The Implementation of Machine Learning Methods in Six Sigma Projects – A Literature Review

One of the methods used today to improve processes in companies is Six Sigma. Within the application of this philosophy, machine learning methods are increasingly employed to analyse established procedures. In this article, the aim is to examine the present status of the use of supervised machine learning methods in the management of production process optimisation projects based on the Six Sigma approach. The analysis is presented based on a relevant literature review of applied/proposed machine learning methods in Six Sigma implementation. The work carried out included a literature review based on a search of significant databases on the issues presented. The research methodology was carried out in the following phases: 1) Identifying data sources and keywords combination; 2) Determining the term; 3) Searching the relevant websites; 4) Evaluating the review and summarizing the results of the searches carried out in the databases Scopus, Science Direct, Web of Science between 2017 and 2022. Keywords and their possible combinations were defined for each machine learning method in combination with the term “Six Sigma.” As a result of the research, MLs used in Six Sigma projects have been identified and linked to several areas of industrial substitution. The analyses produce identified targets suitable for regression and classification methods.