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Soft Air USA Inc. of Grapevine, Texas, is a subsidiary of Cybergun S.A., the world’s leading manufacturer of replica airsoft guns.
Airsoft guns fire 6 mm plastic balls using a low-power spring, CO 2, gas, or electric force designed to avoid the potential for injury. Many Soft Air USA products are used by the military and law enforcement agencies for training exercises. Soft Air USA has licenses from a wide variety of gun manufacturers, including industry leaders such as Smith & Wesson, Colt, Sig Sauer, IMI (Uzi), Mauser, Thompson, and Kalashnikov.
Because the real guns can’t be shipped to Asia, where the replicas are manufactured, in the past manufacturers would be forced to travel to the United States to make silicone molds of the guns, which they would then take back to their nations. However, Soft Air USA has recently devised a creative means for reducing the time required to get its licensed replica airsoft guns to market by as much as four to six weeks. The company is accomplishing this impressive feat by scanning the real guns using the laser-scanning service bureau of 3-D digitizer NVision Inc. of Southlake, Texas.
Gems Sensors & Controls, of Plainville, Connecticut, designs and manufactures a broad portfolio of liquid level, flow, and pressure sensors, miniature solenoid valves, and pre- assembled fluidic systems to exact customer requirements. The large number of configurable products, and the company’s high production volumes, create complex testing requirements. For example, many AC and DC voltages and resistance measurements need to be performed on the large number of different liquid level sensors that are built on a flexible production line.
Jeff Neuner, senior test engineer for Gems, overcame this challenge by developing an innovative testing application that scans a barcode to identify the part number and product configuration information. The test application software uses this product information to create a custom test profile. The application takes advantage of the versatility and speed of the 8845A precision multimeter from Fluke Corp., located in Everett, Washington, to handle many different part numbers and easily keep up with the production pace. The new multimeter has replaced three instruments that were required in the past, which saves floor space, simplifies test system architecture, and reduces maintenance expenses.
Would somebody please ask Mike Micklewright to stop making sense (“Why Root Cause Analysis Sucks in the United States,” http://qualitydigest.com/IQedit/
QDarticle_text.lasso?articleid=12753 )? He’s at risk of exposing a vast cottage industry of what Deming called “hacks,” and that could have incredible repercussions thereunto.
Just kidding about the hacks, of course. The real hacks, as Micklewright points out, are leaders who prefer tried-and-false methods of problem-solving. I liken it to how Congress blathers on about an issue and then passes legislation that has zero effect whatsoever, such as the “Airline Passenger Harassment Act of 2001.”
-- Jonathon Andell
I might not have chosen that title, but the article itself sums up the misconception or the inadequacy of how companies practice root cause analysis.
s a result of the ongoing credit crisis, senior managers in financial services firms are reassessing their risk management processes. This is leading to a greater emphasis on enterprise risk management (ERM) or firmwide risk management, according to “The Bigger Picture: Enterprise Risk Management in Financial Services Organisations,” an Economist Intelligence Unit survey and report sponsored by SAS Institute Inc.
The report is based on an Economist Intelligence Unit survey in July 2008 of 316 senior executives from around the world. Among the respondents, 59 percent say that the credit crisis has forced them to scrutinize their risk-management practices. A key challenge for many financial services companies is that the move to an enterprisewide risk‑management approach is lengthy and often involves a shift in corporate culture.
Executives also indicated that a lack of relevant, timely, and consistent data is preventing a wider acceptance of ERM.
“This survey confirms that financial services organizations will increasingly be looking to adopt best practice in risk management, with firmwide risk and stress testing being placed center stage,” says Allan Russell, head of global risk practice at SAS.
Electronic waste, or e-waste, is considered the fastest-growing component of the municipal waste stream worldwide. Unfortunately, those discarded computers, cell phones, and televisions, among other items, contain hazardous materials such as mercury, chromium, lead, and cadmium, to name a few. These chemicals not only have an enormous environmental effect, but also cause physical problems such as brain damage, kidney disease, reproductive disorders, and cancers. Growing legislation and standards surrounding e-waste disposal globally touch on issues such as illegal dumping, and the effect that recycling these hazardous materials has on the poor in countries such as Ghana, China, and India.
Mammoet USA Inc., the United States division of Netherlands-based Mammoet, a worldwide leader in heavy lifting and transport, brought a challenging problem to P Squared Consulting of The Woodlands, Texas. A new management team, in place about a year, had set a very aggressive goal to triple the size of the division within three years through acquisitions and organic growth. However, with the legacy culture that team members inherited, they were not sure how to achieve this goal.
Initial discussions with management and employees uncovered several overriding areas for improvement: strengthening teamwork between employees and departments, understanding and integrating international cultures, upgrading company processes and strengthening the supply chain, and creating a new culture grounded in continuous process improvement (PI), using teams as the core foundation.
Employees further noted that if anything was going to change, they needed management support and empowerment. They wanted management to guide them with a plan to improve overall company processes.
During the last 10 years, P Squared has developed and refined an approach to ensure success when an organization makes the decision to use PI to improve the productivity and the profitability of the company.
Don’t let the calculation scare you; it looks worse than it is.
Given the data in my June column, “Percentage Deceptiveness,” suppose I wanted to compare the performance of months 1-33 (survival rate of 98.6%) vs. months 34-51 (survival rate of 98.1%). I could use a p-chart analysis of means (ANOM), but because of only one decision being made, the three standard deviation limits would be very conservative. Not only that, the problem of unequal denominators negates using ANOM’s more exact limits (1.39 standard deviations) for comparing two percentages at a 5-percent significance level. In cases like this, there is a nice alternative usually available in most good statistical software packages.
You can create what is called a “2 × 2 table” as shown in figure 1.
Figure 2 shows the generic structure of such data so that you can understand the needed statistical calculation.
Regarding “Big Boxes Beware” (“First Word,” Dirk Dusharme, November 2008): If you want to experience grocery-industry customer service at its finest, visit Wegmans. Their employees will load your groceries in your car for free, provide umbrellas for trips to your car in the rain for those who don’t have one, and personally take you to the items that you’re looking for when you can’t find something.
Those needing a lesson in customer service should shop there a time or two. They will learn what service is all about.
--Chris Wallen
Hear! Hear! My boss was commenting today that he went to “Wally World” on his way to work and tried to buy light bulbs. First of all, he was looking for 100-watt bulbs and the closest that they sell now are the 90-watt kind.
When he went to check out, the register sign was lit up, but no one was there to run the register. He stepped back far enough to see two workers stocking an end cap. They looked his way three times and still went back to stocking.
On the third time, my boss said he even waved at them so that they would see movement at the register. Needless to say, when he left, the light bulbs were still on the counter.
Many have been taught that they must remove outliers prior to analysis. This is because much of modern statistics is concerned with creating a mathematical model for the data. Because all these models are created using algorithms, they tend to be severely affected by any unusual or extreme values.
Therefore, to use these mathematical techniques to obtain useful and appropriate models, it’s often necessary to polish up the data by removing the outliers. However, the act of building a model implicitly assumes that the data are homogeneous enough to justify the use of a model.
For example, the histogram in figure 1 has a bell-shaped curve superimposed. This curve is based on the average and standard deviation statistic for all 100 values in the histogram. It’s neither wide enough nor tall enough to provide a good fit to the data. The histogram in figure 2 contains the 93 values left after the seven extreme values (the four lowest and three highest) were deleted. Now the curve based on the average and the standard deviation statistic does a much better job of fitting the data. Thus, it’s true that outliers can undermine our efforts to create a model for our data.
E-Waste and the Standard That Loves It
