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Why Sacrifice Throughput for Change-Over Time?
Wednesday, March 14, 2012 | Courtesy of Assembleon

The struggle to survive in the world of manufacturing is prompting many volume-driven manufacturing sites in the West to specialize in small-batch manufacturing. The aim is to bring value to their customers with fast-turnaround prototyping and reduced introduction times. Pressures are constantly increasing to reduce response times, though, even during peak production periods. It is therefore crucial to manufacture as efficiently as possible by eliminating the time consuming elements in a high-mix environment. Mostly, that means trading in machine throughput for more, and time consuming, change-overs. As change-over is a major contribution to machine idle time, the machine design determines how that affects the throughput and machine performance in a high-mix environment.

For high-volume, small-batch manufacturing, product diversity is also a challenge and often overseen by just one or two operators. When asked what is the most time consuming element in high-mix manufacturing, the answer will be "setting up feeders." With the minimum number of operators, the aim is to have the fewest setup changes per shift. However, the machine setup determines three things: How effectively your products come off the line; the predictability of your output (can you deliver on time?); and the reliability of  your customer quotes. With the right machine design, there is no real reason to trade in throughput for change-over flexibility.

Predictable Throughput in Small-Batch Manufacturing

to on-time delivery mean that your manufacturing line must deliver what has been promised. Equipment that is not producing is wasting time and if your estimates are regularly out by 10% (or more) then you will have 10% less business than predicted.

Predictability and throughput depend on two things:

1. The output calculated by optimizers must be very close to reality.
2. Machine idle time should be reduced to a minimum.

Small-batch manufacturing may also imply just enough components and boards to produce the batch. That means pick-and-place quality must also be assured to avoid component shortages or reduced yield as a result of poor placement/printing quality.

A setup strategy can range from structured (and balanced) all the way through to a fairly random one (the "just put that feeder anywhere" approach). The more variable (or random) a setup, the more the throughput will be affected by unbalanced loads on individual modules and therefore unbalanced cycle times for those modules. Depending on the type of equipment, the varying mix of components amongst jobs in a random setup determines how machine heads are used. Poor head usage can produce seriously de-rated output. How predictable will it then still be?

Optimizer Predictability

The fewer variables an optimizer needs to work with, the higher the predictability in practice. Variables that influence the outcome are:

  • Collect sequence;
  • Place sequence;
  • Total travel path;
  • Type of head;
  • Number of nozzle exchanges required;
  • Component mix;
  • Alignment method;
  • Component handling class and speed classes;
  • Setup optimization/strategy; and
  • Servicing of the production line, making sure all modules run with same software behaviour.

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