Getting Lean

John Sheehan

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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Read more: Materials Environment Drives New Challenges

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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Read more: Optimizing Design for Manufacturability Analysis

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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Read more: Evaluating the Risk and Reward of Statistical Analysis

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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Read more: Product Development and the 7 Wastes
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