 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Chapter 12 Statistically-Based Quality Improvement for Attributes

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Chapter 12  Generic process for developing attribute charts  Understanding attribute charts  Reliability models

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Generic process for developing attribute charts  Identify Critical Operations  Identify critical product characteristics  Determine whether the product characteristics is a variable or an attribute

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Generic process for developing attribute charts  Select the appropriate process chart  Establish control limits  Update the limits when changes have been made to the process

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  Attribute Charts deal with binomial and Poisson processes, not measurements  Think in terms of defects and defectives rather than diameters and widths

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  A defect is an irregularity or problem with a larger unit.  The larger unit may contain many defects  Defects are countable

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  A defective is a unit that, as a whole, is not acceptable or does not meet performance requirements  Defectives are monitored p and np charts  Defects are monitored using c and u charts

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  A p chart is a process chart that is used the proportion of items in a sample defective  Effective to determine when there has been a shift in the proportion defectives for a particular product or service

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  p chart applications include:  Late deliveries  Incomplete orders  Calls not getting dial tones  Accounting transaction errors  Parts that do not mate properly

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  The np chart is a graph of the number of defectives in a subgroup  The np chart requires the sample size of each subgroup be the same  The uses for the np chart are essentially the same as the p chart

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  The c chart is a graph of the number of defects per unit.  The units must be of the same sample space.. Size, height, length, volume …  The c chart is used to detect nonrandom events in the life of a production process

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Understanding Attribute Charts  The u chart is a graph of the average number of defects per unit.  The units can be of different Sizes, height, length, volume …

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Reliability Models  Bathtub shaped function  The vertical axis is the failure rate  The horizontal axis is time  Shows products are more likely to fail either early or late in their lifetimes

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Reliability Models  Series reliability  Components are in a series if the performance of the entire system depends on all the components functioning properly  The Components need not be physically wired sequentially  All parts must function for the system to function

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Reliability Models  Parallel reliability  High reliability systems often require extremely require high component reliability  When it is impossible an alternative is to use a backup system  Another word for backup is redundant or parallel

 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Strategic Quality Planning Statistically-Based Quality Improvement for Attributes Summary  The object of using process charts is to continually improve your processes  As we make changes and improvements … there will be fewer defects and defectives

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