Abstract
Rapid and frequent organizational change has been a hallmark of business environments in the past two decades. Frequently, technology and new software development are embraced as aspects of complex strategies and tactical plans. Without sufficient analysis, the unforeseen consequences of change can result in unexpected disruptions and the loss of productivity. In order to better control these contingencies, modern managers often employ a variety of decision support aids. One such aid, classified as a representational decision support system, is discrete event simulation (DES).
TopBackground
In its purest form, DES is considered to be a branch of applied mathematics. Its considerable popularity is due in part to the availability of computers and improvements in simulation languages and simulator software packages. DES is often the technique of choice when standard analytical or mathematical methodologies become too difficult to develop. Using a computer to imitate the operations of a real-world process requires a set of assumptions taking the form of logical relationships that are shaped into a model. These models assess the impact of randomly occurring events. Experimental designs are developed and the model manipulated to enable the analyst to understand the dynamics of the system. The model is evaluated numerically over a period of time, and output data are gathered to estimate the true characteristics of the actual system. The collected data are interpreted with statistics allowing formulation of inferences as to the true characteristics of the system. Table 1 lists primary features of a DES application.
Table 1. | Feature | Description |
| Statistics Collection | Tools that gather data for purposes of inferential statistics about the model |
| Resource Modeling | A means for the representation of a constrained resource in the model |
| Transaction | A means for representation of the participants in the simulation model |
| Simulation Clock | Tools for analysis and step processing of the coded model |
| Random Number Generators | A means for producing random number streams for randomization of events within the simulation model |
| Model Frameworks | Generalized frameworks for the rapid development of a model |
While the value of DES in organizational settings has been accepted and is evidenced by the varied and growing market of related products, not every DES application is suited for every problem domain. For this reason, information systems researchers have worked to identify salient characteristics of DES and its usage, and then measure the relationship between its application and successful organizational outcomes. These studies have been conducted in different ways with focuses on independent and dependent variables.
Key Terms in this Chapter
Discrete Event Simulation (DES): Use of a computer to mimic the behavior of a complicated system and thereby gain insight into the performance of that system under a variety of circumstances. Generally the system under investigation is viewed in terms of instantaneous changes due to certain sudden events or occurrences.
Theory of Reasoned Action (TRA): This theoretical model explains actions by identifying connections between various psychological constructs such as attitudes, beliefs, intentions, and behaviors, then posits that an individual’s attitude toward a given behavior is determined by the belief that this behavior will result in particular outcome.
Technology Acceptance Model: A theoretical explanation of factors influencing technology usage that hypothesizes perceived ease of use and perceived usefulness influence a person’s intention, which in turn determine actual technology usage.