Track: Six Sigma

Abstract

With the fourth industrial revolution, the increased trend of digitalization of production processes and products resulted in more data regarding processes and products becoming available. For data-driven strategies like Lean Six Sigma to deal with the amount of data available today, it requires integrating a new set of tools and techniques from the Data Science research field. However, the fact that more data is available only sometimes translates into more efficient and effective processes.. For that, it is necessary to identify what is useful and can be transformed into value and what is not. Following the DMAIC cycle, the project presented in this article focuses on improving a production station of an Optical Bonding production line that produces flat panel displays for the automotive industry. The inclusion of Data Science tools and techniques tools in this improvement project helped extract and analyze information from unstructured log files. The achieved results show that combining data science tools with traditional process improvement tools and methodologies is very productive.