GETTING LEAN
Facilitating Continuous Improvement with IT Tools
How an app approach to data analysis cut initial data formatting time and sped defect resolution.
A key tenet of Lean manufacturing is to reduce variation through process standardization and control. To that end, most companies develop a control plan and monitor various steps of the process. The data collected in those monitoring activities are also useful in facilitating continuous improvement activities. This is particularly true as automated data collection technology has evolved and made it easier to share across multiple platforms.

For example, SigmaTron’s team in its Suzhou facility uses a combination of enhanced inspection equipment, a proprietary manufacturing execution system (MES) and a newly created IT tool to drive continuous improvement efforts.

These efforts build on a Lean manufacturing approach that includes design for manufacturability (DfM) recommendations made to eliminate defect opportunities prior to the new product introduction (NPI) process and use of a production part approval process (PPAP) methodology during the NPI process.

Since the facility’s focus is predominately higher volume production, its SMT lines are optimized to include a higher level of in-process inspection, utilizing 3-D solder paste inspection (SPI) following paste or glue deposition and automated optical inspection (AOI) both pre- and post-reflow. The MES collects yield data at those points and during in-circuit and functional test. The MES also tracks assemblies through each production step in the routing to support traceability requirements.

The thoroughness of this approach provides the facility’s quality team with trends data needed to drive a robust continuous improvement process. The company’s Taiwan-based IT team has built a trends analysis database app, known as the PDCA tool. Built around Edward Deming’s Plan, Do, Check, Act framework, the tool supports trends analysis, continuous improvement activities and corrective and preventive action (CAPA) tracking. It tracks first-pass yield (FPY) by customer, group or model number. It also tracks major defect by process, and its raw data enable tracking of defects by part type. It sends emails to relevant team members when a defect exceeds the control plan limits to signal the need to open a PDCA project. Its summary section can provide FPY information by week, month or year.

In initiating corrective action, the team starts in the Plan phase, performing failure analysis, planning for the corrective action and setting a goal to achieve. In the Do phase, the team implements the corrective action plan. In the Check phase, the team reviews process data such as first-pass yield to determine if the corrective action reduced the identified defect and if any new defects have appeared. In the Act phase, the team documents the corrective action to ensure the process change is standardized across all relevant processes. The PDCA tool tracks this activity and provides dashboards that incorporate yield trends and defect pareto analysis.

In a controlled process, the root cause of drops in yields can often be difficult to identify. A recent project illustrates the benefits of the PDCA process in rapidly identifying these issues. The cumulative FPY failure rate on an SMT printed circuit board assembly (PCBA) had climbed to 1.23%. The team opened PDCAs on the top two defects: misaligned components and lifted parts. Their analysis determined the root cause of the misaligned components was an issue with a pick-and-place machine. A smooth part package was not being uniformly placed because the nozzle pressure was insufficient to hold it in place, and placement speed was contributing to the problem. The pressure and placement speed were adjusted, and yield improved. That process change was documented to be incorporated in future production activities involving that part. In the case of the lifted parts, analysis showed the part was not performing to specification under reflow. The vendor was contacted. It appeared to be a transitory issue, as it did not show up in other production lots. The result of both corrective actions was combined FPY failure rates dropped to 0.61%. The team is now analyzing a new top defect list to achieve further improvement.

This type of app approach to data analysis automates much of the initial data formatting, so quality and production personnel can quickly get the data they need. This simplifies the process of eliminating defect opportunities and is a necessary part of the journey to zero defects.

Hom-Ming Chang headshot
Hom-Ming Chang
is vice president China operations at SigmaTron (sigmatronintl.com); homming.chang@sigmatronintl.com.