YIELD & DEFECT ANALYSIS - IMPROVEMENT
*The dataset used in this project contains confidential company information and cannot be shared publicly*
Over View
This project aimed to improve product yield and reduce defect rates in one of the company’s production lines. Using the DMAIC approach, I analyzed production data to identify root causes of defects and implemented data-driven actions to enhance process performance.
Project Process
-
Define: Identified the main issue — low product yield and high defect rate in a specific product line.
-
Measure: Collected production data, including output quantity, defect types, machine, shift, and operator information.
-
Analyze: Conducted Exploratory Data Analysis (EDA) in Excel to find patterns, correlations, and key factors contributing to defects.
-
Improve: Proposed process improvements and operational adjustments based on analytical findings.
-
Control: Created monitoring visuals (trend charts and Pareto diagrams) to track production performance after improvements were implemented.
My Role
I was responsible for the entire process from planning to result presentation:
-
Planned and executed data collection from production records.
-
Processed and analyzed data using Excel to identify insights and root causes.
-
Presented findings and improvement recommendations to management teams.
Result
The project successfully improved product yield by approximately 4% and reduced defect rate by 2% within 3 months.
Note: The dataset used in this project contains confidential company information and cannot be shared publicly.
