Transcript Quality Management

Quality Management
and Control
Presented by:
Mohammad Saleh Owlia, Visiting Professor, University of Malaya
Adopted from:
Operations Management for Competitive Advantage, Eleventh Edition (2006)
Richard B. Chase, F. Robert Jacobs and Nicholas J. Aquilano
1
What is Quality?
2
Garvin’s Product Quality Dimension
Performance
Features
Durability
Reliability
Serviceability
Conformance
Aesthetics
Perceived Quality
3
Service Quality Dimensions
Parasuraman, Zeithamel, and
Berry’s Service
Quality Dimensions
Tangibles
Responsiveness
Service
Reliability
Assurance
Empathy
4
Total Quality Management
TQM may be defined as managing the
entire organization so that it excels on
all dimensions of products and services
that are important to the customer.
5
Quality Specifications
Design quality (consumer’s view)
–
inherent value of the product in the
marketplace and therefore, has strategic
implications.
Conformance quality (producer’s view)
–
degree to which the product or service
design specifications are met
6
Costs of Quality
Appraisal costs
inspection and testing
Prevention costs
quality planning and training
Internal failure costs
scrap, rework, yield loss, downtime
External Failure costs
complaint adjustment, allowances,
warranty work
7
Cost per good unit of product
Quality Cost: Traditional View
Internal
and external
failure
costs
Total
quality
costs
Prevention
and appraisal
costs
0
Quality level (q)
Minimum
total cost
Optimum
quality level
100%
Phases of Quality Assurance
Inspection
before/after
production
Acceptance
sampling
The least
progressive
Corrective
action during
production
Process
control
Quality built
into the
process
Continuous
improvement
The most
progressive
12
PDCA Cycle (Deming Wheel)
1. Plan a change
aimed at
improvement.
4. Institutionalize
the change or
abandon or
do it again.
3. Study the results;
did it work?
4. Act
1. Plan
3. Check
2. Do
2. Execute the
change.
13
Ishikawa’s Basic Tools of Quality
Check Sheets
Histogram
Pareto Charts
Cause & Effect
Diagrams
Control Charts
Flowcharts
Scatter
Diagrams
14
Histograms
Number of Lots
Graphical representation of data in a bar chart format
Can be used to identify the frequency of quality
defect occurrence and display quality performance.
0
1
2
Data Ranges
3
4
Defects
in lot
15
Pareto Charts
80%
Frequency
Can be used
to find when
80% of the
problems may
be attributed
to 20% of the
causes.
Design
Assy.
Purch.
Training Other
Instruct.
16
Pareto Charts
The Steps Used in Pareto Analysis
Include:
– Gathering categorical data relating to
quality problems.
– Drawing a histogram of the data.
– Focusing on the tallest bars in the
histogram first when solving the problem
17
Cause and Effect
Diagrams
Cause and Effect (or Fishbone or
Ishikawa) Diagram
– A diagram designed to help workers focus
on the causes of a problem rather than
the symptoms.
– The diagram looks like the skeleton of a
fish, with the problem being the head of the
fish, major causes being the “ribs” of the
fish and subcauses forming smaller
“bones” off the ribs.
18
Cause & Effect Diagram
Possible causes:
Machine
Man
Effect
Environment
Method
The results
or effect
Material
Can be used to systematically track backwards to
find a possible cause of a quality problem (or effect)
19
Cause and Effect
Diagrams
20
Check Sheets
Monday
Can be used to keep track of
defects or used to make sure
people collect data in a correct
manner.
Billing Errors
Wrong Account
Wrong Amount
A/R Errors
Wrong Account
Wrong Amount
21
Check Sheets
Setting Up a Check Sheet
– Identify common defects occurring in the
process.
– Draw a table with common defects in the left
column and time period across the tops of
the columns to track the defects.
– The user of the check sheet then places
check marks on the sheet whenever the
defect is encountered.
22
Check Sheets
23
Defects
Scatter Diagrams
Can be used to illustrate the
relationships between
variables (Example: quality
performance and training).
12
10
8
6
4
2
0
0
10
20
Hours of Training
30
Scatter Diagrams
Used to examine the relationships
between variables:
Steps in Setting Up a Scatter Plot
– Determine your X (independent) and Y
(dependent) variables.
– Gather process data relating to the variables
identified in step 1.
– Plot the data on a two-dimensional
Cartesian plane.
– Observe the plotted data to see whether
there is a relationship between the variables.
25
Scatter Diagrams
Prevention in Costs and Conformance
26
Flowcharts
Flowcharts:
Picture of a process
Allows a company to see process weaknesses
Sometimes the first step in many process
improvement projects to see how the process
exists
“You have to be able to know the process
before you can improve it”
27
Example: Process Flow Chart
Material
Received
from
Supplier
No,
Continue…
Inspect
Material for
Defects
Defects
found?
Yes
Can be used to find
quality problems.
Return to
Supplier for
Credit
28
Flowcharts
Basic Flowcharting Symbols
29
Flowcharts
Steps in Flowcharting Include
– Settle on a standard set of flowcharting
symbols to be used.
– Clearly communicate the purpose of the
flowcharting to all the individuals involved in
the flowcharting exercise.
– Observe the work being performed by
shadowing the workers performing the work.
– Develop a flowchart of the process.
30
Control Charts
Control Charts
– Control charts are used to determine
whether a process will produce a product or
service with consistent measurable
properties.
– Control charts are discussed in detail in
Technical Note 7.
31
Diameter
Example: Run Chart
Can be used to identify
when equipment or
processes are not behaving
according to specifications.
0.58
0.56
0.54
0.52
0.5
0.48
0.46
0.44
1
2
3
4
5
6
7
8
Time (Hours)
9
10
11
12
Example: Control Chart
Can be used to monitor ongoing production process quality
and quality conformance to stated standards of quality.
1020
UCL
1010
1000
990
LCL
980
970
0
1
2
3 4
5
6
7
8
9
10 11 12 13 14 15
Six Sigma Quality
 6
A philosophy and set of methods
companies use to eliminate defects in
their products and processes
Seeks to reduce variation in the processes
that lead to product defects
The name, “six sigma” refers to the
variation that exists within plus or minus
six standard deviations of the process
outputs
34
Six Sigma Quality (Continued)
Six Sigma allows managers to readily
describe process performance using a
common metric: Defects Per Million
Opportunities (DPMO)
DPMO =
Number of defects
x 1,000,000
 Number of 
 opportunities 
 for error per  x No. of units
 unit



35
Six Sigma Quality (Continued)
Example of Defects Per Million
Opportunities (DPMO) calculation.
Suppose we observe 200 letters
delivered incorrectly to the wrong
addresses in a small city during a
single day when a total of 200,000
letters were delivered. What is the
DPMO in this situation?
DPMO =
200
 1  x 200,000
So, for every one
million letters
delivered this
city’s postal
managers can
expect to have
1,000 letters
incorrectly sent
to the wrong
address.
x 1,000,000 = 1, 000
Cost of Quality: What might that DPMO mean in terms
of over-time employment to correct the errors?
36
Six Sigma Quality: DMAIC
Cycle (Continued)
1. Define (D)
Customers and their priorities
2. Measure (M)
Process and its performance
3. Analyze (A)
Causes of defects
4. Improve (I)
Remove causes of defects
5. Control (C)
Maintain quality
38
Second Part
Statistical Process Control
42
Statistical Thinking
All work occurs in a system of
interconnected processes
Variation exists in all processes
Understanding and reducing variation are
the keys to success
43
Sources of Variation in Production
Processes
Materials
INPUTS
Operators
Measurement
Instruments
Methods
PROCESS
OUTPUTS
Tools
Machines
44
Environment
Human
Inspection
Performance
Variation
Many sources of uncontrollable
variation exist (common causes)
Special (assignable) causes of
variation can be recognized and
controlled
Failure to understand these differences
can increase variation in a system
45
Problems Created by
Variation
Variation increases unpredictability.
Variation reduces capacity utilization.
Variation makes it difficult to find root causes.
Variation makes it difficult to detect potential
problems early.
46
Importance of
Understanding Variation
time
PREDICTABLE
?
47
UNPREDECTIBLE
Two Fundamental
Management Mistakes
1.
2.
48
Treating as a special cause any fault, complaint,
mistake, breakdown, accident or shortage when it
actually is due to common causes
Attributing to common causes any fault, complaint,
mistake, breakdown, accident or shortage when it
actually is due to a special cause
Types of Data
Variables Data
• Length
• Weight
• Time
Attribute Data
“Things we measure”
• Height
• Volume
• Temperature
• Diameter
• Tensile Strength
• Strength of Solution
“Things we count”
• Number or percent of defective items in a lot.
• Number of defects per item.
• Types of defects.
• Value assigned to defects
(minor=1, major=5, critical=10)
50
Process Control Charts
Variables and Attributes
Variables
Attributes
X (process population average)
P (proportion defective)
X-bar (mean for average)
np (number defective)
R (range)
C (number conforming)
MR (moving range)
U (number nonconforming)
S (standard deviation)
52
Process Control Charts
X-bar and R Charts
– The X-bar chart is a process chart used to
monitor the average of the characteristics
being measured. To set up an X-bar chart
select samples from the process for the
characteristic being measured. Then form the
samples into rational subgroups. Next, find
the average value of each sample by dividing
the sums of the measurements by the sample
size and plot the value on the process control
X-bar chart.
57
Process Control Charts
X-bar and R Charts (continued)
– The R chart is used to monitor the variability
or dispersion of the process. It is used in
conjunction with the X-bar chart when the
process characteristic is variable. To develop
an R chart, collect samples from the process
and organize them into subgroups, usually of
three to six items. Next, compute the range,
R, by taking the difference of the high value in
the subgroup minus the low value. Then plot
the R values on the R chart.
58
Process Control Charts
X-bar and R Charts
59
Example of x-Bar and R Charts:
Required Data
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Obs 1
10.682
10.787
10.78
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.66
Obs 2
10.689
10.86
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
Obs 3
10.776
10.601
10.838
10.812
10.79
10.738
10.689
10.11
10.641
10.708
10.764
10.818
10.893
10.859
10.644
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.85
10.658
10.872
10.544
10.801
10.747
Obs 5
10.714
10.779
10.723
10.73
10.671
10.606
10.603
10.75
10.725
10.712
10.708
10.727
10.75
10.701
10.728
60
Example of x-bar and R charts: Step 1. Calculate
sample means, sample ranges, mean of means,
and mean of ranges.
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Obs 1
10.682
10.787
10.780
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.660
Obs 2
10.689
10.86
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
Obs 3
10.776
10.601
10.838
10.812
10.79
10.738
10.689
10.11
10.641
10.708
10.764
10.818
10.893
10.859
10.644
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.85
10.658
10.872
10.544
10.801
10.747
Obs 5
10.714
10.779
10.723
10.73
10.671
10.606
10.603
10.75
10.725
10.712
10.708
10.727
10.75
10.701
10.728
Averages
Avg
10.732
10.755
10.759
10.727
10.724
10.705
10.735
10.624
10.710
10.732
10.748
10.768
10.733
10.783
10.692
Range
0.116
0.259
0.171
0.221
0.119
0.143
0.274
0.669
0.132
0.179
0.163
0.250
0.349
0.158
0.103
10.728 0.220400 61
Example of x-bar and R charts: Step 2.
Determine Control Limit Formulas and
Necessary Tabled Values
x Chart Control Limits
UCL = x + A 2 R
LCL = x - A 2 R
R Chart Control Limits
UCL = D 4 R
LCL = D 3 R
n
2
3
4
5
6
7
8
9
10
11
A2
1.88
1.02
0.73
0.58
0.48
0.42
0.37
0.34
0.31
0.29
D3
0
0
0
0
0
0.08
0.14
0.18
0.22
0.26
D4
3.27
2.57
2.28
2.11
2.00
1.92
1.86
1.82
1.78
1.74
62
Example of x-bar and R charts: Steps 3&4.
Calculate x-bar Chart and Plot Values
UCL = x + A 2 R = 10.728 .58(0.2204
) = 10.856
LCL = x - A 2 R = 10.728- .58(0.2204
) = 10.601
1 0 .9 0 0
UCL
1 0 .8 5 0
M ea n s
1 0 .8 0 0
1 0 .7 5 0
1 0 .7 0 0
1 0 .6 5 0
1 0 .6 0 0
LCL
1 0 .5 5 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Sam ple
63
Example of x-bar and R charts: Steps 5&6.
Calculate R-chart and Plot Values
UCL = D4 R = (2.11)(0.2204) = 0.46504
LCL = D3 R = (0)(0.2204) = 0
0 .8 0 0
0 .7 0 0
0 .6 0 0
0 .5 0 0
R
UCL
0 .4 0 0
0 .3 0 0
0 .2 0 0
0 .1 0 0
LCL
0 .0 0 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
S a m p le
64
•Interpreting Control
Charts
UCL
Normal Behavior
LCL
1
2
3
4
5
6
Samples
over time
UCL
Possible problem, investigate
LCL
1
2
3
4
5
6
Samples
over time
UCL
Possible problem, investigate
LCL
1
2
3
4
5
6
Samples
over time 65
Process Control Charts
Implications of a Process Out of Control
– If a process loses control and
becomes nonrandom, the process
should be stopped immediately.
– In many modern process industries where
just-in-time is used, this will result in the
stoppage of several work stations.
– The team of workers who are to address the
problem should use a structured problem
solving process.
66
Process Control Charts
Control Charts for Attributes
– We now shift to charts for attributes. These
charts deal with binomial and Poisson
processes that are not measurements.
– We will now be thinking in terms of defects
and defectives rather than diameters or
widths.
A defect is an irregularity or problem with a larger
unit.
A defective is a unit that, as a whole, is not
acceptable or does not meet specifications.
67
Process Control Charts
p Charts for Proportion Defective
– The p chart is a process chart that is used to
graph the proportion of items in a sample that
are defective (nonconforming to
specifications)
– p charts are effectively used to determine
when there has been a shift in the proportion
defective for a particular product or service.
– Typical applications of the p chart include
things like late deliveries, incomplete orders,
and clerical errors on written forms.
68
Process Control Charts
np Charts
– The np chart is a graph of the number of
defectives (or nonconforming units) in a
subgroup. The np chart requires that the
sample size of each subgroup be the same
each time a sample is drawn.
– When subgroup sizes are equal, either the p
or np chart can be used. They are essentially
the same chart.
69
Example of Constructing a p-Chart:
Required Data
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
4
2
5
3
6
4
3
7
1
2
3
2
2
8
3
70
Statistical Process Control Formulas:
Attribute Measurements (p-Chart)
Given:
T o ta l N u m b e r o f D e fe c tiv e s
p =
T o ta l N u m b e r o f O b se rv a tio n s
sp =
p (1- p)
n
Compute control limits:
UCL = p + z sp
LCL = p - z sp
71
Example of Constructing a p-chart:
Step 1
1. Calculate the sample
proportions, p (these
are what can be plotted
on the p-chart) for each
sample.
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
n Defectives
100
4
100
2
100
5
100
3
100
6
100
4
100
3
100
7
100
1
100
2
100
3
100
2
100
2
100
8
100
3
p
0.04
0.02
0.05
0.03
0.06
0.04
0.03
0.07
0.01
0.02
0.03
0.02
0.02
0.08
0.03
72
Example of Constructing a p-chart:
Steps 2&3
2. Calculate the average of the sample proportions.
55
p =
= 0.036
1500
3. Calculate the standard deviation of the sample
proportion
sp =
p (1 - p)
=
n
.036(1- .036)
= .0188
100
73
Example of Constructing a p-chart:
Step 4
4. Calculate the control limits.
UCL = p + z sp
LCL = p - z sp
.036  3(.0188)
UCL = 0.0924
LCL = -0.0204 (or 0)
74
Example of Constructing a p-Chart:
Step 5
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
5. Plot the individual sample proportions, the
average of the proportions, and the control limits
0.16
0.14
0.12
0.1
p 0.08
0.06
n Defectives
100
4
100
2
100
5
100
3
100
6
100
4
100
3
100
7
100
1
100
2
UCL
100
3
100
2
100
2
100
8
100
3
0.04
0.02
LCL
0
1
2
3
4
5
6
7
8
9
Observation
10
11
12
13
14
15
75
p
0.04
0.02
0.05
0.03
0.06
0.04
0.03
0.07
0.01
0.02
0.03
0.02
0.02
0.08
0.03
Process Capability
Process Stability and Capability
– Once a process is stable, the next
emphasis is to ensure that the process is
capable.
– Process capability refers to the ability of a
process to produce a product that meets
specifications.
76
Process Capability
Process limits
Tolerance limits
How do the limits relate to one
another?
77
If the process capability of a normally
distributed process is .084, the process is
in control, and is centered at .550. What
are the upper and lower control limits for
this process?
Process Capability = 6 
6  = .084
 = .014
UCL = .550 + 3(.014) =
.592
LCL = .550 - 3(.014) =
.508
78
Process Capability Chart
Process output
distribution
Output
out of spec
Output
out of spec
5.010
4.90
4.95
5.05
5.00
X
5.10
5.15
cm
Tolerance band
LTL
UTL
Process capability (6 s )
79
Process Capability
This process is
CAPABLE of
producing all good
output.
ä Control the process.
Lower
Tolerance
Limit
Upper
Tolerance
Limit
×
This process is
NOT CAPABLE.
ä INSPECT - Sort out
the defectives
80
Process Capability Index, Cpk
Capability Index shows
how well parts being
produced fit into design
limit specifications.
As a production
process produces
items small shifts in
equipment or systems
can cause differences
in production
performance from
differing samples.
 X  LTL UTL - X 

C pk = min 
or
3 
 3
Shifts in Process Mean
81
Process Capability Index- Example
Given:
process mean = 1.0015
 = .001
Cpk=
LTL = .994
UTL = 1.006
{
Smaller of:
OR
1.0015 -.994
or
3(.001)
Cpk=
min
Cpk=
min [2.5 or 1.5] = 1.5
–
Upper Tol Limit - X
3
–
X - Lower Tol Limit
3
1.006 - 1.0015
3 (.001)
82
Process Capability: Cpk Varieties
Cpk = 1.0
LTL
Cpk = 1.33
UTL
LTL
Cpk = 3.0
UTL
LTL
(d)
(f)
Cpk = 1.0
LTL
UTL
Cpk = 0.60
UTL
LTL
Cpk = 0.80
UTL LTL
UTL
83
Acceptance Sampling
Acceptance Sampling
– A statistical quality control technique used
in deciding to accept or reject a shipment
of input or output.
– Acceptance sampling inspection can range
from 100% of the Lot to a relatively few
items from the Lot (N=2) from which the
receiving firm draws inferences about the
whole shipment.
86
Acceptance Sampling
Purposes
–
–
Determine quality level
Ensure quality is within predetermined level
Advantages
–
–
–
–
–
–
Economy
Less handling damage
Fewer inspectors
Upgrading of the inspection job
Applicability to destructive testing
Entire lot rejection (motivation for improvement)
87
Acceptance Sampling
Disadvantages
–
–
–
Risks of accepting “bad” lots and rejecting
“good” lots
Added planning and documentation
Sample provides less information than 100percent inspection
88
Statistical Sampling
Techniques
n and c
– The bottom line in acceptance sampling is
that acceptance sampling plans are
designed to give us two things: n and c,
where
n = the sample size of a particular sampling plan
c = the maximum number of defective pieces for
a sample to be rejected
90