Even though DoE is one of the best methods for process optimization, the effort behind the execution and repetition of the experiments and the costs associated are not favoring its widespread use in the industry. The pre requisite being identifying the parameters associated with the process input and then generating models to identify if it is an optimized one. The complexity and effort will increase depending on the number of parameters and that is the drawback when a process with multi parameter input above 10 or 20 , it will be very complex. This is not only used in the process optimization but also for the engineering product development, simulation and in calibration of systems widely.
Some of the alternatives used in the industry for DoE is Active DoE, which is using symbolic regression to select and propose specific parameter combination rather than executing a full factorial model to be executed. usually a 1/3rd or slightly more might be the outcome of applying Active DoE. This also has drawback that the number of parameters that can be considered can go up to 20 and if something goes beyond 20 it gets complicated. This is used widely in many calibration tools in automotive and avionics industry. Alternatively, another approach used is the parameter combination specific to the edge cases can be selected and simulated to evaluate the model and its behavior. In many applications, Mote Carlo simulation is used rather than DoE for the parameter selection and simulation in the industry.