Implement Industrial 4.0 into process improvement: A Case Study in Zero Defect Manufacturing

Authors

  • Nguyen Kieu Viet Que Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam Author
  • Nguyen Thi Mai Huong Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam Author
  • Huynh Tam Ha Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam Author
  • Vo Dang Nhat Huy Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam Author
  • Le Dang Quynh Nhu Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam Author
  • Minh Ly Duc Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam Author

DOI:

https://doi.org/10.47392/irjash.2023.013

Keywords:

Zero Defect, Industrial 40, DMAIC, Lean Six Sigma

Abstract

This study describes in detail step-by-step implementation of quality improvement activities in mechanical processing plants. Describe how to implement quality 4.0 technologies in DMAIC (Define-Measure-Analysis-Improve-Control) phases. As well as using statistical formulas, experimental design in data analysis at each phase of DMAIC. This study proposes to use Quality 4.0 Technology in product quality improvement activities in mechanical product processing factories with the aim of becoming zero defect manufacturing. The results of the research found are repair rate reduced from 600 PPM monthly to 0 PPM, processing capacity increased from Cpk1.02 to Cpk2.56, reduce time for inspection product from 702 hours per year (calculated to save USD 2106 per year), reduce the amount of repair products by 196 products per year (calculated in terms of money is reduced by 917 USD per year) and reduce 1 roughing stage (calculated in terms of cost reduction about 171288 USD per year). The roughness dimension has reduced measurement time by about 364 hours per year (save 1092 USD per year). Processing digital signals from sensors in an oily environment is a big challenge for researchers. Improving the security of digital data is also a limitation of this study. This study proposes a model to apply statistical hypothesis testing methods to analyze real data collected from each machining stage and perform each job according to each corresponding DMAIC phase of the model. In addition, digital processing techniques and computer vision techniques are also deployed in the improve phase to complete the goal of improving the semiautomatic production stage to the automatic production stage.

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Published

2023-02-28