The world around us is full of information, with decisions to be made. The way this information is handled and organized impacts the decisions that are borne out of it. Lots of information makes us informed but may not help in risk free decision making. That is where modeling becomes important. Modeling is a methodology to facilitate solving real world problems. Real world experiments are too complex, costly, and may be dangerous and impossible. In such a scenario we can build a model that represents the real system, using abstraction – including details we believe are important and leave those we assume not to be important. Mapping the real world through numerous phases is more an art than science.
The most common model is the mental model that most of us use to understand how things work in real world and make decisions based on such models. However, with the increase in complexity and uncertainty mental models may give a false notion or a biased decision.
Computers are powerful modeling tools, and offer flexible virtual world, enabling creation of nearly anything imaginable. Computer models vary from basic spreadsheets to complex simulation modeling tools allowing experienced users explore dynamic systems such as consumer markets and battlefields.
Modeling takes two dimensions – analytical and simulation. Spreadsheets are the most popular in analytical modeling, however it has its own limitations when the complexity and uncertainty of the problem increases. On the other hand, simulation modeling techniques are capable of handling this complexity and uncertainty to peep into the future for predictive and prescriptive analytics. Simulation models are executable model helping understand the trajectory of a system’s temporal and spatial state changes.
Real-world problems affect businesses, big and small due to the uncertainty and complexity of the environment. The need for companies and organizations to solve these problems, safely and efficiently have always been relevant and a huge challenge. However, computational modeling through simulation has made this challenge easier to apprehend. It provides an important method of analysis through simulating real-world scenarios, which are easily verifiable and simple to communicate and understand. Across industries and disciplines, simulation modeling provides valuable information by giving clear insights into complex systems for viable solutions.
Simulation modeling provides a safe way to test and explore different “what-if” scenarios in our attempt to mitigate risks. This scenario testing allows us the opportunity to make wrong decisions when need be, just to test worse case scenarios while planning for best case scenarios, in a safe virtual environment as we plan for the real-world. This is important, especially when the economy can be volatile and unpredictable as well as competition becoming fiercer. We aim to make the right decisions in simulations before making real-world changes. It is much more cost effective to run tests than making mistakes in the real-world.