Integrating Production and Repair Operations Through Data Driven Defect Flow Analytics for Yield and Resource Optimization in PCBA Manufacturing

Authors

• Ali Tahmasebi, Binghamton University--SUNYFollow
• Yingge Zhou, Binghamton University--SUNY
• Daryl Santos, Binghamton University--SUNY
• Sai Srikanth Reddy Kolli
• Edward Wong

Author ORCID Identifier

0009-0005-2793-2267

Document Type

Conference Proceeding

Publication Date

Summer 6-9-2026

Keywords

Data Analytics, PCBA Manufacturing, Defect Flow Analysis, Repair Operations, Digital Transformation

Abstract

In Printed Circuit Board Assembly (PCBA) manufacturing, maintaining a closed loop connection between production and repair operations is critical to improving product quality and operational efficiency. However, in the manufacturing environment that motivated this work, production and repair data are often managed separately, and repair findings are not systematically fed back to production teams, limiting visibility into defect routing patterns and repair workload drivers. This study developed a data-driven framework that integrates production and repair data streams to analyze defect flow and support repair resource planning. The framework applies descriptive statistics, cross tabulations of repair inflow by product/project and repair stage, and temporal trend analysis to characterize in-line repair demand and downstream transfers to the offline repair/disposition area, referred to as the Not Good Warehouse (NGWH). Using historical production output and repair records, the analysis quantifies the proportion of boards entering in-line repair and the proportion transferred to NGWH, providing actionable feedback for prioritizing improvement efforts and informing planning for in-line benches, material handling (e.g., transport carts), and staffing. Results demonstrate that structured defect flow analytics can identify products and operational areas that contribute disproportionately to repair workload and downstream transfers, patterns that may be overlooked when relying on yield metrics alone. The proposed approach supports data-informed decision making for defect reduction, yield improvement, and production repair synchronization, aligning with Industry 4.0 initiatives for smart and connected manufacturing systems.

Publisher Attribution

© IEOM Society International

Recommended Citation

Tahmasebi, Ali; Zhou, Yingge; Santos, Daryl; Kolli, Sai Srikanth Reddy; and Wong, Edward, "Integrating Production and Repair Operations Through Data Driven Defect Flow Analytics for Yield and Resource Optimization in PCBA Manufacturing" (2026). Systems Science and Industrial Engineering Student Scholarship. 4.
https://orb.binghamton.edu/systems_students/4