In part one, I shared a case study of how a small bicycle-chain manufacturing company in India used Six Sigma’s DMAIC approach to reverse declining productivity. After completing the define, measure, and analysis phases, the team had identified the important factors in the bushing creation process. Armed with this knowledge, they were ready to make some improvements.

The improve phase

During the improve phase, the team applied a statistical method called design of experiments (DOE) to optimize the important factors they’d identified in the initial phases.

Most of us learn in school that to study the effects of a factor on a response, you hold all other factors constant and change the one you’re interested in. But DOE lets you change more than a single variable at a time. This minimizes the number of experimental runs necessary to get meaningful results, so you can reach conclusions about multiple factors efficiently and cost-effectively.

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