Common Sense Solution: Use common sense if the problem allows.

Analytical Solution: If the problem has an analytical solution, avoid simulation.

Direct Experimentation: Choose direct experiments if cost-effective.

Costs vs.Savings: Avoid simulation if costs exceed potential savings.

Resource Constraints: If resources are insufficient, avoid simulation.

Time Constraints: Skip simulation if it exceeds available time.

Data Availability: Simulation requires data; without it, it's not advised.

Verification and Validation: If lacking time or personnel, avoid simulation.

Unrealistic Expectations: Avoid if expectations are unreasonable.

Complex System Behavior: Skip simulation for overly complex or undefined system behavior.

Exploration of new policies, operating procedures, decision rules, information flows, and organizational procedures without disrupting real system operations.

Testing of new hardware designs, physical layouts, transportation systems, etc., without committing resources for their acquisition.

Investigation of hypotheses about the occurrence of certain phenomena for feasibility.

Compression or expansion of time to speed up or slow down the phenomena under investigation.

Insight into the interaction of variables.

Understanding the importance of variables to the system's performance.

Performing bottleneck analysis to identify areas of excessive delays in work in process, information, materials, etc.

Gaining insight into how the system operates, rather than relying solely on individual perceptions.

Addressing specific questions through simulation studies, contributing to the development of knowledge and decision-making.

Training Requirement: Building models demands specific expertise.

Interpretation Challenge: Outputs, often random, pose interpretation difficulties.

Resource Intensity: Time and cost make simulation modeling demanding.

Limited Applicability: Analytical solutions may be preferable in certain cases.

Simulation software vendors develop user-friendly packages with input-only models labeled as "simulator" or "template."

Output-analysis features are incorporated for comprehensive analyses.

Ongoing hardware advancements enhance simulation speed, accommodating complex scenarios in packages.

Simulation proves essential for tackling real-world complexities, as closed-form models often fall short in practical problem-solving.

Manufacturing Applications

Business Processing

Wafer Fabrication

Construction Engineering and Project Management

Logistics, Transportation, and Distribution

Military Applications

Healthcare

Additional Applications

Studying Complex Systems: Simulation is appropriate for understanding and experimenting with the internal interactions of complex systems or subsystems within them.

Modeling Changes: Simulation allows for the simulation of informational, organizational, and environmental changes, observing how alterations affect the model's behavior.

System Improvement: Knowledge gained from designing simulation models can provide valuable insights for suggesting improvements in the investigated system.

Variable Analysis: Changing simulation inputs and observing outputs provides insights into the importance of variables and their interactions.

Pedagogical Tool: Simulation serves as a pedagogical device, reinforcing analytic solution methodologies and aiding in learning.

Pre-implementation Experimentation: Simulation can be used to experiment with new designs or policies before implementation, preparing for potential outcomes.