General Examples

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General Examples of Discrete-Event Simulations

Simulations help improve the overall understanding of a system's behavior without the cost and time investment involved in building it. Managers often use simulations to aid in making decisions about how to configure systems to handle projected workloads or to determine the performance of a new design. Simulations show you the behavior of the system, based on given sets of information. A valid model of a complex system can be extremely useful for predicting system performance as the workload varies, such as transaction response times and failure rates.

Discrete-event simulations can help determine rules for building and maintaining many different types of systems. They help determine the best way to set up a system, such as the number of people required to adequately staff a tech support center, or the number of incoming lines required, or even the optimal ratio of agents to phone lines. Discrete-event simulations can optimize situations such as the following:

Number of customer service representatives, bank tellers, or store clerks required to provide good service to customers, given a variable rate of arrival and type of service required as well as a certain minimal acceptable level of service
Number of gates and equipment required at an airport to service a certain number of flights
Best defense weapons or strategies to use in certain circumstances
Increased punctual delivery of products
Economic and financial forecasting
Reduced operating expenses and equipment requirements
Visibility into the system-wide effect of a specific change. For example, if you add more phone lines to a call center, how does that affect the overall call statistics?

In other examples, CSIM 19 is often used to evaluate and test technology in realistic settings, in order to hit the "sweet spot" in the space of design feature tradeoffs or to prove the capability of a new design:

Evaluating hardware and software requirements for computer systems, web servers, and database systems
Modeling the performance of new computer and network architectures, algorithms, and components
Finding the best configuration of servers for a network, including number of CPUs, number of disk drives, amount of memory, and required LAN transfer rate
Determining assembler and test operator requirements for an assembly line of TV sets
Configuring manufacturing processes to reduce bottlenecks
Defining inventory ordering policies and setting optimal inventory levels
Designing communication systems and their message protocols
Modeling designs for service organizations (call centers, stores, post offices, hospitals, etc.)

In addition to helping users deploy the best configuration of a complex system, simulations help improve understanding of the overall system so that designers can continue to improve it.

For more details about CSIM 19, process-oriented discrete-event simulation, and general simulation examples, check out the CSIM 19 Product Description.