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.

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Data-Driven Lean Six Sigma: Enhancing Agility and Productivity in Die Maintenance Process

  • Rasoul Rashidifar,
  • Matthew Silvas,
  • F. Frank Chen,
  • Nafiseh Ebrahimi,
  • Mohammad Shahin,
  • Peng Cheng

摘要

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.