3.0 Strategic Considerations for M&S Projects
The decision to undertake a Modelling & Simulation project is a strategic choice that requires a clear-eyed assessment of its benefits and drawbacks. While M&S offers a powerful lens for understanding complex systems, project leaders must weigh the significant advantages against the inherent challenges and resource requirements to determine if it is the right approach for their specific problem.
Benefit and Risk Analysis for M&S Adoption
| Key Advantages | Potential Disadvantages |
| Easy to understand: Allows stakeholders to see how a system operates without interacting with the real-time system. | Requires domain expertise: Designing an effective model is an art that requires training and experience. |
| Easy to test: Allows for changes to be made to the system to test their effects without real-world consequences. | Difficult to predict results: The use of random numbers means results are not always easily predictable. |
| Easy to upgrade: Helps determine system requirements by applying and testing different configurations. | Time-consuming: Simulation requires significant manpower and can be a lengthy process. |
| Identifies constraints: Enables bottleneck analysis to find delays in work processes or information flow. | Expensive: The combination of expert manpower and time makes the simulation process costly. |
| Diagnoses problems: Allows for a deep understanding of complex system interactions and the exploration of new policies or procedures. | Results difficult to translate: The outputs often require experts to interpret them correctly for business application. |
Primary Application Areas
The utility of M&S spans a wide range of industries and disciplines, demonstrating its flexibility as an analytical tool. Key application areas include:
- Military applications, training, and support
- Designing semiconductors
- Telecommunications network analysis
- Civil engineering designs and presentations
- E-business models
- Studying the internal structure of complex biological systems
- Optimizing system designs such as routing algorithms and assembly lines
- Testing new designs and policies
- Verifying analytic solutions
Understanding these applications and the associated trade-offs allows an organization to make an informed decision about when and where to apply this powerful methodology.