Integration of Industry 4.0 technologies into Lean Six Sigma DMAIC: a systematic review

被引：46
作者：
Pongboonchai-Empl, Tanawadee [1 ]
Antony, Jiju [2 ]
Garza-Reyes, Jose Arturo [3 ,4 ]
Komkowski, Tim [1 ]
Tortorella, Guilherme Luz [5 ,6 ,7 ]
机构：
[1] Heriot Watt Univ, Edinburgh Business Sch, Edinburgh, Scotland
[2] Khalifa Univ, Dept Ind & Syst Engn, Abu Dhabi, U Arab Emirates
[3] Univ Derby, Ctr Supply Chain Improvement, Derby, England
[4] Graph Era Deemed Univ, Dept Management Studies, Dehra Dun, India
[5] Univ Melbourne, Ind Engn, Melbourne, Australia
[6] Univ Austral, IAE Business Sch, Buenos Aires, Argentina
[7] Univ Fed Santa Catarina, Florianopolis, Brazil
来源：
PRODUCTION PLANNING & CONTROL | 2024年 / 35卷 / 12期
关键词：
Lean Six Sigma; Industry; 4; 0; DMAIC; Big Data Analytics; data science; BIG DATA; IMPROVEMENT; PREDICTION; QUALITY; ORGANIZATIONS; MANAGEMENT; ANALYTICS; KNOWLEDGE; FRAMEWORK;
D O I：
10.1080/09537287.2023.2188496
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that 'Analyze' is by far the best-supported DMAICs phase through techniques, such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context.
引用
收藏
页码：1403 / 1428
页数：26
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