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Getting Lean

Hom Ming Chang

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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John Borneman

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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Jerry Johnson

Striking the right balance between costs and cycle time.

Decisions made in product design can impact assembly cost, defect opportunities and inventory cost. While design for manufacturability (DfM) analysis can eliminate many issues, less commonly analyzed decisions related to cost targets, scheduling and work team assignments can have unintended consequences that generate unacceptable levels of waste.

Lean manufacturing practitioners are aware of Taiichi Ohno’s concept of the seven wastes (muda) in manufacturing as part of the Toyota Production System (TPS). To recap, those seven wastes are:

  1. Waste of overproducing (no immediate need for product being produced).
  2. Waste of waiting (idle time between operations).
  3. Waste of transport (product moving more than necessary).
  4. Waste of processing (doing more than what is necessary).
  5. Waste of inventory (excess above what was required).
  6. Waste of motion (any motion not necessary outside of production).
  7. Waste of defects (producing defects requiring rework).

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Tom Rovtar

Leveraging the IT department to reduce operation-caused variation.

One continuing trend in electronics manufacturing services is the increasing role IT-related solutions have in supporting a Lean manufacturing-driven organizational culture. This is particularly true of proprietary solutions that automate processes in ways that minimize normally occurring variation or help eliminate non-value-added activity.

One example of this is SigmaTron International’s proprietary Manufacturing Execution System (MES) system known as Tango, whose Phase III system went live at the EMS company’s Elk Grove Village (IL) facility in June. The overarching goal of Tango is to centralize tools used throughout the company for production management, while adding enough flexibility via customization to address facility-specific or customer-specific situations.

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System strategies and the visual factory can handle rapid changes in demand.

Supply-chain disruption and Lean philosophy rarely go hand-in-hand. In some cases, however, systems created to support Lean manufacturing or principles themselves help mitigate the chaos the pandemic has created in the global supply chain.

SigmaTron has operations in the US, Mexico, China and Vietnam. As a result, we had a bird’s-eye view of the initial impact on manufacturing operations in China and used that as a roadmap for preparing operations in other locations for disruption, along with best in-plant practices for disease mitigation. While the ways different jurisdictions reacted to Covid-19 varied, the issues were somewhat similar. This column looks at some lessons learned in that process from this contract manufacturer’s perspective.

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Filemon Sagrero

How Lean Six Sigma prepares workers for tomorrow’s workplace.

People outside of manufacturing often imagine that technology’s next step is to turn factories entirely over to robots. While factory automation is growing by leaps and bounds, the reality is most automation is paving the way for workers to be far more involved in critical decision-making on the factory floor. Just as Industry 4.0 is the driving force behind smarter machines that automatically analyze and adjust processes as they inspect product, Lean Six Sigma is paving the way for a smarter workforce, capable of analyzing production trends and optimizing processes.

The benefit of Lean manufacturing philosophy is a holistic focus on eliminating issues that create bottlenecks, defects and wasted effort. It aligns well with an Industry 4.0 vision, since greater levels of automation help drive reduced variation, and eliminate excess handling and errors related to manual processing. However, while a Lean vision helps drive efficiency and improve throughput, factories with a lot of product variation, as is found in contract manufacturing, do develop inefficiencies that need to be addressed. Six Sigma provides a powerful methodology and toolbox for addressing these inefficiencies. Implemented correctly, it creates problem-solving discipline that teaches production teams how to make good choices in the problems they choose to solve, thoroughly analyze root cause, test their preferred solution and make sure the improvement is sustainable over time.

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