University of Hail
College of Engineering
ME 418- Quality in Manufacturing
2nd Semester 2012-2013
Chapter 2
Statistical Process Control
Prof. Mohamed Aichouni
Lectures notes adapted from: PowerPoint presentation to accompany
Besterfield, Quality Improvement, 9th edition
Course Webpage: faculty.uoh.edu.sa/m.aichouni/me418-quality/
Quality Improvement, 9e
Dale H. Besterfield
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Tool 4 - Histogram
A Histogram takes process data, e.g.,
temperature, dimensions, and displays
its distribution.
A Histogram reveals the amount of
variation that any process has within
it.
Figure 3-11 Histogram for
Hole Location
It shows the process capability and, if
desired, the relationship to the
specifications and the nominal
characteristic.
Quality Improvement, 9e
Dale H. Besterfield
31
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
1
Histogram
‰ Displays large amounts of data that are difficult to
interpret in tabular form
‰ Shows
Sh
centering,
t i
variation,
i ti
and
d shape
h
‰ Illustrates the underlying distribution of the data
‰ Provides useful information for predicting future
performance
‰ Helps to answer the question “Is the process capable of
meeting requirements?
32
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Quality Improvement, 9e
Dale H. Besterfield
Histogram
The shape of the histogram
help us to determine :
h h the
h “spread”
“
d” off
a.)) Whether
the distribution falls within
product specifications. If not,
how much falls outside of
specifications. (VARIABILITY)
b.)
b ) Whether the distribution is
centered at the right place.
Are most items on the “high or
low side?”(SKEWNESS)
Quality Improvement, 9e
Dale H. Besterfield
33
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
2
Practical Example of Histogram (Ex2-3)
A sample of process data were
obtained and drawn on the
table.
Use this data to construct a
histogram for analyzing the
process .
Quality Improvement, 9e
Dale H. Besterfield
34
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Practical Example of Histogram
Quality Improvement, 9e
Dale H. Besterfield
35
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
3
Practical Example of Histogram (Ex 2-4)
The thickness in mm of a key
material in process is given in
the table.
Use Minitab to construct the
histogram for the process.
Determine the process
statistics and compare them
with product specifications:
Quality Improvement, 9e
Dale H. Besterfield
36
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Practical Example of Histogram (Ex 2-4)
The data have a central
tendency around 9.8 to 9.99.
The p
process has a normal
distribution with a mean
(.......mm) and a standard
deviation (......... mm).
The specification for the
thickness characteristic
is 7.5 to 10.5, with a target of
9.
we can see th
thatt our
9 Thus,
Th
Histogram indicates the
process is running high
and that defective material is
being made.
Quality Improvement, 9e
Dale H. Besterfield
37
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
4
Tool 5 - Cause-and-Effect Diagram
‰ Developed by Dr. Kaoru Ishikawa in 1943
‰ Picture composed of lines and symbols designed to
represent a meaningful relationship between an effect
and its causes
‰ Effect
Eff t (characteristics
( h
t i ti that
th t need
d improvement)
i
t) on the
th
right and causes on the left.
‰ Called also Fishbone diagram or Ishikawa diagram.
Quality Improvement, 9e
Dale H. Besterfield
38
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Cause-and-Effect Diagram
‰ Enables a team to focus on the content of a
problem, not on the history of the problem or
differing personal interests of team members.
‰ Creates a snapshot of collective knowledge and
consensus of a team; builds support for solutions.
‰ Focuses the team on causes, not symptoms.
‰ Used
d to investigate either
h a “bad”
“b d” effect
ff
and
d to
take action to correct the causes or a “good”
effect and to learn those causes responsible.
Quality Improvement, 9e
Dale H. Besterfield
39
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
5
Cause-and-Effect Diagram
Steps in the construction of a
Cause-and-Effect Diagram:
g
:
1.
Identify the effect or quality problem
2.
Determine the major causes
3.
Determine all the minor causes. Request a
brainstorming session
4.
Once the diagram is complete, evaluate it to
determine the most likely causes
5.
Develop solutions
40
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Quality Improvement, 9e
Dale H. Besterfield
Cause-and-Effect Diagram
Advantages:
1.
Analyzing actual conditions for the purpose of
product or service quality improvement
2.
Elimination of conditions causing
nonconforming product or service and
customer complaints
3.
Standardization
S
d d
off existing and
d proposed
d
operations
4.
Education and training in decision-making
Quality Improvement, 9e
Dale H. Besterfield
41
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
6
Cause-and-Effect Diagram
People
Materials
Work Methods
Primary
Cause
Quality
Characteristic
Secondary Cause
Environment
Equipment
Measurement
The 4 M’s: Methods, Machines, Materials, Manpower
The 4 P’s: Place, Procedure, People, Policies
The 4 S’s: Surroundings, Suppliers, Systems, Skills
42
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Quality Improvement, 9e
Dale H. Besterfield
Cause and Effect Analysis
Measurement
Faulty testing equipment
Man
Machines
Out of adjustment
Poor supervision
Incorrect specifications
Lack of concentration
Improper methods
Inadequate training
Inaccurate
temperature
control
Defective from vendor
Materialhandling problems
Environment
Quality Improvement, 9e
Dale H. Besterfield
Old/worn
Quality
Problem
Not to specifications
Dust and Dirt
Tooling problems
Materials
Poor process design
IIneffective
ff ti quality
lit
management
Deficiencies
in product design
Methods
(Process)
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
7
Cause and Effect Analysis for Improvement of
the Engineering College (UoH
(UoH))
Policies
People (Students)
…….
……….
……
…….
…………….
…..
……..
…..
People (Faculty)
…………..
……..
…..
….
What
Can be
Improved
At the Eng
College ?
……
…..
…….
……
Environment
………
Teaching Facilities
Procedures
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Quality Improvement, 9e
Dale H. Besterfield
Tool 6 - Scatter Diagram
‰ A Scatter Diagram is used to study
the possible relationship between
variables
two variables.
‰ Used to test for possible cause
and effect relationships.
‰ When we need to display what
happens to one variable when
another variable changes in order
to test a theory that the two
variables are related.
Quality Improvement, 9e
Dale H. Besterfield
45
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
8
Scatter Diagram – Possible cases
The
following
g
are the
various
patterns
and
meanings
that Scatter
Diagrams
can have.
Quality Improvement, 9e
Dale H. Besterfield
46
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Scatter Diagram – Example
Scatter Diagram of
Average
g of
manufacturing
errors versus
Average hours
overtime / week in a
manufacturing
plant.
Average of Manufacturing Errors / Week
Quality Improvement, 9e
Dale H. Besterfield
47
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
9
Tool 7 - Control Chart
‰ Focuses attention on
detecting and monitoring
process variation over time.
‰ Distinguishes special from
common causes of variation
‰ Provides a common
language for discussion
process performance.
48
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Quality Improvement, 9e
Dale H. Besterfield
Control Charts
‰ Focuses attention on detecting and monitoring
process variation over time.
‰ Distinguishes special from common causes of
variation..
variation
‰ Provides a common language for discussion
process performance.
Quality Improvement, 9e
Dale H. Besterfield
49
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
10
Tracking Improvement using Control Charts
Control charts can be
used to monitor
processes and make
continuous process
improvement by
reducing process
variability (removing
special causes
variation).
Will be dealt in depth in
next chapters.
Quality Improvement, 9e
Dale H. Besterfield
50
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
SPC Tools : The 7 Basic Quality Tools
The SPC tools (the 7 basic Quality Tools) are Problem Solving
Tools which can:
† Help to identify and prioritize problems quickly and more
effectively.
† Assist the decision making process.
† Provide simple but powerful tools for use in continuous
improvement activity.
† Provide a vehicle for communicating problems and
resolutions throughout the business.
† Provide a way of extracting information from the data
collected.
Quality Improvement, 9e
Dale H. Besterfield
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
11
SPC Tools : The 7 Basic Quality Tools
1. Flow-charts - for describing a process, the current situation
2. Check Sheets - for data collection
3. Pareto Charts - for ordering causes
4. Histograms - for monitoring one variable.
5. Cause and Effect diagrams - for digging to the root causes
6. Scatter Diagrams - for examining the relationship between
two variables
7. Control Charts - for monitoring processes
Quality Improvement, 9e
Dale H. Besterfield
Conclusions
"Quality control truly
begins
and ends
g
with education",
K. Ishikawa (1990).
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
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Quality Improvement, 9e
Dale H. Besterfield
© 2013, 2008 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
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