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Lecture 12

OMIS 2010 Lecture Notes - Lecture 12: Control Chart, Statistical Process Control, Six Sigma


Department
Operations Management and Information System
Course Code
OMIS 2010
Professor
Linda Lakats
Lecture
12

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Lecture 12 - Statistical Process Control
Six Sigma - DMAIC Approach
1. Define critical outputs and identify gaps for improvement
2. Measure the work and collect process data
3. Analyze the data
4. Improve the process
5. Control the new process to make sure new performance is maintained
Statistical Process Control (SPC):
a collection of methods for controlling the quality of a product by collecting and interpreting data to:
(a) determine the capability and current performance of a process, &
(b) help identify and eliminate unwanted causes of variation.
Natural Variations (“common causes”)
Variability that affects every production process to some degree and is to be expected
Form a pattern that can be described as a distribution
For any distribution there is a measure of central tendency (µ) and dispersion (σ)
If the distribution of outputs falls within acceptable limits, the process is said to be “in control”
and natural variations are tolerated.
Assignable Variations (“special causes of variation”)
Can be traced to a specific reason (i.e machine wear, untrained workers)
For the process to remain under control, it is necessary to identify and eliminate assignable
variations
If assignable causes are present, the process output is not stable over time and is not predictable.
Control Charts provide a statistical signal that assignable causes are present
Control Charts
Purpose: help distinguish btw natural variations and variations due to assignable causes
A graphical presentation of data over time
Upper and lower limits define a range of normal variation.
Constructed from historical data
Process Control
STEP 1: Get into control.
STEP 2: Produce within limits.
Steps In Creating Control Charts
1. Take samples from the population and compute the appropriate sample statistic
2. Use the sample statistic to calculate control limits and draw the control chart
3. Plot sample results on the control chart and determine the state of the process (in or out of
control)
4. Investigate possible assignable causes and take any indicated actions
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