Transcript Collaborative Business Networks and Technology Companies

Fifteen:
Overview of Total Quality Tools
MAJOR TOPICS
 Total Quality Tools Defined
 The Pareto Chart
 Cause-and-Effect Diagrams
 Check Sheets
 Histograms
 Scatter Diagrams
 Run Charts and Control Charts
 Stratification
 Some Other Tools Introduced
 Management’s Role in Tool Deployment
Fifteen:
Overview of Total Quality Tools
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Pareto charts are useful for separating the important
from the trivial. They are named after Italian economist
and sociologist Vilfredo Pareto. Pareto charts are
important because they can help an organization
decide where to focus limited resources. On a Pareto
chart, data are arrayed along an X-axis and a Y-axis.
Pareto charts
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The Pareto principle suggests that most effects come from relatively few
causes. In quantitative terms: 80% of the problems come from 20% of the
causes (machines, raw materials, operators etc.); 80% of the wealth is
owned by 20% of the people etc. Therefore effort aimed at the right 20%
can solve 80% of the problems. Double (back to back) Pareto charts can
be used to compare 'before and after' situations. General use, to decide
where to apply initial effort for maximum effect.
Pareto charts
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There are six common steps to prepare a Pareto Diagram:
Decide which data should be shown on your chart. Decide on the time period for which
you will collect the data.
Collect your data on a worksheet (e.g., from budgets, or from cost reports).
Construct your Pareto Diagram from the data you collected. Arrange the data cells in
descending order from the left of the graph.
Add the information to make your chart readable to other people.
cause-and-effect diagram
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The cause-and-effect diagram was developed by
the late Dr. Kaoru Ishikawa, a noted Japanese
quality expert; others have thus called it the
Ishikawa diagram. Its purpose is to help identify and
isolate the causes of problems. It is the only one of
the seven basic quality tools that is not based on
statistics.
cause-and-effect diagram
cause-and-effect diagram
Histograms
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Histograms have to do with variability. Two kinds of data
are commonly associated with processes: attributes data
and variables data. An attribute is something that the output
product of the process either has or does not have.
Variables data are data that result when something is
measured. A histogram is a measurement scale across one
axis and a frequency of like measurements on the other.
Histograms
Frequency
Measurements
Histograms
Histograms
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In this abbreviated example, a bowler wants to improve her game. Her scores, collected over
a six-week period, look like this:
Week 1: 96, 130, 100
Week 2: 150, 135, 115
Week 3: 145, 148, 120
Week 4: 155, 110, 125
Week 6: 175, 135, 140
The cell widths will be 20 points. These cells of scores have this distribution:
91 - 110 = 3
111 - 130 = 3
131 - 150 = 6
151 - 170 = 2
171 - 190 = 3
Histograms
Histograms
Check Sheets
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The check sheet is a tool that facilitates collection of relevant
data, displaying it in a visual form easily understood by the
brain. Check sheets make it easy to collect data for specific
purposes and to present it in a way that automatically converts it
into useful information.
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A Check Sheet is a data recording form that has been designed
to readily interpret results from the form itself. It needs to be
designed for the specific data it is to gather. Used for the
collection of quantitative or qualitative repetitive data. Adaptable
to different data gathering situations. Minimal interpretation of
results required. Easy and quick to use. No control for various
forms of bias - exclusion, interaction, perception, operational,
non-response, estimation.
David Goetsch
Quality Management, 5e
Copyright ©2006 by Pearson Education, Inc.
Upper Saddle River, New Jersey 07458
All rights reserved.
Check Sheets
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How to Construct:
Clearly define the objective of the data collection.
Determine other information about the source of the data that should be
recorded, such as shift, date, or machine.
Determine and define all categories of data to be collected.
Determine the time period for data collection and who will collect the data.
Determine how instructions will be given to those involved in data collection.
Design a check sheet by listing categories to be counted.
Pilot the check sheet to determine ease of use and reliability of results.
Modify the check sheet based on results of the pilot.
Check Sheets
Tips:
Use Ishikawa diagrams or Brainstorming to determine
categories to be used on the check sheet.
Construct an operational definition of each category to ensure
data collected is consistent.
Make check sheet as clear and easy to use as possible.
Spend adequate time explaining the objective of the data
collection to those involved in recording the data to ensure
the data will be reliable.
Data collected in this format facilitates easy Pareto analysis.
Scatter Diagrams
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The scatter diagram is arguably the simplest of the seven basic
quality tools. It is used to determine the correlation between two
variables. It can show a positive correlation, a negative
correlation, or no correlation.
Scatter Diagrams
Scatter Diagrams
Scatter Diagrams
Scatter Diagrams
A Services Example
Flight delays at Midway
• Cause and Effect Diagrams
• Check Sheets
• Pareto Analysis
Problem: Delayed Flights
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No one is sure why, but plenty of opinions
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“Management by Fact”
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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
(root cause
II
I
III
analysis -- closed phase)
II
Pareto Analysis
Late arrivals
Late baggage to aircraft
Weather
65
70
85
100
(sorted histogram)
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
Control Charts
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In the context of the seven total quality tools, run
charts and control charts are typically thought of as
being one tool together. The control chart is a more
sophisticated version of the run chart. The run chart
records the output results of a process over time.
For this reason, the run chart is sometimes called a
trend chart. The weakness of the run chart is that it
does not tell whether the variation is the result of
special causes or common causes. This weakness
gave rise to the control chart.
Control Charts
Control Charts
Control Charts
Data are plotted just as they are on a run chart, but a
lower control limit, an upper control limit, and a
process average are added. The plotted data stays
between the upper control limit and lower control
limit while varying about the center line or average
only so long as the variation is the result of
common causes such as statistical variation.
Control Charts
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In general, control charts are used to plot production values and
variation over time. These charts can then be analyzed to
determine if changes in production values or variation are due to
the inherent variability of the process or a specific correctable
cause. Control charts for variables are fairly straightforward and
can be quite useful in material production and construction
situations. Four popular control charts within the manufacturing
industry are
Control chart for variables.
Control chart for attributes.
Cumulative sum control chart.
Exponentially weighted moving average (EWMA) control chart.
Control Charts
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Control chart for variables. In variable sampling, measurements are
monitored as continuous variables. Because they retain and use
actual measurement data, variable sampling plans retain more
information per sample than do attribute sampling plans (Freeman
and Grogan, 1998). This implies that compared to attribute sampling,
it takes fewer samples to get the same information. Because of this,
most statistical acceptance plans use variable sampling.
Control chart for attributes. This chart is used when a number cannot
easily represent the quality characteristic. Therefore, each item is
classified as "conforming" or "nonconforming" to the particular
specification for the quality characteristic being examined. These
charts look similar to control charts for variables but are based on a
binomial distribution instead of a normal distribution. Two of the most
common attribute control charts are for fraction nonconforming and
defects.