Cost optimization optimizes cost and one or more responses at the same time to determine the factor settings that are both cost-effective and produce acceptable values for the responses. Often the factor settings that produce the best results are the most expensive to do. Cost optimization determines a compromise between minimizing cost and optimizing the responses.
For example, an engineer wants to determine the factor settings that maximize the yield of a chemical process. They can control temperature (low and high) and pressure (low and high) settings. The higher settings produce the best yield but are also significantly more expensive to do. The engineer can use cost optimization to meet product specifications in a cost effective way.
To do cost optimization, you must be able to measure or calculate cost for each treatment combination in the experiment. In Minitab, use the cost column as the response when you analyze your design.