Artificial intelligence (AI);
blockchain;
deep learning;
machine learning;
quality;
six sigma (SS);
systematic literature review (SLR);
BIG DATA;
LEAN MANAGEMENT;
INDUSTRY;
4.0;
HEALTH-CARE;
QUALITY INSPECTION;
HIGHER-EDUCATION;
CHALLENGES;
CONSTRUCTION;
FRAMEWORK;
KAIZEN;
D O I:
10.1109/TEM.2023.3324542
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
The dramatic development of technology in recent years has affected most organizations and companies. Artificial intelligence (AI) and Blockchain can be mentioned as the most important technologies that are developing rapidly. In Industry 4.0, complex data are produced in high volume, which makes implementing improvement projects with traditional methods lose their effectiveness. Six Sigma is one of the most prominent improvement methods organizations and companies use to identify and solve problems. Therefore, for implementing Six Sigma projects in Industry 4.0, the need to develop the traditional Six Sigma toolbox is felt. AI and Blockchain can be suitable tools for developing and improving Six Sigma for Industry 4.0. A systematic review identified 58 articles in this article that presented solutions for integrating AI or Blockchain in Six Sigma. Some articles have evaluated the performance of their proposed method by implementing the proposed models. The most widely used machine learning and deep learning algorithms in Six Sigma have been identified. Also, Six Sigma approaches that mostly use AI or Blockchain have been identified by the analysis of articles. Decision tree algorithms and artificial neural networks are used in most Six Sigma define-measure-analyze-improve-control (DMAIC) projects. Therefore, by reviewing the articles, it was found that AI and Blockchain are mainly used as efficient tools in DMAIC and the Design for Six Sigma models to implement Six Sigma projects. In this article, 28 main gaps are presented as future works for future research.
机构:
Univ Fed Sao Carlos, Dept Ind Engn, Washington Luis Rd S-N, BR-13565905 Sao Carlos, SP, BrazilUniv Fed Sao Carlos, Dept Ind Engn, Washington Luis Rd S-N, BR-13565905 Sao Carlos, SP, Brazil
Message Costa, Luana Bonome
Filho, Moacir Godinho
论文数: 0引用数: 0
h-index: 0
机构:
Univ Fed Sao Carlos, Dept Ind Engn, Washington Luis Rd S-N, BR-13565905 Sao Carlos, SP, BrazilUniv Fed Sao Carlos, Dept Ind Engn, Washington Luis Rd S-N, BR-13565905 Sao Carlos, SP, Brazil
Filho, Moacir Godinho
Fredendall, Lawrence D.
论文数: 0引用数: 0
h-index: 0
机构:
Clemson Univ, Dept Management, Clemson, SC 29634 USAUniv Fed Sao Carlos, Dept Ind Engn, Washington Luis Rd S-N, BR-13565905 Sao Carlos, SP, Brazil
Fredendall, Lawrence D.
Gomez Paredes, Fernando Jose
论文数: 0引用数: 0
h-index: 0
机构:
Univ Fed Sao Carlos, Dept Ind Engn, Washington Luis Rd S-N, BR-13565905 Sao Carlos, SP, BrazilUniv Fed Sao Carlos, Dept Ind Engn, Washington Luis Rd S-N, BR-13565905 Sao Carlos, SP, Brazil
机构:
Mercu Buana Univ, Ind Engn Dept, Jln Meruya Selatan 1, Kembangan 11650, Jakarta, IndonesiaMercu Buana Univ, Ind Engn Dept, Jln Meruya Selatan 1, Kembangan 11650, Jakarta, Indonesia
Tampubolon, S.
Purba, H. H.
论文数: 0引用数: 0
h-index: 0
机构:
Mercu Buana Univ, Ind Engn Dept, Jln Meruya Selatan 1, Kembangan 11650, Jakarta, IndonesiaMercu Buana Univ, Ind Engn Dept, Jln Meruya Selatan 1, Kembangan 11650, Jakarta, Indonesia