Jan 14 2015
Not Exactly Poka-Yoke and Chaku-Chaku
Source: www.youtube.com
An interesting video, but “Poka-Yoke” and “Chaku-Chaku” don’t mean what the narration says they do. And they are not “Japanese” methods but methods invented by specific individuals in specific companies that happened to be in Japan. Likewise, the assembly line is not an “American” method but a method invented by P.E. Martin, Charles Sorensen and others at Ford.
“Poka-Yoke” doesn’t just mean “correct.” More specifically, a Poka-Yoke is a device integrated in the production process to prevent human error or detect it immediately without adding any labor. Checking bar codes on parts, as shown in a video, doesn’t qualify as a Poka-Yoke because it adds labor, and error prevention devices that add labor are ineffective because they are by-passed under pressure.
The video shows an operator attending to a sequence of tasks and calls it “Chaku-Chaku.” There is, however, ,more to Chaku-Chaku than this, such as automatic processing at each station, with automatic unloading and chutes between stations, so that the work of the operator is focused on checking the part after an operation and loading it into the next.
See on Scoop.it – lean manufacturing
Jan 16 2015
The World’s Most Dangerous Job? | James Lawther
“You shouldn’t believe everything you read on the internet, but according to some of the more reliable sources, during World War II:
Source: www.squawkpoint.com
This is a great story both about effective visualization of series of events in space-time and about proper interpretation in the face of sample bias.
Manufacturing, thankfully, is less dangerous than flying bombers in World War II was, but it is still more dangerous than it should be. Posting the locations of injuries on a map of the human body is also an effective way to identify which body parts are most commonly affected, and which safety improvements are most effective.
But are all injuries reported? Many organizations blame the victims for lowering their safety metrics, and discourage reporting. As a consequence, we can expect under-reporting and a bias towards injuries severe enough that reporting is unavoidable.
If you get data on an entire population, or if you thoughtfully select a representative sample, you can avoid bias, but many of the most commonly used samples are biased, often in ways that are difficult to figure out.
Customer surveys of product quality, for example, are biased by self-selection of the respondents. Are unhappy customers more likely to take the opportunity to vent than happy customers to praise? If so, to what extent? The effect of self-selection is even stronger for posting reviews on websites.
See on Scoop.it – lean manufacturing
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By Michel Baudin • Blog clippings 2 • Tags: Quality, Safety, Sample bias, Sampling, Statisics, Visualization