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Machine Learning-based Lean Six Sigma Process

Yildirim, Deniz LU (2023) INF003 20221
Department of Informatics
Abstract
Increasing the effectiveness and efficiency of production processes have always been a significant challenge, primarily because such processes often contain activities that lead to waste. In response to address these challenges, tools like Lean and Six Sigma, and over time their descendant, Lean Six Sigma (LSS) were developed. It is argued that Industry 4.0 proliferated data accessibility in the manufacturing industry and machine learning (ML) tools could utilize and maximize information from vast amount of data and convert it into helpful information that helps in decision making and implementation of LSS. This study focuses on understanding the perception of the individuals working within firms where ML-based LSS has been used and the... (More)
Increasing the effectiveness and efficiency of production processes have always been a significant challenge, primarily because such processes often contain activities that lead to waste. In response to address these challenges, tools like Lean and Six Sigma, and over time their descendant, Lean Six Sigma (LSS) were developed. It is argued that Industry 4.0 proliferated data accessibility in the manufacturing industry and machine learning (ML) tools could utilize and maximize information from vast amount of data and convert it into helpful information that helps in decision making and implementation of LSS. This study focuses on understanding the perception of the individuals working within firms where ML-based LSS has been used and the impact on the manufacturing operation. By utilizing a mixed-method approach of literature review and survey, the study finds that ML could develop further and enhance the production activities beyond what was achieved by LSS. ML allowed for continuous improvement and identification of critical challenges within the system that were previously identified. This led to the improvement in many previously unidentifiable areas and helped improve the overall quality of the process. ML is seen as critical to the future of manufacturing activities as to improve production effectiveness and efficiency. (Less)
Please use this url to cite or link to this publication:
author
Yildirim, Deniz LU
supervisor
organization
alternative title
Machine Learning-based Lean Six Sigma Process: A study on its impact in reducing waste and optimizing production in manufacturing companies
course
INF003 20221
year
type
M2 - Bachelor Degree
subject
keywords
Machine Learning, Lean Six Sigma, Waste Reduction, Efficiency, Manufacturing
report number
INF22-80
language
English
id
9113253
date added to LUP
2023-04-11 11:03:46
date last changed
2023-04-11 11:03:46
@misc{9113253,
  abstract     = {{Increasing the effectiveness and efficiency of production processes have always been a significant challenge, primarily because such processes often contain activities that lead to waste. In response to address these challenges, tools like Lean and Six Sigma, and over time their descendant, Lean Six Sigma (LSS) were developed. It is argued that Industry 4.0 proliferated data accessibility in the manufacturing industry and machine learning (ML) tools could utilize and maximize information from vast amount of data and convert it into helpful information that helps in decision making and implementation of LSS. This study focuses on understanding the perception of the individuals working within firms where ML-based LSS has been used and the impact on the manufacturing operation. By utilizing a mixed-method approach of literature review and survey, the study finds that ML could develop further and enhance the production activities beyond what was achieved by LSS. ML allowed for continuous improvement and identification of critical challenges within the system that were previously identified. This led to the improvement in many previously unidentifiable areas and helped improve the overall quality of the process. ML is seen as critical to the future of manufacturing activities as to improve production effectiveness and efficiency.}},
  author       = {{Yildirim, Deniz}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Machine Learning-based Lean Six Sigma Process}},
  year         = {{2023}},
}