Discrete event simulation
Introduction
A few years ago Eliyahu Goldratt visited Alcatel Bell Telephone Manufacturing in Antwerp. Nowadays the company is part of Alcatel-Lucent Bell The purpose of the visit was to discuss the company’s profitability issues. In a meeting with the management of the company his first question was: “What is the goal of your manufacturing organization?” Several ideas and opinions were brought up by the members of the management. The only answer that everybody agreed upon was: “The goal of a firm is to make money and be profitable”. Without that goal a company’s existence is in danger. There are several financial tools for the Chief Financial Officer to measure this goal. The most important are net profit, return on investment and cash flow. But how can a Chief Operation Officer decide and act in order to improve these financial goals? Goldratt answers this question with his Theory of Constraints.
He defines a series of operational measures:
- Throughput: Throughput is the rate at which a system generates money through sales or any other type of selling activities.
- Inventory: Inventory is the monetary value of the goods that are still in the system and which a company intends to use in its processes in order to sell their products.
- Operational Expense: Operational expense is the monetary value of the expenses of the system in order to turn inventory into throughput.
Goldratt explains to the management that they should use these measures to reach the Goal as follows: “Increase throughput while simultaneously reducing inventory and reducing operating expenses”
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Accoring to Goldratt’s theory, constraints are the only limits of the system that keeps the company from reaching the goal. There are 5 steps that have to be executed in order to reach the Goal.
- Identify the system's constraints.
- Decide how to exploit the system's constraints.
- Subordinate everything else to that decision.
- Elevate the system's constraints
- If in one the previous steps a constraint has been broken, go back to step 1.
The system’s constraints can also be defined as the bottlenecks in a system. The only way to improve the overall system is by process improvements at the bottlenecks. Finding these bottlenecks can be done using discrete-event simulations. A simulation can show hidden excess inventories, inefficiencies and overproduction. In the fourth step, the management makes the decision how to improve the bottlenecks. The consequences of these process changes can immediately be visualized and studied by a simulation of these improvements. These simulations give the possibility to quickly evaluate several solutions at a low cost. As a result, the management can choose the best possible solution. The improvement of Goldratt’s Theory Of Constraints and lean manufacturing in general, is just one of the many useful applications of discrete-event simulations. More applications and examples of discrete event simulations will be explained later in this chapter after we provided a solid definition and introduced the general ideas of discrete event simulations.
Definition
Definition according to Richard E. Nance in "A History Of Discrete Event Simulations" (1993):
“Discrete event simulation utilizes a mathematical/logical model of a physical system that portrays state changes at precise points in simulated time. Both the nature of the state change and the time at which the change occurs mandate precise description. Customers waiting for service, the management of parts inventory or military combat are typical domains of discrete event simulation.”
A more theoretical top-down description of discrete-event simulations is the following: The start of a discrete- event simulation is a model. A model is the construction of a conceptual framework, used to represent a system. The specific characteristics of discrete-event simulation model are the following:
- Stochastic: At least some of the models components are stochastic. This means that there is a certain factor of randomness in the model. This is represented by random number generators, which are explained in the terminology.
- Dynamic: The time evolution of the system’s components is important. Time is a significant variable.
- Discrete: The state of the system changes only significantly when events occur at discrete time instances.
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The difference with continuous simulations is the use of the factor time. The system evolves over time so the variables change continuously in a continuous simulation. Whereas in a discrete event simulation the events itself determine if something happens or not. Time can go by without anything happening.
A combination of discrete and dynamic simulation is called a combined simulation. The basic is a continuous simulation but certain discrete events interfere the continuity of the simulation making it a combined simulation.
Monte Carlo simulations are static simulations. This means that the factor time is not a significant variable in this model, whereas time evolution is important when dealing with discrete event simulations.
