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Found 6 results

  1. Using Six Sigma in Research and NPD. I have been researching on this topic but thought I should post the question here. Looking forward your reply at the earliest.. Regards, Muthu.S
  2. Most of us are wrongly taught that robust design (of DFSS) can only be achieved through experimental methods like DOE and Taguchi. It's a time consuming approach and it's been known for a while that it's very limited. What's more, robust design is significantly easier to understand visually and can also be done easily and quickly mathematically by simply looking at an equation. Let's see how. Consider the formula below. It is for the stiffness of a simple helical spring. We want a certain spring rate k. The question we are now faced with is: what values should we select for each of the input variables to minimise the effects of randomness (or have a robust design)? Consider the graph below. Note how when we have a smaller gradient (blue) the variability is compressed, but with a larger gradient (red) it is expanded? Well that's basically robustification: finding the smaller gradient. So now back to the spring. Let's consider each variable and it's gradient: The spring diameter D Increasing the value will quickly reduce the gradient; it is inverse and cubed The wire diameter d Decreasing it will reduce the gradient very quickly because it is quatic The modulus of rigidity G The gradient doesn't change because it is linear The number of coils n Increasing the value decreases the gradient fairly quickly; it is reciprocated Therefore, the easiest way to robustify this design is to: minimise d, maximise D, maximise n and then adjust G to provide the desired k value. Now obviously this is a simple example, and you will probably have something more sophisticated. Nevertheless, what I have explained to you here is enough so that you can now understand the mathematics of how a system is easily robustified. Without costly experiments. Remember, it is all about the gradient. So why don't we know more about these simple and easy probabilistic methods? probably because the quality industry is still dominated by by statistitians who are basically experimentalists. They key is to become familiar with principles of probabilistic design methods and robustification. Once you have, you can intuitively apply these principles. And produce quality designs with ease. If you want to improve your understanding of how probability and mathematics can help you easily improve quality and achieve six sigma, then you can learn more here. You can sign up to a free theory update about probabilistic design, download some free software and get a free sample of of an e-book on probabilistic design. Please let me know if you have any questions about this. I think a lot of people are let down by not being taught this stuff, and I really want to rectify this situation.
  3. Folks, Provide inputs: In any project , if one has to design a complete new system (no issue with the current system ofcourse), how can one calculate Return on capital investment, if the project is not intended to solve any chronical issue, but it is just to start up a new product line. Can six sigma DMADV be applied in this case?
  4. Can some one help with DMADV road map and the tools used in each phase? Thanks Madhan
  5. Offlate, I see many buisness reviews/companies are talking about " Red Ocean and Blue Ocean Strategy" ( to give a brief note, red ocean is all about competing in existing market place, beat the competition, segment existing customer etc, and where as Blue ocean talks about creating uncontested market space, make the competition irrelevant, value innovation etc. ex: invention of Ipod). An organisation, which thinks in the above lines, how do you implement six sigma methodology to help in value innovation and to make the competion irrelevant? Please share your views.
  6. Dear Sir, i am undergoing a course in embedded system design from ducat institute. please guide me how six sigma can be applicable in this field.
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