Operations
8
473.31
Fall 2015
Bruce Duggan
Providence University College
Summary
The definition of quality has been
expanded to include organizationwide quality issues such as the
quality of the work environment for
employees.
DMAIC, the acronym for Define,
Measure, Analyze, Improve, and
Control, is fundamental to the
approach companies use to guide
improvement projects.
Six sigma processes are designed to
produce very few defects.
Statistical process control techniques
include control charts and
acceptance sampling, which ensure
that processes are operating as they
are designed to operate.
6-20
Know The Answers To These Questions
1.
What does the “total” in total quality management (TQM) mean?
2.
How is quality measured? What are the “dimensions” of quality?
3.
How can two companies spend the amount on quality, but one have far superior
quality?
4.
What is the difference between ISO 9000, ISO 14000, and ISO 26000?
5.
Does Six Sigma’s DMAIC methodology stand for “Dumb Managers Always Ignore
Customers”?
6.
How can we calculate if our process is capable of meeting external specifications?
7.
How does a control chart help you know when to stop a process and investigate it?
8.
Can acceptance sampling be used on raw materials sent from a supplier?
6-21
Total Quality Management
Total Quality Management is defined as “managing the entire
organization so that it excels on all dimensions of products and services
that are important to the customer.”
Quality Gurus Compared
8-4
Quality Specifications
Design quality:
• Inherent value of the product in the marketplace.
Conformance quality:
• Degree to which the product or service design specifications are met.
8-5
Quality Specifications
Quality Specifications
Cost of Quality
• appraisal costs
• prevention costs
• internal failure costs
• external failure costs
ISO 9000
Series of standards agreed upon by the International Organization for
Standardization (ISO).
The idea behind the standards is that defects can be prevented through
the planning and application of “best practices” at every stage in the
business.
A prerequisite for global competition?
ISO 9000 directs you to "document what you do and then do as you
documented."
Recognition for Good Quality
Canada Award for Excellence (CAE)
• An award established on behalf of the Canadian government given annually
to companies that excel in organization wide quality.
Malcolm Baldrige National Quality Award
• An award established by the U.S. Department of Commerce given annually to
companies that excel in quality.
Recognition for Good Quality
8-11
Six Sigma Quality
Six Sigma refers to a statistical term to describe the quality goal of no
more than four defects out of every million units.
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 three standard deviations of the process outputs
Six Sigma Quality
Six Sigma allows managers to readily describe process performance
using a common metric: Defects Per Million Opportunities (DPMO)
DPMO 
Numberof defects
 Numberof 
 opportunit
ies  x No.of units
 for error per 
 unit

x 1,000,000
Six Sigma Quality: DMAIC Cycle
Overall focus of the methodology
is to understand and achieve what
the customer wants.
A 6-sigma program seeks to
reduce the variation in the
processes that lead to these
defects.
DMAIC:
•
Define
•
Measure
•
Analyze
•
Improve, and
•
Control
8-14
Six Sigma Quality: DMAIC Cycle
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
Analytical Tools for Six Sigma
Define
Analytical Tools for Six Sigma
Measure
Analytical Tools for Six Sigma
Analyze
Analytical Tools for Six Sigma
Improve
Analytical Tools for Six Sigma
Control
Analytical Tools for Six Sigma
Failure Mode and Effect Analysis (FMEA) is a structured approach to
identify, estimate, prioritize, and evaluate risk of possible failures at
each stage in the process.
Design of Experiments (DOE) a statistical test to determine cause-andeffect relationships between process variables and output.
Statistical Quality Control
The quantitative aspects of quality management.
Processes usually exhibit some variation in their output.
Some variation can be controlled and others are inherent in the
process.
Statistical Quality Control
Assignable variation is caused by factors that can be clearly identified
and possibly managed.
• Example:
o A poorly trained employee that creates variation in finished product output.
Common variation is inherent in the production process
• Example:
o A molding process that always leaves “burrs” or flaws on a molded item.
Statistical Quality Control
Statistical Quality Control
8-25
Process Capability
Process limits
Specification limits
How do the limits relate to one another?
Process Capability
Process Capability
8-28
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.
C pk
 X  LTL
UTL - X 


= min 
or

3

3



Shifts in Process Mean
8-29
Statistical Process Control (SPC)
Techniques for testing a random sample of output from a process to
determine whether the process is producing items within a preselected
range.
Types of Statistical Sampling
Attribute (go or no-go information)
• Defectives refers to the acceptability of product across a range of
characteristics.
• Defects refers to the number of defects per unit which may be higher than
the number of defectives.
• p-chart application
Variable (continuous)
• Usually measured by the mean and the standard deviation.
• X-bar and R chart applications
8-31
Control Limits
We establish the Upper Control Limits (UCL) and the Lower Control
Limits (LCL) with plus or minus 3 standard deviations from some x-bar
or mean value. Based on this we can expect 99.7% of our sample
observations to fall within these limits.
99.7%
LCL
UCL
x
Statistical Process Control (SPC)
Using p Charts
[8.4]
[8.5]
[8.6]
[8.7]
p
Total number of defects from all samples
Number of samples  Sample Size
sp 
p 1  p 
n
UCL  p  zs p
LCL  p  zs p
Using p Charts
Using X-bar and R Charts
Using X-bar and R Charts
Using X-bar and R Charts
Using X-bar and R Charts
Statistical Sampling for Quality Control
Acceptance Sampling is performed on goods that already exist to
determine what percentage of product conforms to specifications.
Statistical Process Control is sampling to determine if the process is
within acceptable limits.
Acceptance Sampling
purposes
• the purpose of a sampling plan is to test the lot to either :
o determine quality level
o ensure that the quality is what it is supposed to be
Acceptance Sampling
advantages
• economy
• less handling damage
• fewer inspectors
• upgrading of the inspection job
• applicability to destructive testing
• entire lot rejection (motivation for improvement)
Acceptance Sampling
disadvantages
• risks of accepting “bad” lots and rejecting “good” lots
• added planning and documentation
• sample provides less information than 100-percent inspection
Acceptance Sampling
Single Sampling Plan
• a simple goal:
• determine
1.
how many units, n, to sample from a lot, and
2.
the maximum number of defective items, c, that can be found in the sample before
the lot is rejected.
Risk
Acceptable Quality Level (AQL)
• maximum acceptable percentage of defectives defined by producer
the α
• the Producer’s risk
• the probability of rejecting a good lot
Lot Tolerance Percent Defective (LTPD)
• percentage of defectives that defines consumer’s rejection point
the 
• the Consumer’s risk
• the probability of accepting a bad lot
Operating Characteristic Curve (OCC)
The OCC brings the concepts of producer’s risk, consumer’s risk,
sample size, and maximum defects allowed together.
The shape or slope of the curve is dependent on a particular
combination of the four parameters.
Operating Characteristic Curve
Example:
• The vendor produces circuit boards to parameters of:
o AQL = 0.02
o LTPD = 0.08
o 5% risk of having lots of this level or fewer defectives rejected
o acceptance of poor-quality lots no more than 10%
• What values of n and c should be selected to determine the quality of this
lot?
Example: Operating Characteristic Curve
Example: Operating Characteristic Curve
Now given the information below, compute the sample size in units to
generate your sampling plan.
c = 4, from Table
n (AQL) = 1.970, from Table
n = 98.5, round up to 99
Therefore, the appropriate sampling plan is c = 4, n = 99.
Operating Characteristic Curve (OCC)
End of Chapter 8
Ken
Hin
•7
• 12
• 11
• 13
• 14
• 14
Suggest you work o 8 & 9 together for practice.