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

Tanawadee Pongboonchai-Empl; Jiju Antony; Jose Arturo Garza-Reyes; Tim Komkowski; Guilherme Luz Tortorella

doi:10.1080/09537287.2023.2188496

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

Tanawadee Pongboonchai-Empl, Jiju Antony, Jose Arturo Garza-Reyes, Tim Komkowski, Guilherme Luz Tortorella

Management Science & Engineering

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

Abstract

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.

Original language	British English
Journal	Production Planning and Control
DOIs	https://doi.org/10.1080/09537287.2023.2188496
State	Accepted/In press - 2023

Keywords

Big Data Analytics
data science
DMAIC 4.0
Industry 4.0
Lean Six Sigma

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1080/09537287.2023.2188496

Cite this

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title = "Integration of Industry 4.0 technologies into Lean Six Sigma DMAIC: a systematic review",

abstract = "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 {\textquoteleft}Analyze{\textquoteright} 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.",

keywords = "Big Data Analytics, data science, DMAIC 4.0, Industry 4.0, Lean Six Sigma",

author = "Tanawadee Pongboonchai-Empl and Jiju Antony and Garza-Reyes, {Jose Arturo} and Tim Komkowski and Tortorella, {Guilherme Luz}",

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AU - Garza-Reyes, Jose Arturo

AU - Komkowski, Tim

AU - Tortorella, Guilherme Luz

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