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36 Cards in this Set
- Front
- Back
- 3rd side (hint)
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Statistical Quality Control (SQC)? |
Is the application of statistical techniques to maintain the desired quality level during all stages of production or storage. |
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SQC categories? |
Descriptive Statistics (DS) Statistical Process Control (SPC) Acceptance sampling (AS) |
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Descriptive Statistics? |
Used to describe quality characteristics and relationship of a product and process. Most important DS are: -Measure of central tendency i.e Mean -Measure of variability i.e SD & range -Measure of distribution of data. |
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Applications of Descriptive Statistics? |
Used to describe quality characteristics of product and process |
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Statistical Process Control (SCP)? |
Is a collection of tools (control charts) that when used together can result in process stability and variance reduction |
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SPC involves? |
Collection Tabulation Analysis Interpretation Presentation of data. |
PICTA |
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Types of data? |
Variable Data (Measurable data) Attribute Data (that are observed to be either present or absent, conforming or non conforming |
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Applications of SPC |
-Measure and evaluate the quality of product/process -Monitor the production process through the use of control charts -Monitor both the process centre and the variation about the centre -Identify variation/change in quality characteristic. (Thus reducing wastes and processing duration) -Indicates when to take actions and when not in process |
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Source of variations? |
Common cause Assignable cause |
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Variation leads to? |
Quality defects Lack of product consistency |
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Common cause? |
Small random changes that can not be avoided. Due to slight differences in processing i.e Material, workers, machines, tools. When detected production continues |
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Assignable causes? |
The cause can be identified and eliminated. Eg; Poor quality of raw materials Employee who needs training Machine that needs repair. |
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Relationship between variation and assignable cause. |
When the variation between two points is large enough for the process to be out of control, the variation is determined to be due to non natural or assignable cause. |
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Small Standard Variation and range |
Data are clustered close around the mean. |
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Symmetric distribution |
There are the same number of observation below and above the mean. |
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Skewed distribution |
There are different number of observations above and below the mean. |
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Sigma |
1- 68.26% 2- 95.44% 3- 99.74% |
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Control Chart? |
Is the graph that shows whether a sample of data falls within the common or normal range of variation. |
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Alpha ? |
The sum of probabilities in both tails of the distribution that falls outside the confidence limits. |
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Control Chart for Variable |
Used to monitor characteristics that can be measured and have a continuum of values i.e height, weight and volumes. |
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Control Chart for Attribute |
Used to monitor characteristics that have discrete values and can be counted. Eg Colour, taste and smell |
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Advantages of Attribute Control Charts |
-Allow quick summaries of product quality -Easily understood by the managers -Bypass the need for expensive, precise devices and time consuming measurement procedure |
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Advantages of Variable Control Charts |
More sensitive than Attribute Control Chart Alert us to quality problems |
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Central requirements for properly using process control charts. |
-Know how to interpret process control chart -Understand the generic process for implementing process chart -Know when different process charts are used -Know how to compute limits for different types of process charts. |
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Procedures for control charts |
-Take periodic samples from a process -Plot the sample points on control chart -Determine if the process is within limits -Correct the process before defect occur |
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Benefits of control chart |
-Improve productivity -Effective in defect prevention -Prevent unnecessary process adjustment -Provide diagnostic information -Provide information about process capability |
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Upper control limit (UCL) |
Is the maximum acceptable variation from the mean for a process that is in a state of control. |
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Lower control limit (LCL) |
Is the minimum acceptable variation from the mean for a process that is in a state of control. |
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X bar chart |
Used to monitor changes in the mean value of a process. Constructed in two ways; -Process Average -Process variability |
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R chart |
Used to monitor process dispersion/variability |
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P chart |
Measures the proportion of items in a sample that are defective. Eg; Proportion of broken cookies in a batch |
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C chart |
Used to count the actual number of defects Eg; Number of microorganisms in a petri dish Number of complaints from customers. |
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Type 1 error |
Occurs when a process us thought to be out of control when in fact it is not. |
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Type 2 error |
Occurs when a process is thought to be in control when it is out of control. |
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Steps for developing control charts |
- Identify critical operations in a process - Identity product characteristics - List the data points in time order - Find the mean of the points - Calculate control limits - Set up scales - Draw a solid line - Draw the upper and lower limits - Plot the data points in time sequence |
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Constructing a control chart for variable |
- Define the problem - Select quality characteristics to be measured - Choose sample size - Collect data - Determine the trial centre line for X bar chart - Determine the control limits for X bar chart - Determine the control limits for the R chart - Examine the process (Control Chart Interpretation) - Revise the chart - Achieve the purpose. |
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