Data processing: generic control systems or specific application – Specific application – apparatus or process – Contest or contestant analysis – management – or monitoring
Reexamination Certificate
2000-08-25
2002-03-05
Cheng, Joe H. (Department: 3713)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Contest or contestant analysis, management, or monitoring
C702S081000
Reexamination Certificate
active
06353767
ABSTRACT:
BACKGROUND OF INVENTION
The invention relates generally to a method and system of predicting and tracking a confidence level or score in reliability prediction within engineering and business processes that use design for six sigma techniques (DFSS).
The invention is a subset of a system for implementing a DFSS process. For any process (business, manufacturing, service, etc.), the “Z” statistic is a metric that indicates how well that process is performing. The higher the “Z” value, the better the output. The “Z” statistic measures the capability of the process to perform defect-free-work, where a defect is synonymous with customer dissatisfaction. With six sigma the common measurement index is defects-per-unit where a unit can be virtually anything—a component, a component of a jet engine, an administrative procedure, etc. The “Z” value indicates how often defects are likely to occur. As the “Z” value increases, customer satisfaction goes up along with improvement of other metrics (e.g., cost and cycle time). “Six sigma” generally refers to a quality improvement system in which statistical processes are used to assess and measure process or product capabilities (with six sigma referring to an extremely small defect rate corresponding to six standard deviations of the desired process capability).
Most uses of six sigma have been for improving a specific application, such as semiconductor manufacturing, through a quality improvement project. The basic steps in a quality improvement project are first to define the real problem by identifying the customer's critical-to-quality requirements and related measurable performance that is not meeting customer expectations. This real problem is then translated into a statistical problem through the collection of data related to the real problem. By the application of the scientific method (observation, hypothesis and experimentation), a statistical solution to this statistical problem is arrived at. This solution is deduced from the data through the testing of various hypotheses regarding a specific interpretation of the data. Confidence (prediction) intervals provide a key statistical tool used to accept or reject hypotheses that are to be tested. The arrived at statistical solution is then translated back to the customer in the form of a real solution.
In common use, data is interpreted on its face value. However, from a statistical point of view, the results of a measurement cannot be interpreted or compared without a consideration of the confidence that measurement accurately represents the underlying characteristic that is being measured. Uncertainties in measurements will arise from variability in sampling, the measurement method, operators and so forth. The statistical tool for expressing this uncertainty is called a confidence interval depending upon the exact situation in which the data is being generated.
Confidence interval refers to the region containing the limits or band of a parameter with an associated confidence level that the bounds are large enough to contain the true parameter value. The bands can be single-sided to describe an upper or lower limit or double sided to describe both upper and lower limits. The region gives a range of values, bounded below by a lower confidence limit and/or from above by an upper confidence limit, such that one can be confident (at a pre-specified level such as 95% or 99%) that the true population parameter value is included within the confidence interval. Confidence intervals can be formed for any of the parameters used to describe the characteristic of interest. In the end, confidence intervals are used to estimate the population parameters from the sample statistics and allow a probabilistic quantification of the strength of the best estimate.
A prediction interval for an individual observation is an interval that will, with a specified degree of confidence, contain a randomly selected observation from a population. The inclusion of the confidence interval at a given probability allows the data to be interpreted in light of the situation. The interpreter has a range of values bounded by an upper and/or lower limit that is formed for any of the parameters used to describe the characteristic of interest. Meanwhile and at the same time, the risk associated with and reliability of the data is fully exposed allowing the interpreter access to all the information in the original measurement. This full disclosure of the data can then be used in subsequent decisions and interpretations for which the measurement data has bearing.
A drawback to specific applications of the six sigma process is that there is a lack of flexibility to allow for the existing implementation to be applied to other business processes. There is a need to develop a confidence level in the DFSS results of a project while in the early stages of its development. With such a confidence level prediction, decisions can be made on improving the confidence of achieving a six sigma level in the final stages of the project development. It is well known to those skilled in the art of project management, that changes in the early stage of a product are easier and less expensive than if such changes are made at the customer's location.
SUMMARY OF INVENTION
An exemplary embodiment of the invention is a method of developing a confidence level in six sigma prediction scores that includes determining a customer expectation value. A Z factor comprising a set of factors is determined. The set of factors are selected and a plurality of data is generated for the set of factors. The data is collected in at least one scorecard. At least one Z score is calculated for at least one scorecard. A total Z score is generated for the scorecards. The total Z score for said scorecards is compared with the Zst value. A Z confidence range is calculated. A confidence level based upon said Z confidence range and said total Z value is scored. The confidence level is reported.
Another embodiment uses a storage medium encoded with machine-readable computer program for developing a confidence level in six sigma prediction scores. The storage medium includes instructions for causing a computer to implement a method comprising determining a customer expectation value, an availability value and a life calculation prediction value. A Z factor comprising a set of factors is determined. The set of factors are selected and a plurality of data is generated for the set of factors. The data is collected at least one scorecard. A transfer function is generated to quantify the data at the scorecards. At least one Z score is calculated for at least one scorecard. A total Z score is generated for the scorecards. The total Z score for the scorecards is compared with the Zst value. A Z confidence range is calculated. A confidence level based upon said Z confidence range and said total Z value is scored. The confidence level is reported.
These and other features and advantages of the present invention will be apparent from the following brief description of the drawings, detailed description, and appended claims and drawings.
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Arvidson Alan Luis
Hauer Thomas Anthony
Wakeman Thomas George
Cabou Christian G.
Cheng Joe H.
General Electric Company
Ingraham Donald S.
Nguyen Kim T.
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