Lean Six Sigma 4.0 in an empirical application: a case of digital measurement for thickness deviation on a galvanizing line in a steel industry

Purpose This study aims to explore an empirical investigation for developing a digital control system for identifying steel sheet thickness deviation to improve productivity and coating quality on a galvanizing process line in the context of Lean Six Sigma 4.0. Design/methodology/approach This paper used a Lean Six Sigma 4.0 empirical application approach to promote a digital control system in zinc thickness measurement. The approach covered equipment specification, network configuration, control and monitoring software, security routine, thickness calculation, deviation routine and supervisory system. Performance tests show that this approach has produced good results, making it a viable application. Findings The main contribution of this study is to demonstrate that Lean Six Sigma 4.0 concepts can promote the measurement of steel sheet thickness deviations with accuracy, sensitivity and reliability, as demanded by the global market. The results of this study showed errors of 0.1% of the actual thickness, making it technically feasible to implement this system for steel sheet thickness readings ranging from 0.01 mm to 4 mm. Originality/value This research demonstrates an interdimensional linkage between Lean Six Sigma 4.0 concepts to explore an empirical investigation for implementing a digital control system using data acquisition modules and control software to guarantee the coating quality applied to steel products under the standard organization.

Authors

Institutions

Publication Details

Journal
International Journal of Lean Six Sigma
Published
2025-03-24
DOI
https://doi.org/10.1108/ijlss-08-2024-0188
Citations
3
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
3.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lean Six Sigma 4.0 in an empirical application: a case of digital measurement for thickness deviation on a galvanizing line in a steel industry

Andrei Bonamigo, Nuno Miguel de Matos Torre, Renan Souza Diniz
3 citations
International Journal of Lean Six Sigma
Manufacturing Process and Optimization
3.73
article

Lean Six Sigma 4.0 in an empirical application: a case of digital measurement for thickness deviation on a galvanizing line in a steel industry

Andrei Bonamigo, Nuno Miguel de Matos Torre, Renan Souza Diniz
article en
3 citations

Abstract

Purpose This study aims to explore an empirical investigation for developing a digital control system for identifying steel sheet thickness deviation to improve productivity and coating quality on a galvanizing process line in the context of Lean Six Sigma 4.0. Design/methodology/approach This paper used a Lean Six Sigma 4.0 empirical application approach to promote a digital control system in zinc thickness measurement. The approach covered equipment specification, network configuration, control and monitoring software, security routine, thickness calculation, deviation routine and supervisory system. Performance tests show that this approach has produced good results, making it a viable application. Findings The main contribution of this study is to demonstrate that Lean Six Sigma 4.0 concepts can promote the measurement of steel sheet thickness deviations with accuracy, sensitivity and reliability, as demanded by the global market. The results of this study showed errors of 0.1% of the actual thickness, making it technically feasible to implement this system for steel sheet thickness readings ranging from 0.01 mm to 4 mm. Originality/value This research demonstrates an interdimensional linkage between Lean Six Sigma 4.0 concepts to explore an empirical investigation for implementing a digital control system using data acquisition modules and control software to guarantee the coating quality applied to steel products under the standard organization.

International Journal of Lean Six SigmaVol. 16(5)
Universidade Federal Fluminense (BR)
Openalex Percentile: Top 8%
Manufacturing Process and Optimization
3.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.