Practitioners are often reluctant in using a formal optimization method for routine applications, mainly due to the general perception of requiring a large computational time and ending up with a specialized and often "brittle" solution. Optimization methods have come a long way and are made flexible to handle various practicalities including reduction of solution time, search for robust and reliable solutions, and discover useful knowledge understanding intricacies of the problem. In this talk, we shall lay out some of the new advances in optimization field, such as multi-criterion optimization, surrogate-assisted optimization, innovative knowledge discovery, etc., and demonstrate their usefulness on a number of industrial case studies.