Happy’s Essential Skills: Design for Manufacturing and Assembly, Part 2


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Editor's Note: This is the second part of  Happy’s Essential Skills: Design for Manufacturing and Assembly column, CLICK HERE for the first part.

The Role of Metrics For Predicting and Planning

Metrics are data and statistically backed measures, such as T-downs, Connectivity, Wiring Demand, Wd. These measures can be density, connectivity or in this context, producibility. These measures are the basis for predicting and planning. When  used in the design process, there are three categories of measures applied to a product. Only the metrics can be shared by all in the design team. The non-metrics provide little assistance in design:

Metrics

  • Metrics: Both the product and the process are measured by physical data using statistical process control (SPC) and total quality management (TQM) techniques. (Predictive Engineering Process)
  • Figure of Merit: Both the product and the process are scored by linear equations developed by consensus expert opinion. (Expert Opinion Process)

Non-Metrics

* Opinion: Opinion, albeit from an expert, is applied after or concurrent with design. (Manufacturing Engineering Inspection Process)

* No Opinions: No attempt to inspect or improve the design is done during the specification, partitioning, or design stage. (Over The Wall Process)

Metrics also establish a common language that links manufacturing to design. The "producibility scores" form a non-opinionated basis that allows a team approach that results in a quality, cost competitive product (Figure 8). Metrics allows design to progress through the “Engineering Design Ages.”

  • Age of Anarchy (60%): anything goes.
  • Age of folklore (25%): Wisdom is passed from one generation to another, over pizza and beer.
  • Age of Methodology (10%): The way DFM is to be  engineered is documented, and is actually done  that way, using Figures of Merit.
  • Age of Metrics (4%): Both the product and the process are measured in standard ways.
  • Age of Engineering (1%): Productivity is achieve through continuous quality improvement, much  like it is in manufacturing.

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Figure 8: The benefits of metrics as a common design language.

The strategy in applying these measures is shown in Figures 9 and 10. The analysis process is unique to every individual and company, but certain conditions have to be met and considered if the product is going to be successful. If the score meets producibility requirements then select this approach, if not, then evaluate other opportunities and repeat the process. In the rest of this column, measures and metrics will be introduced that provide insight  for layout, fabrication and assembly planning.

Non-Metrics

It is always preferred to have metrics when discussing producibility. But if metrics are not available, then the opinions of experts are better than nothing (no opinions). The problem with opinions is that they are difficult to defend and explain and, when used in conjunction with producibility, many times they vary with each person. Sometimes, the opinion process is implemented for good intentions by taking experienced production experts and having them review a new design. This is the expert opinion process and although it is sometimes successful, it is difficult to replicate and many times results in building barriers between manufacturing and design. That is why the Figure Of Merit Process is so popular. For a small amount of work by experts it produces a scoring procedure that can be used and understood by all. This was detailed in the last column, #9.

13_HappyFig9.jpg 

Figure 9: Process using “use of measures and metrics” to match a company’s design process to its manufacturing process and support culture.

14_HappyFig10.jpg 

Figure 10: Process to develop a Manufacturability Metric as a substitute for opinions.

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