Simulation is an incredibly powerful tool that lets you analyze, design, and operate complex systems. Imagine trying to understand a process that’s too intricate for spreadsheets or flowcharts—simulation models can help you do just that. They allow you to test hypotheses at a fraction of the cost of real-world experiments. Plus, they’re great for communicating how an operation works and sparking creative ideas for improvement. Whether in industry, government, or education, models help shorten design cycles, cut costs, and boost knowledge.
Why Simulation Matters
At its core, simulation involves creating a model of a system and running experiments on it over time. This lets you see how a real-world activity might perform under different conditions without the high costs of actual trials. One of the best things about models is that you can start simple and gradually add details as you learn more. This “step-wise refinement” helps you get a good grasp of complex problems quickly. As you refine the model, it starts to mimic the real-life process more closely.
Systems, Models, and Simulation
Every profession uses models in some form, but the term “model” can mean different things to different people. Let’s break it down:
Systems
Think of the real world as a collection of systems. A system is a set of related components or entities that interact based on certain rules or policies.
- Entities are the internal parts of the system, involved in various processes.
- Operating policies are the external inputs, like controls and resources, that dictate how the system operates.
Over time, the interactions of these entities cause changes in the system, known as system behavior or dynamics. Systems can be simple, like a flower growing towards the sun, or complex, like supply chain operations composed of planning, selling, distribution, production, and sourcing subsystems.
Models
A model is a simplified representation of a system at a specific point in time. It captures the essence of the system while focusing on the most important aspects for analysis. Models come in various forms:
- Scaled representations: Like a 1:18 diecast model of a Tesla or a scale model of the solar system.
- Graphical or symbolic visualizations: Think flowcharts, board games like Monopoly, or architectural plans.
- Analytical or mathematical formulas: These provide static, quantitative solutions, like spreadsheet or linear programming models.
- Dynamic models: These incorporate data and assumptions to describe how a system behaves over time. ExtendSim products, for example, are tools for building these dynamic models.
Dynamic modeling is the backbone of computer modeling, showing how systems evolve over time. ExtendSim models typically have a time component and can show cause and effect and the flow of entities throughout a system (you can also create ExtendSim animations that show spatial relationships).
Simulation
According to Wikipedia, simulation is “the imitation of the operation of a real-world process or system”. To simulate something, you first need a model that represents the key characteristics of the system. The simulation then shows how the system operates over time.
Simulations run in “simulation time,” an abstraction of real time. As the simulation clock ticks, the model updates its values and outputs results. If the model is accurate, these outputs will reflect the real system’s performance.
Using ExtendSim for simulation means creating a logical model that mirrors the real system. You simulate its operations, analyze areas of interest, and make informed decisions to reduce risk and uncertainty.
The simulation industry is focused on making software more user-friendly while maintaining flexibility and accuracy. Features like template models, integrated databases, and interfaces with other tools are making it easier to build and maintain useful simulation models.
For complex problems, simulation is just one part of the solution. Various applications help define processes, gather data, model and simulate, analyze and optimize, and present results. Improved interfaces between software applications reduce rework and errors.
While advances in simulation software are making it easier to build models, a good understanding of the processes being modeled is still crucial. The goal is to spend less time on the details of modeling and more time solving the actual problem.
