Lean Six Sigma is a quality improvement methodology that combines the principles of Six Sigma and Lean Manufacturing. It aims to achieve Six Sigma quality levels, less than 3.4 parts per million defectives, by reducing variations and wastes within processes. To achieve this goal, data collection is required to overcome quality problems. Although many traditional data analysis techniques can be used to develop the 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. Utilizing these analysis methods in each step of Lean Six Sigma cycles allows effective decisions to be made. 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.
