AI-Enabled Electronics Manufacturing: Applications in Quality and Root-Cause Analysis
AI-Enabled Electronics Manufacturing: Applications in Quality and Root-Cause Analysis
How AI can help electronics manufacturing teams use data, knowledge, and experience to make more informed operational decisions
Electronics manufacturing teams are under increasing pressure to improve quality, reduce defect escapes, respond quickly to customer requirements, and make better day-to-day decisions across complex manufacturing operations. At the same time, the information needed to make those decisions is often distributed across inspection systems, process logs, equipment data, engineering specifications, FMEAs, corrective-action records, customer requirements, and other technical documentation. The challenge is not simply having more data; it is turning fragmented information and technical knowledge into practical, trustworthy decision support.
Mukund Shenoy, Ph.D. will explore practical applications of artificial intelligence in electronics manufacturing, focusing on quality engineering, root cause analysis, process knowledge, and manufacturing operations. The session will discuss how AI-enabled approaches can help manufacturing teams identify patterns, retrieve relevant technical information, connect process observations to possible causes, and support more consistent engineering, quality, and operational decisions.
The webinar will include high-level examples from surface-mount assembly and semiconductor/electronics manufacturing to illustrate how AI can support investigation and decision-making in complex production environments. These examples will emphasize the importance of combining data-driven methods with domain knowledge, process understanding, expert judgment, and human-in-the-loop validation. Rather than positioning AI as a replacement for engineers or manufacturing experts, the session will present AI as a practical decision-support capability that helps teams organize evidence, improve traceability, and accelerate learning across manufacturing operations.
Attendees will leave with a practical understanding of where AI can create value in electronics manufacturing today, which implementation challenges to consider, and how organizations can begin moving from disconnected data and documents toward more informed and effective manufacturing decisions.
Speaker Bio:
Mukund Shenoy is a manufacturing and quality engineering professional with more than 25 years of experience in semiconductor technology development, manufacturing operations, industrial engineering, equipment quality, Industrial Internet of Things, and strategic sourcing. He spent over two decades at Intel, where he worked across advanced technology-development and manufacturing organizations, with responsibilities spanning process improvement, quality systems, equipment and supplier quality, data-enabled manufacturing, and operational execution.
Mukund recently completed his Ph.D. at Arizona State University, where his research focused on AI-enabled approaches to root cause analysis and zero-defect manufacturing in semiconductor and electronics assembly. His work explores how manufacturing data, process knowledge, engineering specifications, and expert judgment can be integrated to support more explainable and actionable quality decisions.
He is also a Lean Six Sigma Black Belt and ASQ Certified Quality Engineer, with professional interests spanning AI for manufacturing, electronics assembly quality, root cause analysis, knowledge-based decision support, and the practical deployment of advanced analytics in manufacturing operations.