Summary by Gina Cannella
Master of Accountancy Program
University of South Florida, Fall 2001
Deming Main Page | Quality Related Main Page
Achieving robust quality begins with the design of a product. The quality of a product is inherent in a well thought out design that takes into consideration possible external factors that may negatively affect product use. The design must account for various conditions that a product may be exposed to in the hands of customers. Defective products adversely affect customer satisfaction and may severely damage the reputation of a company, thus resulting in higher than expected losses. The fact that it costs a company three times as much to acquire a new customer as it does to retain present customers stresses the importance of selling high quality products that satisfy customer needs.
Zero Defects, Imperfect Products
Factories measure quality losses in terms of the value of products that cannot be shipped due to defects, as well as the added costs to repair defective products. These losses are generated either from a malfunction within the factory or from an inherent failure in product design. The concept of zero defects claims that the effort to reduce process failures in the factory will simultaneously reduce instances of product failure in the field. In contrast, Taguchi and Clausing believe that reducing product failure in the field will simultaneously reduce the number of defective products in the factory. Robust quality is captured by emphasizing the importance of reducing defects in the field that, in turn, will reduce defects in the factory. Taguchi’s argument against zero defects stems from the fact that managers tend to focus on quality in terms of acceptable deviations from targets. Instead of consistently striving to hit a particular target, they settle for being within an acceptable range. Specified tolerances are built into the concept of zero defects. This may encourage managers to set wider tolerances than necessary in order to fall within the guidelines of zero defects and bypass additional spending on corrective measures.
Robustness as Consistency
Taguchi emphasizes the importance of consistency. A company is in a better position to correct a malfunction that misstates a target with perfect consistency, than one that hits the target haphazardly. With consistency of error, it is much easier to detect the variable causing the discrepancy and adjust machinery accordingly, than to test numerous variables affecting a number of deviations. Under zero defects, a product may randomly hit the target as long as it stays within the specified range and still be approved for sale. Taguchi argues that quality robustness stems from consistency. Even if it means a product falls outside standard tolerances, as long as it does so consistently, then it is much easier to adjust machinery to attain precise targets.
Quality Loss Function
The total loss absorbed by a company due to target deviations is equal to the losses discovered after products are shipped plus factory losses. The quality loss function is defined as
L = D2C,
where D is the deviation from the target and
C the costs the factory would incur to achieve target specifications.
This formula reveals the higher costs associated with remaining off target. The losses associated with customer dissatisfaction would far outweigh the costs of adjusting the machinery to meet its designated target.
In order to calculate a loss, there must be a known target from which to measure deviation. Targets, for the most part, are a reflection of what customers demand from a product. Taguchi and Clausing use the terms signal and noise to measure quality robustness. The signal is the quality the product aims to achieve, while the noise is what interferes with the signal’s ability to do so. In order to maximize signal to noise ratios, there must be input from all departments, including product design, manufacturing, field support, and marketing. One approach to optimizing signal to noise ratios was developed by a British statistician named Sir Ronald Fisher. His system of orthogonal array tested products under various conditions and recorded the average effect each factor had on the product. Once signal-to-noise ratios and design values are optimized, a System Verification Test compares a prototype to the benchmark product. Engineers test the two products under conditions that simulate expected use.
Although a product manufactured to conform to zero defects may be marketable, a “robust” product is able to withstand more variation in the production system. Focusing on design and the enduring quality of products outside of the company helps engineers explore the effects external factors may have on product viability as well as build a foundation for optimal quality during manufacturing. It is important for designers to visualize a product beyond factory walls in order to meet customer needs and maintain customer loyalty.
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Related summaries:
Albright, T. L. and H. Roth. 1992. The measurement of quality costs: An alternative paradigm. Accounting Horizons (June): 15-27. (Summary).
Albright, T. L. and H. Roth. 1993. Controlling quality on a multidimensional level. Journal of Cost Management (Spring): 29-37. (Summary).
Albright, T. L. and H. P. Roth. 1994. Managing quality through the quality loss function. Journal of Cost Management (Winter): 20-37. (Summary).
Anderson, S. W. and K. Sedatole. 1998. Designing quality into products: The use of accounting data in new product development. Accounting Horizons (September): 213-233. (Summary).
Deming, W. E. 1993. The New Economics for Industry For Industry, Government & Education. Cambridge: Massachusetts Institute of Technology Center for Advanced Engineering Study. Chapter 10. (Summary).
Kim, M. W. and W. M. Liao. 1994. Estimating hidden quality costs with quality loss functions. Accounting Horizons (March): 8-18. (Summary).
Martin, J. R. Not dated. Summary of the 1992 PBS Program Quality or Else. Management And Accounting Web. Quality Or Else
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Morse, W. J. 1983. Measuring quality costs. Cost and Management (July-August): 16-20. (Summary).
Pasewark, W.R. 1991. The evolution of quality control costs in U.S. manufacturing. Journal of Cost Management (Spring): 46-52. (Summary).
Roth, H. P. and T. L. Albright. 1994. What are the costs of variability? Management Accounting (June): 51- 55. (Summary).
Sedatole, K. L. 2003. The effect of measurement alternatives on a nonfinancial quality measure's forward-looking properties. The Accounting Review (April): 555-580. (Summary) and