2. Robustness Strategy
Variation reduction is universally recognized as a key to reliability and productivity improvement. There are many approaches to reducing the variability, each one having its place in the product development cycle.
By addressing variation reduction at a particular stage in a product’s life cycle, one can prevent failures in the downstream stages. The Six Sigma approach has made tremendous gains in cost reduction by finding problems that occur in manufacturing or white-collar operations and fixing the immediate causes. The robustness strategy is to prevent problems through optimizing product designs and manufacturing process designs.
The manufacturer of a differential op-amplifier used in coin telephones faced the problem of excessive offset voltage due to manufacturing variability. High offset voltage caused poor voice quality, especially for phones further away from the central office. So, how to minimize field problems and associated cost? There are many approaches:
Compensate the customers for their losses.
Screen out circuits having large offset voltage at the end of the production line.
Institute tighter tolerances through process control on the manufacturing line.
Change the nominal values of critical circuit parameters such that the circuit’s function becomes insensitive to the cause, namely, manufacturing variation.
The approach 4 is the robustness strategy. As one moves from approach 1 to 4, one progressively moves upstream in the product delivery cycle and also becomes more efficient in cost control. Hence it is preferable to address the problem as upstream as possible. The robustness strategy provides the crucial methodology for systematically arriving at solutions that make designs less sensitive to various causes of variation. It can be used for optimizing product design as well as for manufacturing process design.
The Robustness Strategy uses five primary tools:
P-Diagram is used to classify the variables associated with the product into noise, control, signal (input), and response (output) factors.
Ideal Function is used to mathematically specify the ideal form of the signal-response relationship as embodied by the design concept for making the higher-level system work perfectly.
Quadratic Loss Function (also known as Quality Loss Function) is used to quantify the loss incurred by the user due to deviation from target performance.
Signal-to-Noise Ratio is used for predicting the field quality through laboratory experiments.
Orthogonal Arrays are used for gathering dependable information about control factors (design parameters) with a small number of experiments.