Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously.
Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27. link ↗