How it Works
A Structured Approach to Simulation
From Engineering Questions to Defensible Decisions
Every assignment begins with a decision. Is the proposed production capacity achievable? What equipment configuration is required? Would additional storage improve performance? How would alternative operating strategies affect production?
MISIM works with engineering and operations teams to establish what needs to be tested, then develops a simulation model appropriate to the complexity and consequences of the decision.
Models can draw on operating data, engineering designs, historical experience and clearly defined assumptions. Real-time data integration is available where useful, but is not a prerequisite for credible analysis.
Our five-stage DIVES methodology provides a structured approach to defining the problem, developing and validating the model, evaluating alternatives and translating results into engineering decisions.
D
Define
We begin by establishing the decision and modelling requirements: the engineering or business question, the alternatives under consideration and the performance measures that will inform the decision.
Together with the client, we establish the system boundaries, available information, critical assumptions and appropriate level of modelling detail. If a simpler analytical approach can reliably answer the question, we recommend it.
- Decision objectives and performance measures
- Model boundaries, scope and required detail
- Available data and key assumptions
I
Implement
We develop a simulation model that represents the physical and operational behaviour of the system, including equipment, material flows, storage, resources, schedules, reliability and operating rules.
The level of detail reflects the intended application. Models may represent a specific operating process or connect multiple production and logistics systems into an integrated analysis.
- Equipment, resources and operating logic
- Engineering data and model inputs
- Performance measures and model verification
V
Validate
We establish confidence in the model by checking that it provides a credible representation of the system before its results support important decisions.
For existing operations, we compare model behaviour with historical performance and operational experience. For greenfield developments, validation draws on engineering calculations, design information, representative operating data and reviews with subject-matter experts.
We document assumptions, limitations and areas of uncertainty so that results can be interpreted appropriately.
- Verification of model logic and calculations
- Comparison with operating data or engineering benchmarks
- Review of assumptions, limitations and model behaviour
E
Evaluate
We use the model to compare alternatives and quantify outcomes across equipment configurations, operating strategies, maintenance arrangements, storage capacities and investment options.
Simulation captures interactions and variability that can be difficult to assess using static calculations. By examining different scenarios and credible operating assumptions, we establish how alternatives affect achievable production, utilization, delays, inventories and other performance measures.
- Scenario analysis and alternative configurations
- Bottleneck identification and capacity assessment
- Performance sensitivity, uncertainty and risk
S
Synthesize
We translate simulation results into engineering decisions by presenting achievable performance, trade-offs, investment implications and the conditions under which outcomes may change.
Results are communicated through technical reports, visualizations and model demonstrations as appropriate. Our objective is to provide transparent analysis that engineering, operations and management teams can review and use.
- Quantified outcomes and comparisons
- Key assumptions, trade-offs and uncertainties
- Recommendations and next steps
See Simulation in Action
An animation of our train-loadout simulation illustrates how equipment movements, operating rules and system interactions can be represented in a discrete-event model.
Animation helps engineering and operations teams review how the model works, while the underlying analysis quantifies capacity, utilization, delays and other performance measures.
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