DMAIC公司
精益六西格玛
六西格玛
背景(考古学)
计算机科学
大数据
相关性(法律)
过程管理
过程(计算)
工业4.0
数据科学
制造工程
精益制造
工程类
数据挖掘
古生物学
法学
政治学
生物
操作系统
作者
Tanawadee Pongboonchai-Empl,Jiju Antony,Jose Arturo Garza‐Reyes,Tim Komkowski,Guilherme Luz Tortorella
标识
DOI:10.1080/09537287.2023.2188496
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI