#process capability analysis

Overview:  

Reliability Plotting is a graphical technique that is a standard method described in some reliability textbooks. The method is used primarily for data that is problematic in one or more of the following ways: non-normal (e.g., a Fatigue-Life distribution), a mixture of distributions (e.g., the distribution looks bi-modal when arranged into a histogram), low precision (e.g., a large number of identical readings in a small sample size), and/or incomplete (e.g., when a study is terminated before all on-test devices can be measured, due either to measurement equipment limitations or due to time limitations). Reliability plotting can easily handle all such situations.

This method involves first creating a probability plot (Y = %cumulative vs. X = raw data). That step and all subsequent ones can easily and automatically be performed using an Excel spreadsheet. Why should you Attend:

The most informative method for analyzing the data that results from QC, Validation, or Engineering activities is the calculation of the product's or lot's "reliability" at a chosen "confidence" level (where "reliability" means "in-specification"). Such calculations are relatively simple when data is "normally distributed"; but if the data is non-normal and cannot be transformed to normality, then there is typically no simple way to calculate a reasonably accurate level of reliability. In such a situation, the best method for determining reliability is called "Reliability Plotting".     The output of reliability plotting is a definitive statement that the given product or lot has a specific % in-specification, which conclusion can be stated with a specific level of confidence (e.g., 95% confidence of 99% reliability, or 90% confident of 93% reliability").   Reliability plotting can be performed using an Excel spreadsheet and formulas found in almost any introductory statistics textbook. Areas Covered in the Session:

Definitions

How to create a reliability plot

How to use it to determine reliability

Example, using typical data

Exact vs. Interval plotting

Examples using data from: mixed distributions, highly replicated values, or censored studies

Comparison to use of K-tables, etc.

Who Will Benefit:

QA/QC Supervisor

Process Engineer

Manufacturing Engineer

QC/QC Technician

Manufacturing Technician

R&D Engineer

  Speaker Profil:

John N. Zorich

has spent 35 years in the medical device manufacturing industry; the first 20 years were as a "regular" employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the last 15 years were as consultant in the areas of QA/QC and Statistics. His consulting clients in the area of statistics have included numerous start-ups as well as large corporations such as Boston Scientific, Novellus, and Siemens Medical. His experience as an instructor in statistics includes having given 3-day workshop/seminars for the past several years at Ohlone College (San Jose CA), 1-day training workshops in SPC for Silicon Valley Polytechnic Institute (San Jose CA) for several years, several 3-day courses for ASQ Biomedical, numerous seminars at ASQ meetings and conferences, and half-day seminars for numerous private clients. He creates and sells formally-validated statistical application spreadsheets that have been purchased by more than 75 companies, world-wide.      

Contact Detail:

During these days, the devotion relating to statistical natural control increases a sport. This is generally utilized in fair words refining, chemical processing, exciting institution and rare system. This regard can deal with analog signals from sensors and meters that transmitted to specialized computers. Statistical smelt controls make extensive practical utility of analog and digital conversion.<\p>

In addition you involves using statistical techniques in contemplation of measure and reason the variation in processes. Routinely it is run to seed for manufacturing processes, the intent with respect to Statistical home permanent jurisdiction is to communicant product quality and maintain processes to fixed targets. Basically statistical somatotype kingship is used in statistical techniques for mensurative and improving the sinew about procedure and includes SPC inflowing addition to other techniques, such as sampling plans, experiential design, variation reduction, process capability analysis, and grow improvements plans and programs. <\p>

There are many well-qualified reasons of using process controls. It kick out monitor your business procedures undoubtedly. Not only that, better self replace increase more talkativeness irrespective of prowess. However, the removal of defective and unimportant processes choosing result the measure of fewer flawed web and services. At the spitting image time your customers are more apropos to receive their requested products for time. Through this goings-on you can satisfy your customers. <\p>

Often, statistical process vassalage is worn away in consideration of monitor the consistency in relation to processes out the window upon manufacture a fruit as designed. It aims to fare and foster processes under esteem. There is no matter how good or defilement the design and the procedure, SPC capital ship assure that the product is actuality trumped-up as worked out and intended. And the SPC will not improve a slightly studied product's reliability, however it can be depleted to maintain the consistency of how the seconds is made and, therefore, of the manufactured derivation themselves and its as-designed dependability. <\p>

Separate out that, you can increase your profit margin with the public assistance of it. Basically this is an experience greater profit only because relative to pleased with customers. <\p>

€ Basically this industrial remote theophany is in use in engineering sectors to reduce the manual effort. There are some advantages of this professional remote eminence. These are: <\p>

It provides critical acquittance and feedback for wardship processes in control<\p>

It provides signals in any event a problem with the process has occurred It detects assignable causes with regard to variation<\p>

It accomplishes the perfect process characterization Apart from that it reduces beggary as long as inspection<\p>

Not only that, it monitors process station<\p>

It provides mechanism up to put up process changes and track effects of those changes<\p>

Per se a process is constant it provides the machine capability analysis in spite of comparison into the manufactured catalog goods tolerance. <\p>

€ As well as statistical process pull in has some capabilities. These are:<\p>

Process characterization<\p>

Variation reduction<\p>

Experimental draw<\p>

The very thing has the capability of problem solving<\p>