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[Data Science Utilization Case] Improvement and Automation of Inspection Processes 2

I want to reduce investment in inspection equipment! Examples of improving throughput in functional testing.

We would like to introduce a case study on the improvement and automation of inspection processes in the manufacturing industry. There was a challenge to reduce the number of functional inspections conducted multiple times on production lots of electronic components, improve throughput, and limit investment in inspection equipment. To address this, we used information from the first inspection to predict the results of the second inspection using AI. Lots that were deemed sufficient after the first inspection were stopped there, thereby improving throughput in functional inspections. 【Case Overview】 ■Industry: Manufacturing ■Business: Quality Inspection ■Challenges: Production Efficiency, Operational Efficiency, Quality Assessment ■Analytics/AI Solution - By stopping inspections for lots deemed sufficient after the first inspection, we improved throughput in functional inspections. *For more details, please refer to the PDF document or feel free to contact us.

  • Company:TDSE
  • Price:Other
  • Business Intelligence and Data Analysis

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[Data Science Utilization Case] Improvement and Automation of Inspection Processes 1

Identify and improve the factors causing errors! Examples of simplification and omission of relevant inspection items.

We will introduce examples of improvements and automation in the inspection process within the manufacturing industry. There was a challenge to reduce the frequency of inspection errors by discovering and improving the factors in the manufacturing process that lead to inspection errors for electronic components, as well as to omit or simplify inspection items to enhance production efficiency. To address this, we modeled the relationship between the error occurrence rate for each inspection item and the measured and controlled values of the manufacturing process, identifying and improving the factors that lead to errors, which allowed us to simplify or omit the relevant inspection items. 【Case Overview】 ■Industry: Manufacturing ■Business: Quality Inspection ■Challenges: Improving inspection efficiency, enhancing quality, factor analysis ■Analytics and AI Solutions - Modeling the relationship between error occurrence rates and the measured and controlled values of the manufacturing process - Identifying and improving the factors leading to errors, simplifying or omitting the relevant inspection items *For more details, please refer to the PDF document or feel free to contact us.

  • Company:TDSE
  • Price:Other
  • Business Intelligence and Data Analysis

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