This study aimed to optimize tissue quality control through Lean Six Sigma by designing and developing an automated image processing-based defect detection system for a tissue converting company. The study focused on improving the inspection process before the bundling stage, where packaging defects were previously detected only after products had been bundled, resulting in reinspection, rework, and quality-related costs. The Design for Six Sigma (DFSS) methodology using the DMADV (Define–Measure–Analyze–Design–Verify) framework was employed to identify Critical-to-Quality (CTQ) requirements, evaluate the existing inspection process, determine root causes of inspection inefficiencies, develop the proposed inspection system, and verify its effectiveness. Process mapping, time study, statistical process control, capability analysis, root cause analysis, and ISO/IEC 25010 software quality evaluation were utilized. The proposed system reduced the inspection cycle time from 1.463 to 0.649 seconds per unit, increased the defect detection rate from 88.90% to 99.59%, and decreased the average daily quality-related cost from ₱708.46 to ₱15.29. The software obtained an overall ISO/IEC 25010 evaluation mean of 3.44 (Strongly Agree). The findings demonstrate that the proposed image processing-based inspection system significantly improved inspection efficiency, defect detection capability, and cost performance, making it a practical and economically viable solution for enhancing tissue packaging quality control.

Keywords: Lean Six Sigma, DMADV, image processing, automated visual inspection, defect detection