Although discrete-event simulation has many advantages, there areseveral disadvantages with this approach. The models cannot be developed orused by unskilled personnel (Banks et al. 2005) unless proper user interfacesare developed. They can be costly and time-consuming to develop, may requiremore resources than are available, and require a lot of data that may not beavailable (Banks et al. 2005). In addition, it can be difficult to determinewhether observations are due to system interrelationships or due to therandomness of the variables in the model (Banks et al. 2005). Also, like anyother modeling method, simulation models require several assumptions andsimplifications to be made, so they do not represent the system 100 percent(Box 1979). However, many models can be considered useful if developed in theright way (Box 1979) with appropriate input data. To ensure the models areuseful, they must be verified and validated, which can be a time-consuming(but valuable) process. Verification is the process of ensuring that theactual programming and representation of the model function as expected. Forexample, checking that an entity proceeds to a different location when aqueue is full, or checking that a specific event occurs when the appropriateconditions are true, would be verification of the model. Validation is theprocess of ensuring that the model represents reality in a way that isconsidered acceptable to the developers and users of the model. For example,a model is considered valid if it contains all parameters that are relevantto the problem and the functional relationships between the parameters arevalid (Miller and Schmidt 1984). One way of determining this would be to getoutputs from the model and compare them with outputs from the real systemunder the same conditions. If the outputs are indistinguishable, the modelwould be considered valid (Skandari 2015b). It is important to note that allmodels must have the right mix between accuracy and utility (Miller andSchmidt 1984). However, no matter the amount of accuracy or utility, poorinput data will result in incorrect (and thus "invalid") outputdata, which can be referred to as the "garbage in, garbage out"analogy (Miller and Schmidt 1984).
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