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Chapter 10

GMS401- Chapter 10- Statistical Quality Control.docx

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Global Management Studies
GMS 401
Sam Lampropoulos

GMS401- Chapter 10- Statistical Quality Control Introduction  Statistical Quality Control- uses statistical techniques & sampling to monitor & test the quality of goods & services o Acceptance sampling determines to accept or reject a product o Statistical process control determines if process is operating within acceptable limits  Inspection- appraisal of goods/services against standards o How much/ how often o Where/when o Centralized VS. on-site Statistical process control planning process 1. Define the quality characteristics important to customers, and how each is measured  What is to be controlled 2. For each characteristics a. Determine a quantity control point i. At the beginning of the process—little sense in paying goods that do not meet standards ii. At the end of the process—customer satisfaction, company’s image, repairing or replacing product iii. At the operation where a characteristic of interest to customers is first determined—before costly, irreversible, or covering operation b. Plan how inspection is to be done, how much to inspect, and whether centralized or on site i. Technical and needs engineering knowledge ii. iii. On-site In Lab  Immovable product  Specialized equipment  Simple or handheld  Skilled quality control measuring equipment inspectors  Automated inspection  More favorable test environment c. Plan the corrective plan i. Uncovering the cause and correcting it 1 GMS401- Chapter 10- Statistical Quality Control Statistical Process Control  Statistical process control (SPC)- statistical evaluation of the product in the production process 1. The Quality Control Steps Control 2. Type of Variations 3. Control Charts steps 4. Designing Control Charts  5. Individual Unit and Moving Range Charts 6. Control Charts for Attributes 7. Using Control Charts  Types of variations and sampling distribution o Random variation- natural variation in the output of a process, created by countless minor factors o Assignable variation- non-random variability in process output; a variation whose cause can be identified o Unlike random variation—main source of assignable variation can usually be identified and eliminated o Variability of a sample statistic is described by its sampling distribution o Central limit theorem implies—sampling distributions will be approximately normal even if the population is not  Control charts- time ordered plot of sample statistic with limits o Basis—sampling distribution o Purpose monitor process output to distinguish between random and assignable variation o Upper and lower control limits define the range of acceptable variation o Control limits- dividing lines for the value of sample statistic between concluding no process shift and a process shift, hence random and assignable variation o Type I error- concluding that a process has shifted when it has not o Alpha risk- where alpha (α) is the sum of the probabilities in the 2 tails o Using wider limits reduces the probability of a Type I error decreases the area in the tails of destitution o Type II error- concluding that a process has not shifted when it has  2 sigma limits and 3 sigma limits—commonly used without specifically referring to the probability of a type II error o Designing control charts 1. Determine a sample size n 2. Obtain 20-25 samples of size n 3. Establish preliminary control limits using appropriate formulas ad graph them 4. Plot the sample statistic values on the control chart 5. If you find no points outside control limits, assume there is no assignable cause the process is stable and control Sample mean and range control charts  Sample mean control chart- the control chart for sample mean, used to monitor the process mean  Average of means is the mean of all observation in sample grand mean 2 GMS401- Chapter 10- Statistical Quality Control  First approach to calculate control limits—standard deviation of the process o Upper control limit (UCL )= + zσ X o Lower control limit (LCL )X - zσ  σ = σ√n  Second approach to calculate control limits- use sample range as a measure of process variability o UCL = X + A2R o LCL = X - 2 R  A2 get from table (page 330, Table 10-2)  Sample range (R) control charts- used to monitor process dispersion or spread o USL RD 4R o LCL RD 3R  D3, 4  get from table (page 330, Table 10-2) Why use both sample mean and sample range control charts?  Sample mean control charts are sensitive to
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