PowerPoint PresentationNorman Augustine
Deming and Juran outlined the principles of Quality Management.
Tai-ichi Ohno applies them in Toyota Motors Corp.
Japan has its National Quality Award (1951).
U.S. and European firms begin to implement Quality Management programs (1980’s).
U.S. establishes the Malcolm Baldridge National Quality Award (1987).
Today, quality is an imperative for any business.
What is quality management all about?
Try to manage all aspects of the organization in order to excel in all dimensions that are important to “customers”
Two aspects of quality:
freedom from trouble: fewer defects = higher quality
What does Total Quality Management encompass?
TQM is a management philosophy:
continuous improvement
leadership development
partnership development
Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes.
PDCA: Plan-do-check-act as defined by Deming.
Benchmarking : what do top performers do?
Plan
Do
Act
Check
1. Process flowchart
2. Run Chart
3. Control Charts
4. Cause and effect diagram (fishbone)
Environment
Machine
Man
Method
Material
5. Check sheet
6. Histogram
7. Pareto Analysis
A bank is employing a call answering service
The main goal in terms of quality is “zero waiting time”
- customers get a bad impression
- company vision to be friendly and easy access
The question is how to analyze the situation and improve quality
The current process
Customer B
Complaining
Takes too much time to explain
Does not understand customer
(12-day analysis with check sheet)
Daily average
Total number
14.3
172
B
5.1
61
D
1.6
19
E
1.3
16
F
A
B
C
D
E
F
Frequency
Percentage
0%
49%
71.2%
100
200
300
87.1%
150
250
Ask all employees to leave messages when leaving desks
Compiling a directory where next to personnel’s name appears her/his title
Results of implementing the recommendations
Before…
…After
A
B
C
D
E
F
Frequency
Percentage
100%
0%
49%
71.2%
100
200
300
87.1%
100%
B
C
A
D
E
F
Frequency
Percentage
0%
100
200
300
Improvement
In general, how can we monitor quality…?
Assignable variation: we can assess the cause
Common variation: variation that may not be possible to correct (random variation, random noise)
By observing
variation in
output measures!
Statistical Process Control (SPC)
Every output measure has a target value and a level of “acceptable” variation (upper and lower tolerance limits)
SPC uses samples from output measures to estimate the
mean and the variation (standard deviation)
Example
We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ
Question:
The average (mean) of the observations:
The standard deviation of the observations:
What is the key assumption behind SPC?
LESS VARIABILITY implies BETTER PERFORMANCE !
Target
Capability Index (Cpk)
It shows how well the performance measure fits the design specification based on a given tolerance level
A process is ks capable if
Capability Index (Cpk)
Cpk < 1 means process is not capable at the ks level
Cpk >= 1 means process is capable at the ks level
Another way of writing this is to calculate the capability index:
Accuracy and Consistency
We say that a process is accurate if its mean is close to
the target T.
We say that a process is consistent if its standard deviation
is low.
LTL = 9
UTL = 11
UTL
LTL
X
Example
Consider the capability of a process that puts pressurized grease in an aerosol can. The design specs call for an average of 60 pounds per square inch (psi) of pressure in each can with an upper tolerance limit of 65psi and a lower tolerance limit of 55psi. A sample is taken from production and it is found that the cans average 61psi with a standard deviation of 2psi.
Is the process capable at the 3s level?
What is the probability of producing a defect?
Solution
No, the process is not capable at the 3s level.
Example (contd)
Suppose another process has a sample mean of 60.5 and
a standard deviation of 3.
Which process is more accurate? This one.
Which process is more consistent? The other one.
Solution
=G(-3)+1-G(2)
= 0.0241
Control Charts
Control charts tell you when a process measure is exhibiting abnormal behavior.
Upper Control Limit
p Chart
This is a plot of proportions over time (used for performance measures that are yes/no attributes)
X/R Chart
This is a plot of averages and ranges over time (used for performance measures that are variables)
Statistical Process Control with p Charts
LCL = 0.015
UCL = 0.117
p = 0.066
When decisions are simple “yes” or “no” by inspection
When the sample sizes are large enough (>50)
Statistical Process Control with p Charts
Sample (day)
Statistical Process Control with p Charts
Let’s assume that we take t samples of size n …
Statistical Process Control with p Charts
Statistical Process Control with p Charts
LCL = 0.015
UCL = 0.117
p = 0.066
It is not possible to label “good” or “bad”
If we have relatively smaller sample sizes (<20)
Statistical Process Control with X/R Charts
Take t samples of size n (sample size should be 5 or more)
R is the range between the highest and the lowest for each sample
Statistical Process Control with X/R Charts
X is the mean for each sample
Statistical Process Control with X/R Charts
X is the average of the averages.
R is the average of the ranges
define the upper and lower control limits…
Statistical Process Control with X/R Charts
Read A2, D3, D4 from
Table TN 8.7
Sample
Calculate the average and the range for each sample…
Sample
Finally…
LCL = 11.90
UCL = 12.10
LCL
UCL
X
LCL
R
UCL
Example
a. Develop a p chart for 95 percent confidence (z = 1.96)
b. Based on the plotted data points, what comments can you make?
Sample
n
Ten defectives were found in 10 samples of size 15.
UCL = .067 + 1.96(.0645) = .194
LCL = .067 - 1.96(.0645) = -.060 zero
Defect proportion on Days 1 and 8 is 0.2, so process out of control.
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