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Statistical Topics  


Statistical Process Control
Design of Experiment
Parameter Design
Tolerance Design


SPC - Statistical Process Control
What is Statistical Process Control?
Statistical Process Control is a quality tool that has been around since the 1920's. It was developed by Dr. William Shewhart, as a means of controlling processes, using statistics. It was also identified as a tool that could easily be used by those who did the work.
Using simple data collection techniques, straightforward calculations, and graphing tools, those who make the product, run the process, or provide the service can get immediate feedback as to how the product, process or service is performing.
Other tools associated with Statistical Process Control?


Statistical Process Control primarily utilizes variable and attribute control charts to monitor products, processes or services. Other components of SPC include Pareto Charts, Cause & Effect diagrams, Histograms and Capability Analyses.
Who is involved in Statistical Process Control?
Anyone within the organization who has a responsibility for the quality outcome of a product, process or service, should have SPC knowledge. There should also be some central coordinator, and a steering committee that ensures the results are shared with the rest of the organization.
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DOE - Design of Experiments
What is a DOE?
A Design of Experiment is a tool that was first developed as a result of a famine in Great Britain. It has since become a vital quality tool to test the cause & effect relationship between the inputs and the outputs. A well-designed DOE eliminates the effect of all outputs except the ones that can be controlled. If the output, or response changes significantly, there is a great possibility that the output is related to an input variable that has changed, rather than some other independent variable that has not changed.
A DOE can be used to manipulate process inputs to get a better understanding of the effects on the process outputs. It is a test or a sequence of tests, known as runs, where potential important variables are changed or tweaked in a systematic matter to again see the effect the adjustments have on the results.
Typically, experiments are designed to test the interaction of multiple variables, rather than altering one at a time. Isolation of one factor at a time could lead to erroneous or missed information and results.
Advantages of a Design of Experiment
Contrary to belief, results from a DOE can be obtained in a rather timely fashion, and for a relatively low cost. There is an excellent chance that the optimal variable levels will be detected. The results will yield a high level of confidence. Another advantage realized by a DOE is that there is an increased ability to identify independent main effects and any interaction effects, depending on the design chosen.
Who is involved in a DOE?
Design of experiments should be the responsibility of a cross-functional team that includes members from the area or areas where the understanding of the product, process or service is to be understood or improved. The team should also include anyone else with insight into the product, process or service.
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What is Parameter Design?
Parameter Design is a statistical technique of establishing the optimum parameters of a given product, process, system or service. The goal of parameter design is to determine the parameter values of a product, process, system or service such that it is functional and exhibits a high level of performance with a minimum sensitivity to external influences (noise factors).
A parameter design experiment will typically involve two types of factors, the design (or controllable) factors, and the noise factors. A design factor is a factor whose level can and will be set and maintained.


A noise factor is a factor whose level either cannot or will not be set or maintained, yet could affect the outcome (or levels of performance) of the functional characteristics. Generally, these factors are of the format of being too difficult, too expensive or impossible to control.
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What is Tolerance Design?
Tolerance design is a statistical technique, the tolerances of the input parameters are adjusted to obtain a desired output variation; which should be at a minimum. The objective in a tolerance design experiment is to determine the allowable ranges of variation for the product, process, system or service parameters.
The experiment will identify those parameters whose variation affects the output variation. This effect could be linear, or quadratic, or of some other format. As a result, it is possible to determine which parameters' tolerances can be tightened and to what limit.
The technique used to determine the amount of total variation due to each of the factors is a statistical tool known as ANOVA, ANalysis Of VAriance. The factors that contribute large amounts of variability are the ones that will be considered in an effort to tighten tolerances.
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