Transcript Managing Quality - Yahoo Small Business

Managing Quality
Chapter Objectives
Be able to:
 Discuss the various definitions and dimensions of quality
and why quality is important to operations and supply
chains.
 Describe the different costs of quality, including internal
and external failure, appraisal, and prevention costs.
 Know what TQM is, along with its seven core principles.
 Calculate process capability ratios and indices and set
up control charts for monitoring continuous variables
and attributes.
 Describe the key issues associated with acceptance
sampling, as well as the use of OC curves.
 Distinguish between Taguchi’s quality loss function and
the traditional view of quality.
Managing Quality
• Quality defined
• Total cost of quality
• Total quality management (TQM)
• Statistical quality control
• Managing quality across the supply
chain.
Definitions of Quality
• ASQ:
– The characteristics of a product or service that bear on
its ability to satisfy stated or implied needs
– Fitness for use (value perspective)
– Free from defects (conformance perspective)
• How would you evaluate the quality of the
following?
– Software package
– Hand-held vacuum cleaner
– No-frills air flight
Strategic Quality
Quality as a Competitive
Advantage
Dimensions of Quality
•
•
•
•
•
•
•
•
Performance
Features
Which dimensions do
you think are directly
Reliability
affected by Operations
Durability
and Supply Chain activities?
Conformance
Aesthetics
Serviceability
Perceived Quality
Quality Dimension Examples
Dimension
New Car
Tax Preparation
Performance
Tow capability; gas mileage
Cost and time to prepare taxes
Features
Accessories; extended warranty
Advance on refund check; E- filing
Reliability
Miles between required service
Not applicable
Durability
Expected useful life of the engine,
transmission, body
Not applicable
Conformance
Number of defects in the car
Number of mistakes on the tax return
Aesthetics
Styling, interior appearance
Neatness of the return
Serviceability
Qualified mechanics in the area?
Maintenance time and cost?
Will the tax preparation firm talk with
the IRS in case of an audit?
Perceived Quality
How do prices for used vehicles
hold up?
What is the reputation of the firm?
Defensive Quality
• Quality analyzed in economic terms
Total Cost of Quality:
$ Failure Costs
$ Appraisal Costs
$ Prevention Costs
Total Cost of Quality
— Traditional View
Cost per defectfree unit of product
($)
Total Cost
of Quality
Minimum Total
Cost
Appraisal Costs
Prevention
Costs
100% Defects
Internal/External
Failure Costs
Q* = Optimal Quality
0% Defects
Zero Defects View
Cost per defectfree unit of product
Total Cost
of Quality
($)
The total
costs of
quality fall
as defect
levels
decrease
Minimum Total
Cost
Prevention Costs
Appraisal Costs
Internal/External
Failure Costs
100% Defects
0% Defects
Q* = Optimal Quality
Total Quality Management
(TQM)
Managing the entire organization so that it
excels in all dimensions important to the
customer.
 Product development
 Marketing
 Operations
 Supply chain
 Support services
TQM Principles
•
•
•
•
•
Customer focus
Leadership involvement
Continuous improvement
Employee empowerment
Quality assurance (including SQC or
SPC)
• Strategic partnerships
• Strategic quality plan
TQM Principles Expanded
Customer focus
 Each employee has a customer whether
internal or external to the company
Leadership involvement
 Must be ‘top’ down, throughout the company
 If not, major cause of TQM failures
Continuous improvement
 Supports other core principles
Performance
Continuous Improvement
(CI) versus “Leaps” Forward
Time
TQM Principles Expanded
Employee empowerment
 Key to success
 Lack of empowerment major cause of TQM/SPC
failures
Quality assurance
 Quality Function Deployment (QFD) discussed in
Chapter 6
 Statistical quality control (SQC), also called statistical
process control (SPC)
 Acceptance sampling (OC curve)
Switching Focus . . .
TQM to Quality Assurance
“Did we do it right?”
We Noted That
Organizations Must ...
• Understand which quality dimensions are
important
• Develop products and services that will
meet users’ quality needs
• Put in place business processes capable
of meeting these needs
• Verify that business processes are
meeting the specifications
Six Sigma Methodology
Core value is having less than 3.4 defects per
million opportunities (DPMO). Key elements are:
 Understanding and managing customer
requirements
 Aligning key business processes to achieve
those requirements
 Using rigorous data analysis to understand and
ultimately minimize variation in those processes
 Driving rapid and sustainable improvement to
business processes.
Six Sigma Methodology
Two basic Six Sigma processes are:
DMAIC (Define-Measure-AnalyzeImprove-Control) — an updated version of
the PDCA process promoted by Deming.
DMADV (Define-Measure-AnalyzeDesign-Verify)
The PDCA Cycle
Do
Plan
Check
Act
Common Improvement
Tools
 Cause and effect diagrams (aka “Fishbone”
or Ishikawa diagrams)
 Check sheets
 Pareto analysis
 Run charts and scatter plots
 Bar graphs
 Histograms
A Services Example
Flight delays at Midway
• Cause and Effect Diagrams
• Check Sheets
• Pareto Analysis
Problem: Delayed Flights
• No one is sure why, but plenty of opinions
• “Management by Fact”
• CI Tools we will use:
– Fishbone diagram
– Check sheets
– Pareto analysis
Cause and Effect Diagram
ASKS: What are the possible causes?
Root cause analysis — open and narrow phases
Generic C&E Diagram
Manpower
Methods
Effect
Materials
Machines
Measurements
Midway C&E diagram
Personnel
Procedures
Pay
Policy
Turnover
Number of Agents
Late Passengers
Cleaning Crews
Delayed Flights
Gate Occupied
Maintenance Problems
Equipment
We can further
subdivide these
by asking
“Why?” until we
get to the root
cause
Check Sheets
Event:
Late arrival
Day 1
Day 2
Day 3
II
II
I
Gate occupied
Too few agents
I
Accepting late
passengers
II
I
III
II
(root cause analysis -- closed phase)
Pareto Analysis
(sorted histogram)
Late arrivals
Late baggage to aircraft
Weather
65
70
85
100
Late passengers
Other (160)
Percent of each out of 480
total incidents ...
Late passengers 21%
Late arrivals 18%
Late baggage to aircraft 15%
Weather 14%
Other 33%
Run Charts and Scatter
Plots
Measure
Run
Time
Variable Y
Scatter
Variable X
Histograms
Frequency
Measurements
Process Capability
Answers the Question:
Can the process provide
acceptable quality
consistently?
Process Capability Ratio (Cp)
Upper Tolerance Limit – Lower Tolerance Limit
6σ
Where σ is the estimated
standard deviation
for the individual observations
Shown Graphically:
Mean
LTL
3
UTL
3
Process Capability ratio of 1
(99.7% within tolerance range)
“Six Sigma Quality”
Mean
LTL
6
UTL
6
When a process operates with 6σ variation centered between the
tolerance limits, only 2 parts out of a billion will be unacceptable.
Process Capability Index (Cpk)
  LTL UTL   

Cpk  min 
,

3

3



• Used when the process is not precisely centered
between the tolerance limits.
Discovering “problems”
• Inspect every item
• Expensive to do
• Testing can be destructive, should be
simply unnecessary
• Statistical techniques
Statistical process control (SPC)
Acceptance Sampling
Statistical Process Control
• “Representative” samples are measured
– good, but not perfect, picture of process
• Sampling by Variable (continuous values
— length, weight, area, volume, etc.)
• Sampling by Attribute (good, bad, #
defects/unit, %)
Example: Fabric Dyeing
• Rolls of fabric go through dyeing process
• Target temperature of 140 degrees
Too
low . . . ?
Too high
...?
• Temperature must be “monitored” and action
taken when something is “unusual”
• Is temperature a “variable” or an “attribute”?
Step 1:
Sampling the Process
Observation
2
3
4
Sample
1
5
1
136
137
144
141
138
2
143
138
140
140
139
3
140
141
144
137
135
4
139
140
141
139
141
5
137
138
143
140
138
6
142
141
140
139
138
7
143
141
143
140
140
8
139
139
141
140
136
9
140
138
143
141
139
10
139
141
142
140
136
Things
should
be
working
OK when
we do
this . . .
Step 2: Calculate the Mean
and Range for Each Sample
Sample
X
R
1
139.2
8
2
140
5
3
139.4
9
4
140
2
5
139.2
6
6
140
4
7
141.4
3
8
139
5
9
140.2
5
10
139.6
6
X = 139.8°
R = 5.3°
Step 3A: Use These Values
to Set Up X and R charts
Upper control limit for X chart:
UCLX = X + A2 × R = 142.9
Lower control limit for X chart:
LCLX = X – A2 × R = 136.7
Step 3B: Use These Values to
Set Up X and R charts (cont’d)
Upper control limit for R chart:
UCLR = D4 × R = 11.2
Lower control limit for R chart:
LCLR = D3 × R = 0
Use the Charts to Plot the
Following Data . . .
UCLX = 142.9
Sample
X
R
11
141.2
8
12
142
9
13
144
12
14
140
5
15
139.6
4
16
140.8
5
X-Bar = 139.8
LCLX = 136.7
UCLR = 11.2
R-Bar = 5.3
LCLR = 0
Out of
Contro
l
Sampl
e
What is the process capability
ratio for our dyeing example?
148  132
16

 1.107
6  2.41 14.46
What conclusions can you
draw?
σ = 2.41 from sample data
What would need to be for
us to have “” quality ?
12σ = UTL – LTL = 148 – 132
σ = 16/12 = 1.33
Sampling by Attribute
• Gonzo Pizza is interested in tracking the
proportion (%) of late deliveries
• Like before, you take several samples of say,
50 observations each when things are
“typical”
• For each sample, you calculate the proportion
of late deliveries and call this value p. For
example:
p = (8 late)/(50 deliveries) = 0.16
Gonzo Pizza (cont’d)
For all samples, calculate the average p:
0.16
0.20
0.00
0.14
0.10
p = 0.10
Gonzo Pizza (cont’d)
• Calculate standard deviation
for the p-chart as follows:
Sp 
p  (1  p)
 0.042
n
Where n = size of each sample = 50
Gonzo Pizza (cont’d)
And the control limits are:
UCLp = p + z × Sp = 0.226
LCLp = p – z × Sp = – 0.026, or zero
Here z is 3, but can be chosen as other
values to increase the sensitivity of the
chart to changes in the process.
Gonzo Pizza
• Although text says to go ahead with control
charts, consider that it is probably too early to
develop them since the process is not yet in
control (i.e., late deliveries are too high a
percentage at present). A more practical
approach would be:
– First, fix the more obvious problem(s)
– Then take new samples
– Then put in place control charts
Acceptance Sampling
Some definitions
• Acceptable quality level (AQL)
– Maximum defect level for 100% customer acceptance
• Lot tolerance percent defective (LTPD)
– Highest defect level customer will tolerate
• Consumer’s risk, 
– Probability of accepting a bad lot
• Producer’s risk, 
– Probability of rejecting a good lot
• Operating characteristics (OC) curve
– Probability of accepting a lot given the actual fraction defective in the
entire lot and the sampling plan being used.
Putting the terms together
OC Curve
The Big Picture
So how do TQM, continuous
improvement, and all these statistical
techniques “fit” together?
3 Lines of Defense
1) PREVENT defects from occurring
 TQM and continuous improvement
2) DISCOVER problems early
 Process control charts
3) CATCH DEFECTS before used or
shipped
 inspection / acceptance sampling
Traditional View of the Cost
of Variability
$
Cost of
Bad Quality
Low
Spec
Target
Spec
High
Spec
Taguchi’s Quality Loss
Function
An alternative perspective on
the
cost of quality
Consider Big Bob’s Axles ...
Axles have slightly larger
or smaller diameter than
target value
(
Wheels have slightly
larger or smaller holes
than target value
What are the possible outcomes?
Taguchi’s view of the cost of
variability
$
Cost of
Bad Quality
Target
Low
Spec
Spec
What are the managerial implications?
(HINT: think continuous improvement)
High
Spec
TQM Principles Expanded
Strategic partnerships
Value of good suppliers and distributors
i.e., GIGO (garbage in, garbage out)
Quality consistent throughout supply
chain
Strategic quality plan
ISO 9000 family of quality standards,
www.iso.org
American Society for Quality, www.asq.org
Managing Quality Case Study
Dittenhoefer’s Fine China