TY - GEN
T1 - Data-Driven Lean Six Sigma
T2 - 34th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2025
AU - Rashidifar, Rasoul
AU - Silvas, Matthew
AU - Chen, F. Frank
AU - Ebrahimi, Nafiseh
AU - Shahin, Mohammad
AU - Cheng, Peng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - The implementation of Lean Six Sigma is a powerful approach to address inefficiencies and enhance operational performance in manufacturing systems. This paper focuses on the application of Lean Six Sigma methodology to improve the die maintenance process. In the first step, a comprehensive data collection through time studies and value stream map is established to identify baseline metrics such as downtime, lead time, defect rate and productivity. A Root Cause Analysis is applied to determine key inefficiencies such as disorganized workspaces, lack of standardized work instructions, and inefficient die storage. Afterward, Lean Six Sigma tools are implemented to improve inefficiencies. The results demonstrate crucial improvements in downtime (61.4%), defect rate (20%), lead time (13.9%) and employee productivity (40%). Statistical validation using Hypothesis Testing confirms that downtime and employee productivity are statistically significant, however defect rate and lead time are not significant, indicating areas for further improvement. A positive cultural change is also demonstrated, as employees report higher morale and engagement.
AB - The implementation of Lean Six Sigma is a powerful approach to address inefficiencies and enhance operational performance in manufacturing systems. This paper focuses on the application of Lean Six Sigma methodology to improve the die maintenance process. In the first step, a comprehensive data collection through time studies and value stream map is established to identify baseline metrics such as downtime, lead time, defect rate and productivity. A Root Cause Analysis is applied to determine key inefficiencies such as disorganized workspaces, lack of standardized work instructions, and inefficient die storage. Afterward, Lean Six Sigma tools are implemented to improve inefficiencies. The results demonstrate crucial improvements in downtime (61.4%), defect rate (20%), lead time (13.9%) and employee productivity (40%). Statistical validation using Hypothesis Testing confirms that downtime and employee productivity are statistically significant, however defect rate and lead time are not significant, indicating areas for further improvement. A positive cultural change is also demonstrated, as employees report higher morale and engagement.
KW - Agile manufacturing
KW - Employee productivity
KW - Lean Six Sigma
KW - Process improvement
KW - Root Cause Analysis
KW - Statistical Analysis
UR - https://www.scopus.com/pages/publications/105020567241
U2 - 10.1007/978-3-032-05610-8_62
DO - 10.1007/978-3-032-05610-8_62
M3 - Conference contribution
AN - SCOPUS:105020567241
SN - 9783032056092
T3 - Lecture Notes in Mechanical Engineering
SP - 623
EP - 631
BT - Flexible Automation and Intelligent Manufacturing
A2 - Srihari, Krishnaswami
A2 - Khasawneh, Mohammad T.
A2 - Yoon, Sangwon
A2 - Won, Daehan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 21 June 2025 through 24 June 2025
ER -