Within that context, robustification can include the process of finding the inputs that contribute most to the random variability in the output and controlling them, or tolerance design.
This basic principle underlies all robustification, but in practice there are typically a number of inputs and it is the suitable point with the lowest gradient on a multi-dimensional surface that must be found.
It was developed in the United States before the wave of quality methods from Japan came to the West, but still remains unknown to many.
After optimisation, the random variability of the inputs is controlled and reduced, and the system exhibits improved quality.
The analytical approach might also be used in conjunction with some kind of surrogate model that is based on the results of experiments or numerical simulations of the system.
Numerical optimisation methods such as hill climbing or evolutionary algorithms are then used to find the optimum nominal values for the inputs.
This approach typically requires less human time and effort than the other two, but it can be very demanding on computational resources during simulation and optimization.