Chapter 6 Statistical Quality Control Flashcards
solution to quality problems are important to…
companies, stakeholders, employees, customers
Statistical quality control (SQC)
the term used to describe the set of statistical tools used by quality professionals.
SQC categories: descriptive statistics
are used to describe quality characteristics and relationships.
Included are statistics such as the mean, the standard deviation, the range, and a measure of the distribution of data.
SQC categories: Statistical process control (SPC)
involves inspecting a random sample of the output from a process and deciding if it falls within range and whether the process is functioning properly.
SQC categoties: Acceptance sampling
is used to randomly inspect a batch of goods to determine acceptance/rejection.
what is the difference between SPC and acceptance sampling?
SPC is looking at the statistical process to see if it’s within the functional range. Whereas acceptance sampling is taking the end product, a batch of the goods, evaluating if they are acceptable or rejecting.
Sources of Variation has 2 categories
Common causes of variation, Assignable causes of variation
Sources of Variation: Common causes of Variation definition
Common causes of variation are based on random causes that we cannot identify. These types of variation are unavoidable and are due to slight differences in processing.
Sources of Variation: Assignable sources of variation
can be observed involves those where the causes can be precisely identified and eliminated
SQC - descriptive statistics, mean:
A statistic that measures the central tendency of a set of data.
SQC - descriptive statistics, range:
The difference between the largest and smallest observations in a set of data.
SQC - descriptive statistics, standard deviation:
A statistic that measures the amount of data dispersion around the mean.
SQC - descriptive statistics, Distribution of data shape:
normal/Bell shape, When a distribution is symmetric, there are the same number of observations below and above the mean. This is what we commonly find when only normal variation is present in the data.
skewed: When a disproportionate number of observations are either above or below the mean, we say that the data have a skewed distribution.
SQC - Statistical Process Control methods (SPC), control chart:
(also called process chart or quality control chart) is a graph that shows data plotted on a graph with a center line (CL), upper control limit (UCL), and lower control limit (LCL)
Control charts for variables are used to monitor characteristics that can be measured, e.g., length, weight, diameter, time.
Control charts for attributes are used to monitor characteristics that have discrete values and can be counted, e.g., percent defective, number of flaws.
SQC - SPC, control chart shows - out of control:
when a plot of data reveals that one or more samples fall outside the control limits.
types of control charts
variable and attribute
Type of Control Chart: Variable
A product characteristic that can be measured and has a continuum of values (e.g., height, weight, or volume):
x-bar charts
R-charts
Type of Control chart, Attribute:
A product characteristic that has a discrete value and
can be counted, yes/no or pass/fail:
p-charts
c-charts
control charts, variable: x-bar charts
to monitor the changes in the mean of a process
(central tendencies)
System can show acceptable central tendencies but unacceptable variability.
control charts, variable, R-charts:
Use R-charts to monitor the dispersion or variability of the process.
System can show acceptable variability but unacceptable central tendencies.
control charts for attributes, p-charts:
Use p-charts for quality characteristics that are discrete and involve yes/no or good/bad decisions.
P-charts measure the proportion of items in a sample that are defective:
• proportion of broken cookies in a batch
• proportion of cars produced with a misaligned fender
P-charts are used when both the total sample size and the number of defects can be computed.
control charts for attributes, C-charts:
Use c-charts for discrete defects when there can be more than one defect per unit.
Actual number of defects.
Number of flaws or stains in a carpet sample cut from a production run. o Number of complaints per customer at a hotel.
C-charts are used when you can compute only the number of defects but not the proportion that is defective.
process capability
The ability of a production process to meet or exceed preset specifications.
product specifications
Preset ranges of acceptable quality characteristics.
often called tolerances
preset ranges of acceptable quality characteristics, such as product dimensions
Example: bottle fill might be 16 oz. ± 0.2 oz. (15.8oz.–16.2oz.)
based on how product is to be used or what the customer expects
Measuring Process capabilities, Process capability index (Cp)
Process capability is measured by the process capability index, Cp, which is computed as the ratio of the specification width to the width of the process variability.
Cp assumes that the process is centered in the specification
range.
Measuring Process capabilities, Cpk
helps to address a possible lack of centering of the
process.
six sigma quality
A high level of quality associated with approximately 3.4 defective parts per million.
Six Sigma Approach, DMAIC
Define Measure Analyze Improve Control
Six Sigma Approach, DMAIC, Define:
Define the quality problem of the process
Six Sigma Approach, DMAIC, Measure:
measure the current performance of the process
Six Sigma Approach, DMAIC, Anaylyze:
Analyze the process to identify the root causes of the quality problem
Six Sigma Approach, DMAIC, Improve:
Improve the process by eliminating the root cause of the problem
Six Sigma Approach, DMAIC, Control:
control the process to ensure the improvements continue
Acceptance sampling:
the third branch of statistical quality control, refers to the process of randomly inspecting a certain number of items from a lot or batch in order to decide whether to accept or reject the entire batch. What makes acceptance sampling different from statistical process control is that acceptance sampling is performed either before or after the process, rather than during the process. Acceptance sampling before the process involves sampling materials received from a supplier, such as randomly inspecting crates of fruit that will be used in a restaurant, boxes of glass dishes that will be sold in a department store, or metal castings that will be used in a machine shop. Sampling after the process involves sampling finished items that are to be shipped either to a customer or to a distribution center
Goal of acceptance sampling
to determine the criteria for acceptance or rejection based on:
size of the lot (N)
size of the sample (n)
number of defects above which a lot will be rejected (c) o number of samples that will be taken
single , double, multiple sampling plans
Which one to use is based on cost involved, time consumed, and cost of passing on a defective item.
Can be used with both variable and attribute measures, but is more commonly used for attributes.
