Six sigma - About Six Sigma



Six Sigma Green Belt Flowchart

 Six Sigma Explanation:


        If Six Sigma is so great, where has it been hiding all these years? Like most great inventions, Six Sigma is not all "new". It combines some of the best techniques of the past with recent breakthroughs in management thinking and plain old common sense. for example, Balanced Scorecards are a relatively recent addition to management practices, while many of the statistical measurement tools used in Six Sigma have been around since the 1940s and earlier.


        The term "Six Sigma" is a reference to a particular goal of reducing defects to near zero. Sigma is the Greek letter statisticians use to represent the "standard deviation of a population." The sigma, or standard deviation, tells you how much variability there is within a group of items (the "population"). The more variation there is, the bigger the standard deviation. You might buy three shirts with the "same" sleeve length only to discover that none them are exactly the length printed on the label: two are shorter than the stated length, and the other is nearly an inch longer-quite a bit of "standard deviation".


        In statistical terms, therefore, the purpose of Six Sigma is to reduce variation to achieve very small standard deviations so that almost all of your products or services meet or exceed customer expectations.


 Customer Requirements and Variation:


        Traditionally, businesses have described their products and services in terms of averages: average cost, average time to deliver a product, and so on. Even hospitals have a measure for the average number of patients who pick up a new infection during their stay. Trouble is, averages can hide lots of problems. With the way that most processes operate today, if you promise customers to deliver packages within two working days of getting their order, and your average delivery time is two days, many of the packages will be delivered in more than two days having an average of two days means some packages take longer and some take less. If you want all packages to be delivered in two days or less, you’ll have to dramatically eliminate problems and variations in your process.


         Here’s an example from The Six Sigma Way: you want your “drive to work” process to produce defects (early or late arrivals) no more often than 3.4 trips out of every million trips you make. Your target arrival time at work is 8:30 a.m., but you’re willing to live with a few minutes either way, say 8:28 to 8:32 a.m. Since your drive normally takes you 18 minutes, this means your target commute time is anywhere between 16 and 20 minutes. You gather data on your actual commute times, and create a chart like that shown in Figure 1.


         There will always be some variation in a process: the core issue is whether that variation means your services and products fall within or beyond customer requirements. If you want to be a Six Sigma commuter, the problem is that your process produces a lot of defects (late or early arrival times).


         So you set about improving your process. You find the route that is most reliable (has the least traffic and fewest stop lights), you get up when your alarm clock first goes off, you recalibrate your cruise control, etc. After all your changes have been implemented, you gather more data. And voila, you have become a Six Sigma commuter. The new standard deviation of just 1/3 of a minute means the variation in your process practically guarantees that you will always arrive within 16 to 20 minutes of leaving your house (see Figure 2).


         This example has direct meaning for the business world. If we promise on time airline departures, but actual departures vary from 5 to 30 minutes late, customers will understandably be angry and take their business elsewhere (except it might be hard to find an airline that does that well!). And an electric toaster that toasts the bread today but burns it tomorrow at the same darkness setting will find its way back to the store, along with an unhappy purchaser.


        What happens when we achieve Six Sigma performance? For a Six Sigma commuter, it means predicting commute time very precisely every day. And a “defect” a commute taking less than 16 or more than 20 minutes would happen only 3.4 out of every 1 million commutes (may we live that long!).


Defects and Sigma Levels


       One virtue of Six Sigma is that it translates the messiness of variation into a clear black-or-white measure of success: either a product or service meets customer requirements or it doesn’t. Anything that does not meet customer requirements is called a defect. A hotdog at the fair with mustard is a defect if the customer asked for ketchup. A rude reception clerk is providing defective service. A bad paint job on a new car is a defect; a late delivery is a defect; and so on.


      If you can define and measure customer requirements, you can calculate both the number of defects in your process and outputs as well as the process yield, the percentage of good products and services produced (meaning they are without defects). There are simple tables that let you convert yield into sigma levels. 

[Ari's Hint:  Yield

                 Yield of a process is defined as “The percentage of defect free items out of the total items produced in that process”. In other words

For example,

                If, in a cooking-gas filling plant, 50 cylinders are sent-in for filling gas to a pre-fixed weight of 20kg, and if 5 cylinders are rejected as under or over weight, then the yield of the gas filling process is (45/50)*100=90%.

           However, readers may please note that in the above example, the good ones (is 45 cylinders) consisted of any reworked cylinders or not is not known to us. This is important as we are sometimes interested in not only the yield of a process but also what is called the First Time Yield (FTY) of a process which is a more robust indicator of the performance of  a process. In other words

First Time Yield (FTY) = [ (in - defective items - reworked items - scrap) / in ] * 100

In the same example given above,             

in= number of items sent-in = 50

reworked items = not known or 0

scrap = not known or 0

defective = 5

Therefore, First Time Yield = [(50 - 5 - 0 - 0)/50]*100 = 90%

However, the first time yield would be different if, out of 45 good ones, 4 were reworked cylinders and then accepted:


First Time Yield = [(50 - 5 - 4 - 0)/50] * 100 = 82% which is less than the generally reported yield

In real life situations many process owners hide the reworked items and report a high % of yield. As a manager, we are more interested in knowing the first time yield than simply the yield because of the fact that first time yield reveals the number of reworked items and makes the "hidden factory" obvious.]

[Many such hints/explanations are available on various such hot topics of six sigma through out the course to make the online study more interesting and friendly for self learning.]  

      Another approach to determining a sigma level is to calculate how many defects occur compared to the number of opportunities there are in the product or service for things to go wrong. The outcome of this calculation is called Defects per Million Opportunities (DPMO), which is another way to calculate the Sigma Level or yield of a process.



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