Almost all quality improvement methods require data collection and analysis to solve quality problems. The combination of six sigma and lean manufacturing creates lean six sigma methodology that aims to reach six sigma quality levels, less than 3.4 part per million defectives, by reducing variations and wastes within processes. Achieving the goal depends on data collection to overcome quality problems.

Although many traditional data analysis techniques can be used to develop quality of products and processes, massive data sets collected by industry 4.0 technologies should be mined with powerful data analysis methods that produce meaningful results from big data. It is possible to make effective decisions by utilizing these analysis methods in each step of lean six sigma cycles. The use of data analysis methods at every stage, especially in the measure and analyze stages, has critical importance to make powerful decisions.

The aim of this study is to provide a guide that allows applying lean six sigma to make faster, more reliable and satisfied decisions with data. It contributes to the manufacturing processes with lean six sigma by reducing the lead-time, producing better quality products; on the other hand, it aids to make effective decisions using different mining techniques.