Design of Experiments (DOE) is a methodology employed for performing extensive parameter studies on a machine or process where a resulting problem is known, but the cause of the problem is not. A number of parameters or factors are thought to contribute to the problem and a single factor may be considered to have two or more levels. For example, a factor could be a vehicle suspension bush rate whose levels may be defined by a tolerance of minus 15%, the intended value and plus 15%. If all factors and levels are considered, then the number of permutations may be too many to be analysed. DOE employs techniques such as fractional factorial analysis, where a realistic proportion of the full permutations is analysed in order to better understand the system behaviour. The information obtained from such analysis is:   Which of the factors or interactions between the factors are most significant with respect to the problem. What should the factor levels be in order to minimise the problem. Further analysis can then be employed on a reduced number of factors using methods such as DOE response surface techniques or optimisation in order to make detailed predictions of what optimum factor levels should be in order for the objective to be achieved. Software employed to perform the DOE and optimisation analysis is Minitab, MATLAB, ADAMS, ADAMS/Insight and SIMPACK.