Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
1999-09-03
2004-01-06
Broda, Samuel (Department: 2123)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
C703S001000, C700S095000, C700S109000, C702S179000
Reexamination Certificate
active
06675135
ABSTRACT:
CROSS-REFERENCE TO RELATED APPLICATIONS
Not applicable.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
Not applicable.
BACKGROUND OF THE INVENTION
The present invention relates to a method for increasing the sigma of a process by identifying sigma during a product development procedure and providing a confidence factor for each sigma during the development procedure and, based on the confidence factor adjusting resource allocation in a proactive manner.
With the advent of a worldwide market place and corresponding consumer demand for highly reliable products, quality has become an increasingly important issue for manufacturers. When manufactured products having defects are produced and sold, the result is lost manufacturing time as well as unfavorable publicity for the manufacturer. The quality of a company's product line can therefore play a decisive role in determining the company's reputation. As a result of this pressure for defect free products, increased emphasis has been placed on quality control at virtually all levels of the manufacturing process. Higher quality requirements have lead to various initiatives designed to improve quality.
One way to track production quality is to plot a parameter (e.g. length) which measures a specific characteristic of a product being manufactured against frequency of production and by comparing the distribution (e.g., the range and frequency of lengths) to an optimum design point (e.g., a specified length) and upper and lower limits to identify defective products. The result is usually a Gausian distribution having a mean about the optimum parameter design point with some portion of production outside the limits.
The term “sigma” which is represented herein by the symbol Z is synonymous with a standard deviation. One measure of product quality is the number of sigmas or standard deviations about the distribution mean which fit within specified limits. For example, where one sigma (i.e. Z=1) fits within limits, 68% of an entire distribution is within limits. Where two sigmas (i.e. Z=2) fit within limits, 95% of the entire distribution is within the limits. Where six sigmas (i.e. Z=6) fit within limits, 99.9999998% of the total distribution is within the limits and only 3.4 parts per million (ppm) are outside the limits and defective. Thus, the higher the sigma value the better the output.
One general grouping of quality initiatives is referred to as “six sigma”. Initially these initiatives were named six sigma because their goal was to achieve six sigma status or 3.4 ppm defects. More recently the term 41 six sigma” has been used to generally refer to any program having the goal of increasing a process Z (e.g., increasing Z from 4 to 5) or increasing quality.
While the name six sigma has only been coined relatively recently, the roots of the six sigma initiative (i.e., increasing Z) are in quantitative quality improvement efforts which were employed on manufacturing floors as early as the 1940s. In the factory environment it became common to “grow” or improve the quality of an existing product design by specifying product characteristics and limits, producing prototypes, measuring the characteristics of the prototypes and, where to many of the measured characteristics were not within the prescribed limits, adjusting some process step in an effort to conform a greater portion of the characteristics to the limits. In effect, iterative experiments were performed to increase Z.
While the Z of almost any process can be increased through extensive experimentation and adjustments, conventional methodologies for increasing Z have a number of shortcomings. First, because Z is related to actual product measureables, prototypes have to be produced prior to generating a process Z and process adjustments are only made after prototyping and measurement. In other words these conventional methodologies are reactive, not proactive, when it comes to identifying product characteristics which have to be modified. Prototype production and modification are relatively expensive and therefore prototyping increases development costs appreciably.
Second, in many cases a process includes several different variables which may affect a single characteristic of the end product. In these cases, which of a set of different variables will effect the characteristic positively (i.e, increase Z) often is unclear and therefore the process adjustment task to increase Z amounts to an unscientific guessing game. In effect, while a low Z indicates problems with a process, the low Z itself cannot provide a road map for increasing Z.
Third, even where prototypes are relatively inexpensive to produce, early on in the development and experimentation process only very few (or perhaps even zero) prototypes are produced. Where only a small number of prototypes are produced it is not possible to identify a process Z. Similarly, in the case of some highly specialized or extremely expensive products, only very few products may ever be produced. In these cases an accurate Z is never generated.
Fourth, reactive quality methodologies are extremely time consuming as prototyping, experimenting and adjusting have to be performed repeatedly in order to increase Z to an acceptable level. While the luxury of time may be available in some industries, increased speed to market is a competitive advantage in many different industries such that iterative design processes cannot be tolerated.
Therefore it would be advantageous to have a method for proactively predicting a sigma value during product development and also for identifying confidence identifying factors or data which can be used to assess the likelihood that the predicted sigma value is accurate so that development resources can be shifted during the development procedure to provide a process having a higher Z at the end of the procedure thereby reducing the need for reactive product and process redesign.
BRIEF SUMMARY OF THE INVENTION
An exemplary embodiment of the invention is directed to a method to be used during a product development procedure wherein the procedure includes a series of consecutive development phases and the product includes at least two critical to quality characteristics (CTQs). The method is for generating a confidence matrix which can be used to increase a product sigma through product design. A user initially provides product limits and thereafter provides additional development information during each consecutive development phase. During at least two of the development phases and for each CTQ, development information is used to determine a quality factor which is indicative of the probability that the product will be within the specified limits. Also, for each CTQ, a confidence factor is identified which is indicative of the probability that the quality factor is accurate. Then, quality factors, CTQs and confidence factors are arranged such that the CTQs and factors are correlated.
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Harrold, “Designing for Six Sigma Capability”, Control Engineering, vol. 46 Issue 1, pp. 62-64, 66, 68, and 70 (Jan. 1999).*
Smith, “Six-Sigma Design”, IEEE Spectrum, vol. 30 Issue 9, pp. 43-47 (Sep. 1993).*
Lantzy, “Application of Statistical Process Control to the Software Process”, ACM Proceedings of the 9th Washington Ada Symposium on Ada, pp. 113-123 (1992).
Berezowitz William A.
Immelt Jeffrey R.
Murray Jonathan A.
Broda Samuel
GE Medical Systems Global Technology Company LLC
Horton Carl
Quarles & Brady LLP
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