Date: 9/20/2016
Author: Descreye Solutions
Creating simulation models can be difficult. Many people have spent hours creating models only to scrap them because they weren't getting any useful insight from them. The following 4 mistakes are common for people when they start creating simulation models. Avoiding them is an important part of becoming an experienced discrete-event simulation modeler.
Many times someone has finished creating a current-state model and jumped right into testing various alternative scenarios. It is then surprising when the changes don't reflect anything close to the expectations. Often this is a result of not validating the original model.
Validation is the step of simulation model building where the simulation model is tested against the real-life system to confirm that it accurately represents the system. The amount and rigidity of model validation depends on the model that is being created, but some model validation should be done in any situation. Another common mistake with model validation is waiting until the end of model creation to start validating the model. If one waits until the whole system is modeled, then if makes it difficult to identify which steps in the process is not representative of the real-life system. Validating the model during each step of model creation is an easier method. This is done by testing each process as it is added to the model to make sure it performs as expected. Once the model is complete the whole system can then be tested to make sure it is an accurate representation. Whatever the method, model validation is a necessary step to any successful simulation project.
Simulation thrives off of providing insights into systems that have a significant amount of variability. However, often people use average values as the input in their simulation models. This makes the model less accurate than if the data was fit to a distribution that could be used to sample random numbers. Simulation uses random number sampling distributions so that it can accurately reflect the variabiltity that is found in the real-life system. By accurately reflecting the variability it is then possible to get more accurate insight from the simulation model.
Usually people use averages for the following reasons:
Through developments in simulation software packages it has become much easier to do random number sampling, which makes using average values much less useful in simulating systems for process improvement.
Trying to model every aspect of the system is a critical error in most simulations. Often people who are just starting to simulate systems try to add every aspect of the system to the model, because they want to see everything run as it does in real-life. This makes the modeling time grow and grow until the project inevitably fails.
The following are a few questions an experienced simulation modeler will ask to ensure that they don't model too much:
Sometimes it becomes apparent that the simulation model doesn't have the details that are necessary for the intended analysis. This problem often becomes apparent during a meeting after the simulation is completed when someone semi-involved in the project asks, "What if we were to change x, how would that influence this scenario." If x wasn't in the original model or was an underlying assumption, then it is often difficult to add that into the model. However, the method for making sure that the model isn't too simple is to review the same questions that were asked for modeling too much. Those questions should be reviewed with every stakeholder in the project before the model is started in order to ensure that all the necessary detail will be included.