The Taguchi loss function is graphical depiction of loss developed by the Japanese business statistician Genichi Taguchi to describe a phenomenon affecting the value of products produced by a company. Praised by Dr. W. Edwards Deming (the business guru of the 1980s American quality movement),[1] it made clear the concept that quality does not suddenly plummet when, for instance, a machinist exceeds a rigid blueprint tolerance. Instead 'loss' in value progressively increases as variation increases from the intended condition. This was considered a breakthrough in describing quality, and helped fuel the continuous improvement movement.
The concept of Taguchi's quality loss function was in contrast with the American concept of quality, popularly known as goal post philosophy, the concept given by American quality guru Phil Crosby. Goal post philosophy emphasizes that if a product feature doesn't meet the designed specifications it is termed as a product of poor quality (rejected), irrespective of amount of deviation from the target value (mean value of tolerance zone). This concept has similarity with the concept of scoring a 'goal' in the game of football or hockey, because a goal is counted 'one' irrespective of the location of strike of the ball in the 'goal post', whether it is in the center or towards the corner. This means that if the product dimension goes out of the tolerance limit the quality of the product drops suddenly.
Through his concept of the quality loss function, Taguchi explained that from the customer's point of view this drop of quality is not sudden. The customer experiences a loss of quality the moment product specification deviates from the 'target value'. This 'loss' is depicted by a quality loss function and it follows a parabolic curve mathematically given by L = k(y–m)2, where m is the theoretical 'target value' or 'mean value' and y is the actual size of the product, k is a constant and L is the loss. This means that if the difference between 'actual size' and 'target value' i.e. (y–m) is large, loss would be more, irrespective of tolerance specifications. In Taguchi's view tolerance specifications are given by engineers and not by customers; what the customer experiences is 'loss'. This equation is true for a single product; if 'loss' is to be calculated for multiple products the loss function is given by L = k[S2 + ( – m)2], where S2 is the 'variance of product size' and is the average product size.
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Transcription
Taguchi, developed a methodology, for applying statistics, to improve the quality of manufactured goods. Taguchi loss function, is used to measure financial loss to society, resulting from poor quality. Taguchi suggests, that every process has a target value, and as the product moves away from target value, there's a loss incurred by society. Taguchi calls common cause variation, as the noise. Taguchi techniques, aim to reduce the effect, or impact of the noise, on the product quality. Taguchi robust design, is used to make the system, less sensitive to variations. The attempt should be to meet the target value, rather than being within upper, and lower specification. As you move away, from the target value, the loss increases. This loss, is proportional to the square of the distance, from the target value. Noise factors, are classified into three categories: Outer Noise, Inner Noise, and Between Product Noise. Taguchi's approach, is not to eliminate, or ignore the noise factors. Taguchi techniques, aim to reduce the effect, or impact, of the noise, on the product quality. The signal to noise ratio, provides a measure of the impact, of noise factors, on performance. The larger the ratio, the more robust the product, is against noise.
Overview
The Taguchi loss function is important for a number of reasons—primarily, to help engineers better understand the importance of designing for variation.
See also
Taguchi also focus on Robust design of model.
References
- ^ Deming, W. Edwards (1993). The New Economics: For Industry, Government, Education. MIT Press. ISBN 0-911379-05-3.
This page was last edited on 5 October 2020, at 20:08