A leading manufacturer of large blades for wind turbines faced significant variability in paint thickness across seven different manufacturing plants. This variation affected paint consumption and product quality, necessitating an optimization of the painting process to meet design requirements and control costs.
MetaExpert Stanislav led a Six Sigma DMAIC project to address the variability in paint thickness. The project involved analyzing paint thickness distributions, providing feedback and training to painting teams, and optimizing the manual painting process at each plant. This initiative also laid the groundwork for the future implementation of robotic painting.
The project resulted in a more uniform paint thickness across all plants, significantly reducing paint consumption. Each plant achieved an average reduction of 1 to 1.5 metric tons of paint per year, enhancing cost-efficiency while meeting the required quality standards.
Not explicitly calculated, but the reduction in paint consumption per plant translates to substantial cost savings and improved resource utilization.
The project successfully standardized the painting process across multiple plants, demonstrating the effectiveness of Six Sigma tools in reducing variability and improving operational efficiency. This achievement not only optimized current processes but also provided a solid foundation for future advancements in robotic painting.
A six-month part-time project focused on variability reduction and process optimization in the production of wind turbine blades.
MetaExpert skills needed: Six Sigma, DMAIC, process optimization, quality control, team training.
Contract Length: 6 months, part-time alongside other responsibilites
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