Geneci taguchi

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Geneci taguchi

  1. 1. Genichi Taguchi (January 1, 1924 – June 2, 2012) Presented by : REVA BM 012125
  2. 2. EARLY LIFE  Taguchi was born and raised in the textile town of tokamachi in Niigata prefecture  In 1950, he joined the Electrical Communications Laboratory (ECL) when statistical quality control was beginning to become popular in Japan, under the influence of W. Edwards Deming , he collaborated widely and in 1954-1955
  3. 3.  Genichi Taguchi is well known for developing a methodology to improve quality and reduce costs.  In the United States, is referred to as the Taguchi Methods. he also developed the quality loss function.
  4. 4. LOSS FUNCTION  The Taguchi Loss Function is graphical depiction of loss in which taguchi describe a phenomenon affecting the value of products produced by a company.  Traditionally, companies measure quality by the number of defects or the defect rate. In this system, defects are identified through inspections of the materials and products. Upper and lower quality limits are established. Everything that does not fall within the limits is considered a defect.
  5. 5.  In this approach, the closer to the target value, the better.  It does not matter whether the deviation is above or below the target value.  Under this approach the deviation is quadratic.
  6. 6. Three things can be summarise by taguchi’s philosophy:  We can improve quality without increasing cost.  We can reduce cost by improving quality.  We can reduce cost by reducing variation. (When we do so, performance and quality will automatically improve.)
  7. 7. Uses of quality loss function 1. Reduces Costs: There are three ways that managers can use QLF to reduce costs.  Move the average of the actual distribution closer to the target value.  Reduce variability.  Do a combination of both. 2. Setting Specific Limits  The data from the quality loss function can be used to determine where limits should be set to help minimize losses.
  8. 8. Orthogonal array  Taguchi's orthogonal arrays are highly fractional orthogonal designs  These designs can be used to estimate main effects using only a few experimental runs.  These designs are not only applicable to two level factorial experiments, but also can investigate main effects when factors have more than two levels
  9. 9. a) shows the design, (b) shows the 2 design with the defining relation and (c) marks the columns of the L4 array with the corresponding columns of the design in (b)

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