Dec 24 2014
Hong Kong Power Company Holds QC Circle Convention | Quality Alchemist
CLP Power Quality Control Circle (QCC) Convention was established in 2002. It aims to offer our staff a platform to submit any creative ideas they may have to improve processes, procedures and overall operations in the form of a proposal. CLPP QCC Convention is one of key quality culture activities and HKSQ exco members were honored to be invited as guests for the Convention. Moreover, our former chairman Dr. Aaron Tong was one of judges.
Source: qualityalchemist.blogspot.com
The QC circle, born in Japan in the early 1960s and the object of a short-lived fad in the US and Europe in the 1980s, lives on as a useful tool in organizations that stuck with it, including many companies in Japan, China, India, and other Asian countries.
CLP Power has been an electrical utility serving Hong Kong for 100 years. In the jury that awarded prizes to circle projects at this convention was my friend Aaron Tong, former chair of the Hong Kong Society for Quality (HKSQ).
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