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.

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Needed: Methods to best predict and adjust to demand spikes.

Any supply-chain management executive will likely tell you that 2021 is 2020 on steroids. Reason: While 2020 had supply-chain disruption, the worst part of that disruption was followed by drops in customer demand due to Covid-19-related lockdowns, so the situation never worsened beyond spot shortages or transportation delays. This year, pent-up consumer demand combined with historic low interest rates supporting consumer spending is spiking product demand in multiple industries as consumers make purchases they delayed in 2020. 5G infrastructure is rolling out, demand has increased for electric vehicles, which have substantially more electronic components per car, and Covid-19 continues to drive higher medical equipment production. As a result, demand variations are changing schedules weekly. At the same time, constraints developing in the materials market are driving higher prices and longer lead-times. Transportation and freight resources are stretched, and pricing and lead-times are increasing. Covid-19 continues to cause some level of disruption as hot zones develop around the world. In short, 2021 will be a year where multiple variables are constantly in flux.

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A near real-time feedback loop between layout and assembly.

Two core tenets of Lean manufacturing philosophy are eliminating defect opportunities and minimizing process variation. Consequently, most companies embracing Lean principles do some form of design for manufacturability (DfM) analysis to identify manufacturability issues either during design or in the new product introduction phase. In some cases, this is an automated feature of design software. In other cases, this is done manually.

SigmaTron has adopted a hybrid process that uses software automation to speed basic analysis, followed by an engineering review. This E-DFM software tool reduces the time it takes to create a detailed report from several days to a few hours and works with SigmaTron’s existing Valor software platform.

Automating the process improves efficiency, since the engineering team reviews the automatically generated reports and suggests solutions for accuracy instead of individually performing a full analysis themselves. They then can make suggestions to further optimize the recommendations, as needed. The tool has been customized from industry-standard PCBA design rules and SigmaTron’s equipment/process-specific manufacturing guidelines, so it reflects equipment and process constraints.

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Questions to ask before action is taken.

Most who perform statistical analyses that guide organizations to solve problems do not have advanced degrees in statistics. We’ve attended classes at university, engaged in varying levels of Six Sigma training, or conducted self-study.

But I think it is safe to say we all have learned that statistically evaluating a set of data is complicated and rife with uncertainty. We choose among many possible statistical tools, and numbers “pop” out telling us if our hypothesis is correct. From those data, we proceed to either take an action or not take an action, depending on the statistical results.

Yet how many finish an analysis and wonder what if it is wrong? Did I have enough data?  Did I choose the proper statistical tool? Do I even know the proper statistical tool? Arghh! (I suspect doctors of statistical science also have “arghh” moments.)

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