability or prove that the modelled pathway is causal.
- Scenario forecasting: Produce a separate future path for each coherent set of stated assumptions, using the same target, market boundary, units, horizon and calculation so differences can be interpreted. Include a clear reference case, keep assumptions within each scenario consistent and identify signs that would indicate which conditions are emerging. Scenario forecasts show what follows if specified conditions hold. They are not automatically probabilities or claims about the most likely future, and the range across selected scenarios is not a statistical prediction interval. Combine or weight scenarios only when the probabilities have an independent, defensible basis.
- Scenario modelling: Build an internally consistent model that turns a coherent set of scenario assumptions into connected outcomes. Specify how demand, capacity, cost, timing and dependencies respond together, and show which inputs are fixed or uncertain. It tests the consequences of a possible world; it does not establish that the world is likely.
- Simulation: Build and run a mathematical or computational representation of a system or calculation to examine how it behaves under specified relationships, rules, inputs and scenarios. Define what is represented, the inputs or probability distributions, decision rules and treatment of time where relevant. Verify that the model implements the intended logic, and compare its behaviour with observed evidence when suitable evidence exists. A stochastic simulation needs enough independent repetitions to show outcome variation instead of relying on one path. Discrete-event simulation follows events, queues and resources through time, while Monte Carlo simulation repeatedly samples uncertain inputs or outcomes. Simulation evaluates behaviour; it does not select the best decision unless deliberately linked to optimisation.
- Stress testing: Test a decision, model or operation under deliberately severe but clearly stated conditions to locate vulnerabilities and failure boundaries. A stress can be historical, hypothetical or generated, and results must say which. Stress testing shows what happens under the tested conditions; it does not estimate how likely those conditions are unless a separate probability model supports that claim.
- System dynamics modelling: Build a quantitative simulation model of stocks, flows, feedback loops and delays to explore how a system behaves over time under different policies and conditions. It tests the consequences of the system's structure. Its results depend on the model structure and parameter estimates, which are stated and tested.
Parent method family
Forecasting, Risk & Optimisation explains how this method connects to adjacent methods and relevant services.
Related service families
These service families contain business questions supported by this method. Service pages link to the wider method family so readers can understand the complete analytical approach.