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QUALITY IMPROVEMENT TOOLS:Data Tables, Identify the problem, Random method

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LESSON 38
QUALITY IMPROVEMENT TOOLS
BROAD CONTENTS
Seven Basic Tools of Statistical Process Control
38.1
Seven Basic Tools of Statistical Process Control (SPC):
They are as follows:
1. Data Tables
2. Cause-and Effect Analysis
3. Histograms
4. Pareto Analysis
5. Scatter Diagrams
6. Trend Analysis
7. Process Control Charts
Quality Improvement Tools:
Over the years, statistical methods have become prevalent throughout business, industry, and
science. With the availability of advanced, automated systems that collect, tabulate, and analyze
data; the practical application of these quantitative methods continues to grow. Statistics today
plays a major role in all phases of modern business.
More important than the quantitative methods themselves is their impact on the basic
philosophy of business. The statistical point of view takes decision making out of the subjective
autocratic decision-making arena by providing the basis for objective decisions based on
quantifiable facts.
This change provides some very specific benefits:
·
Improved process information
·
Better communication
·
Discussion based on facts
·
Consensus for action
·
Information for process changes
Statistical Process Control (SPC) takes advantage of the natural characteristics of any process.
All business activities can be described as specific processes with known tolerances and
measurable variances. The measurement of these variances and the resulting information
provide the basis for continuous process improvement. The tools presented here provide both a
graphical and measured representation of process data. The systematic application of these tools
empowers business people to control products and processes to become world-class
competitors.
The basic tools of statistical process control are data figures, Pareto analysis, cause-and-effect
analysis, trend analysis, histograms, scatter diagrams, and process control charts. These basic
tools provide for the efficient collection of data, identification of patterns in the data, and
measurement of variability.
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The following Figure 38.1 shows the relationships among these seven tools and their use for the
identification and analysis of improvement opportunities. We will review these tools and
discuss their implementation and applications.
Figure 38.1: Seven Quality Improvement Tools
38.1.1 Data Tables:
Data tables or data arrays provide a systematic method for collecting and displaying
data. In most cases, data tables are forms designed for the purpose of collecting specific
data. These tables are used most frequently where data is available from automated
media. They provide a consistent, effective, and economical approach to gathering data,
organizing them for analysis, and displaying them for preliminary review. Data tables
sometimes take the form of manual check sheets where automated data are not
necessary or available. Data figures and check sheets should be designed to minimize
the need for complicated entries. Simple-to-understand, straightforward tables are a key
to successful data gathering.
Figure 38.2 is an example of an attribute (pass/fail) data figure for the correctness of
invoices. From this simple check sheet several data points become apparent. The total
number of defects is 34. The highest number of defects is from supplier A, and the most
frequent defect is incorrect test documentation. We can subject this data to further
analysis by using Pareto analysis, control charts, and other statistical tools.
In this check sheet, the categories represent defects found during the material receipt
and inspection function. The following defect categories provide an explanation of the
check sheet:
·
Incorrect invoices: The invoice does not match the purchase order.
·
Incorrect inventory: The inventory of the material does not match the invoice.
·
Damaged material: The material received was damaged and rejected.
·
Incorrect test documentation: The required supplier test certificate was not received
and the material was rejected.
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Figure 38.2: Check Sheet for "Material Receipt and Inspection"
38.1.2 Cause-and -Effect Analysis (C and EA) "Fishbone":
After identifying a problem, it is necessary to determine its cause. The cause-and-effect
relationship is at times obscure. A considerable amount of analysis often is required to
determine the specific cause or causes of the problem.
Cause-and-effect analysis uses diagramming techniques to identify the relationship
between an effect and its causes. Cause-and-effect diagrams are also known as
fishbone diagrams. Figure 38.3 demonstrates the basic fishbone diagram. Six steps are
used to perform a cause-and-effect analysis.
Figure 38.3: Cause-and-Effect Diagram
Step 1 ­ Identify the problem:
This step often involves the use of other statistical process control tools, such as Pareto
analysis, histograms, and control charts, as well as brainstorming. The result is a clear,
concise problem statement.
Step 2 ­ Select interdisciplinary brainstorming team:
Select an interdisciplinary team, based on the technical, analytical, and management
knowledge required determining the causes of the problem.
Step 3 ­ Draw problem box and prime arrow:
The problem contains the problem statement being evaluated for cause and effect. The
prime arrow functions as the foundation for their major categories.
Step 4 ­ Specify major categories:
Identify the major categories contributing to the problem stated in the problem box. The
six basic categories for the primary causes of the problems are most frequently
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personnel, method, materials, machinery, measurements, and environment, as shown in
Figure 38.3. Other categories may be specified, based on the needs of the analysis.
Step 5 ­ Identify defect causes:
When you have identified the major causes contributing to the problem, you can
determine the causes related to each of the major categories. There are three approaches
to this analysis: the random method, the systematic method, and the process analysis
method.
Figure 38.4: Random Method
Random method: List all six major causes contributing to the problem at the same time.
Identify the possible causes related to each of the categories, as shown in Figure 38.4.
Systematic method: Focus your analysis on one major category at a time, in descending
order of importance. Move to the next most important category only after completing
the most important one. This process is diagrammed in Figure 38.5.
Figure 38.5: Systematic Method
Process analysis method: Identify each sequential step in the process and perform
cause-and-effect analysis for each step, one at a time. Figure 38.6 represents this
approach.
Figure 38.6: Process Analysis Methods
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Step 6 ­ Identify corrective action:
Based on (1) the cause-and-effect analysis of the problem and (2) the determination of
causes contributing to each major category, identify corrective action.
The corrective action analysis is performed in the same manner as the cause-and-effect
analysis. The cause-and-effect diagram is simply reversed so that the problem box
becomes the corrective action box. Figure 38.7 displays the method for identifying
corrective action.
Figure 38.7: Identify Corrective Action
38.1.3 Histogram-(HG):
A histogram is a graphical representation of data as a frequency distribution. This tool
is valuable in evaluating both attribute (pass/fail) and variable (measurement) data.
Histograms offer a quick look at the data at a single point in time; they do not display
variance or trends over time. A histogram displays how the cumulative data looks
today. It is useful in understanding the relative frequencies (percentages) or frequency
(numbers) of the data and how that data are distributed. Figure 38.8 illustrates a
histogram of the frequency of defects in a manufacturing process.
Figure 38.8: Histogram for Variables
38.1.4 Pareto Analysis (PA):
A Pareto diagram is a special type of histogram that helps us to identify and prioritize
problem areas. The construction of a Pareto diagram may involve data collected from
data figures, maintenance data, repair data, parts scrap rates, or other sources. By
identifying types of nonconformity from any of these data sources, the Pareto diagram
directs attention to the most frequently occurring element.
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There are three uses and types of Pareto analysis:
1. The basic Pareto analysis identifies the vital few contributors that account for most
quality problems in any system.
2. The comparative Pareto analysis focuses on any number of program options or
actions.
3. The weighted Pareto analysis gives a measure of significance to factors that may
not appear significant at first-- such additional factors as cost, time, and criticality.
The basic Pareto analysis chart provides an evaluation of the most frequent occurrences
for any given data set. By applying the Pareto analysis steps to the material receipt and
inspection process described in Figure 38.9, we can produce the basic Pareto analysis
demonstrated in Figure 38.10. This basic Pareto analysis quantifies and graphs the
frequency of occurrence for material receipt and inspection and further identifies the
most significant, based on frequency.
Figure 38.9: Material Receipt and Inspection Frequency of Failures
Figure 38.10: Basic Pareto Analysis
A review of this basic Pareto analysis for frequency of occurrences indicates that
supplier A is experiencing the most rejections with 37 percent of all the failures.
Pareto analysis diagrams are also used to determine the effect of corrective action, or to
analyze the difference between two or more processes and methods. Figure 38.11
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displays the use of this Pareto method to assess the difference in defects after corrective
action.
Figure 38.11: Comparative Pareto Analysis
38.1.5 Scatter Diagrams:
Another pictorial representation of process control data is the scatter plot or scatter
diagram. A scatter diagram organizes data using two variables: an independent variable
and a dependent variable. These data are then recorded on a simple graph with X and Y
coordinates showing the relationship between the variables. Figure 38.12 displays the
relationship between two of the data elements from solder qualification test scores. The
independent variable, experience in months, is listed on the X-axis. The dependent
variable is the score, which is recorded on the Y-axis.
Figure 38.12: Solder Certification Test Score
These relationships fall into several categories, as shown in Figure 38.13 below. In the
first scatter plot there is no correlation-- the data points are widely scattered with no
apparent pattern.
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Figure 38.13: Scatter Plot Correlation
The second scatter plot shows a curvilinear correlation demonstrated by the U shape
of the graph. The third scatter plot has a negative correlation, as indicated by the
downward slope. The final scatter plot has a positive correlation with an upward
slope.
From Figure 38.12 we can see that the scatter plot for solder certification testing is
somewhat curvilinear. The least and the most experienced employees scored highest,
whereas those with an intermediate level of experience did relatively poorly. The next
tool, trend analysis, will help clarify and quantify these relationships.
38.1.6 Trend Analysis (T/A):
Trend analysis is a statistical method for determining the equation that best fits the
data in a scatter plot. Trend analysis quantifies the relationships of the data,
determines the equation, and measures the fit of the equation to the data. This
method is also known as curve fitting or least squares.
Trend analysis can determine optimal operating conditions by providing an equation
that describes the relationship between the dependent (output) and independent
(input) variables. An example is the data set concerning experience and scores on the
solder certification test (see Figure 38.14).
Figure 38.14: Scatter Plot Solder Quality and Certification Score
The equation of the regression line, or trend line, provides a clear and understandable
measure of the change caused in the output variable by every incremental change of the
input or independent variable. Using this principle, we can predict the effect of changes
in the process.
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One of the most important contributions that can be made by trend analysis is
forecasting. Forecasting enables us to predict what is likely to occur in the future. Based
on the regression line we can forecast what will happen as the independent variable
attain values beyond the existing data.
38.1.7 Process Control Charts (C/C):
The use of control charts focuses on the prevention of defects, rather than their
detection and rejection. In business, government, and industry, economy and
efficiency are always best served by prevention. It costs much more to produce an
unsatisfactory product or service than it does to produce a satisfactory one. There are
many costs associated with producing unsatisfactory goods and services. These costs
are in labor, materials, facilities, and the loss of customers. The cost of producing a
proper product can be reduced significantly by the application of statistical process
control charts.
·
Control Charts and the Normal Distribution:
The construction, use, and interpretation of control charts is based on the normal
statistical distribution as indicated in Figure 38.15. The centerline of the control
chart represents the average or mean of the data ( ). The upper and lower control
limits (UCL and LCL), respectively, represent this mean plus and minus three
standard deviations of the data either the lowercase s or the Greek letter   (sigma)
represents the standard deviation for control charts.
The normal distribution and its relationship to control charts are represented on the
right of the figure. The normal distribution can be described entirely by its mean
and standard deviation. The normal distribution is a bell-shaped curve (sometimes
called the Gaussian distribution) that is symmetrical about the mean, slopes
downward on both sides to infinity, and theoretically has an infinite range. In the
normal distribution 99.73 percent of all measurements lie within and; this is why
the limits on control charts are called three-sigma limits.
Figure 38.15: The Control Chart and Normal Curve
Companies like Motorola have embarked upon a six-sigma limit rather than a three-
sigma limit. The benefit is shown in Table 38.1 below. With a six-sigma limit, only
two defects per billion are allowed. The cost to maintain a six-sigma limit can be
extremely expensive unless the cost can be spread out over, say, 1 billion units
produced
Control chart analysis determines whether the inherent process variability and the
process average are at stable levels, whether one or both are out of statistical control
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(not stable), or whether appropriate action needs to be taken. Another purpose of
using control charts is to distinguish between the inherent, random variability of a
process and the variability attributed to an assignable cause. The sources of random
variability are often referred to as common causes. These are the sources that
cannot be changed readily, without significant restructuring of the process. Special
cause variability, by contrast, is subject to correction within the process under
process control.
Common cause variability or variation: This source of random variation is always
present in any process. It is that part of the variability inherent in the process itself.
The cause of this variation can be corrected only by a management decision to
change the basic process.
Special cause variability or variation: This variation can be controlled at the local
or operational level. Special causes are indicated by a point on the control chart that
is beyond the control limit or by a persistent trend approaching the control limit.
Table 38.1: Attributes of the Normal (Standard) Distribution
To use process control measurement data effectively, it is important to understand
the concept of variation. No two product or process characteristics are exactly alike,
because any process contains many sources of variability. The differences between
products may be large, or they may be almost immeasurably small, but they are
always present. Some sources of variation in the process can cause immediate
differences in the product, such as a change in suppliers or the accuracy of an
individual's work. Other sources of variation, such as tool wear, environmental
changes, or increased administrative control, tend to cause changes in the product
or service only over a longer period of time.
To control and improve a process, we must trace the total variation back to its
sources. Again the sources are common cause and special cause variability.
Common causes are the many sources of variation that always exist within a
process that is in a state of statistical control. Special causes (often called assignable
causes) are any factors causing variation that cannot be adequately explained by any
single distribution of the process output, as would be the case if the process were in
statistical control. Unless all the special causes of variation are identified and
corrected, they will continue to affect the process output in unpredictable ways.
The factors that cause the most variability in the process are the main factors found
on cause-and-effect analysis charts: people, machines, methodology, materials,
measurement, and environment. These causes can either result from special causes
or be common causes inherent in the process.
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The theory of control charts suggests that if the source of variation is from chance
alone, the process will remain within the three-sigma limits. When the process goes
out of control, special causes exist. These need to be investigated and corrective
action must be taken.
·
Control Chart Types:
Just as there are two types of data, continuous and discrete, there are two types of
control charts: variable charts for use with continuous data and attribute charts for
use with discrete data. Each type of control chart can be used with specific types of
data. Table 38.2 provides a brief overview of the types of control charts and their
applications.
Variables charts: Control charts for variables are powerful tools that we can use
when measurements from a process are variable. Examples of variable data are the
diameter of a bearing, electrical output, or the torque on a fastener.
Table 38.2: Types of Control Charts and Application
As shown in Table 38.2,  and R charts are used to measure control processes
whose characteristics are continuous variables such as weight, length, ohms, time,
or volume. The p and NP charts are used to measure and control processes
displaying attribute characteristics in a sample. We use p charts when the number of
failures is expressed as a fraction, or NP charts when the failures are expressed as a
number. The c and u charts are used to measure the number or portion of defects in
a single item. The c control chart is applied when the sample size or area is fixed,
and the u chart when the sample size or area is not fixed.
Attribute charts: Although control charts are most often thought of in terms of
variables, there are also versions for attributes. Attribute data have only two values
(conforming/nonconforming, pass/fail, go/no-go, present/absent), but they can still
be counted, recorded, and analyzed. Some examples are: the presence of a required
label, the installation of all required fasteners, the presence of solder drips, or the
continuity of an electrical circuit. We also use attribute charts for characteristics
that are measurable, if the results are recorded in a simple yes/no fashion, such as
the conformance of a shaft diameter when measured on a go/no-go gauge, or the
acceptability of threshold margins to a visual or gauge check.
It is possible to use control charts for operations in which attributes are the basis for
inspection, in a manner similar to that for variables but with certain differences. If
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we deal with the fraction rejected out of a sample, the type of control chart used is
called a p chart. If we deal with the actual number rejected, the control chart is
called an NP chart. If articles can have more than one nonconformity, and all are
counted for subgroups of fixed size, the control chart is called a c chart. Finally, if
the number of nonconformities per unit is the quantity of interest, the control chart
is called a u chart.
The power of control charts (Shewhart techniques) lies in their ability to determine
if the cause of variation is a special cause that can be affected at the process level,
or a common cause that requires a change at the management level. The
information from the control chart can then be used to direct the efforts of
engineers, technicians, and managers to achieve preventive or corrective action.
The use of statistical control charts is aimed at studying specific ongoing processes
in order to keep them in satisfactory control. By contrast, downstream inspection
aims to identify defects. In other words, control charts focus on prevention of
defects rather than detection and rejection. It seems reasonable, and it has been
confirmed in practice, that economy and efficiency are better served by prevention
rather than detection.
·
Control Chart Components:
All control charts have certain features in common (Figure 38.16). Each control
chart has a centerline, statistical control limits, and the calculated attribute or
control data. Additionally, some control charts contain specification limits.
The centerline is a solid (unbroken) line that represents the mean or arithmetic
average of the measurements or counts. This line is also referred to as the X bar line
( ). There are two statistical control limits: the upper control limit for values greater
than the mean and the lower control limit for values less than the mean.
Figure 38.16: Control Chart Elements
Specification limits are used when specific parametric requirements exist for a
process, product, or operation. These limits usually apply to the data and are the
pass/fail criteria for the operation. They differ from statistical control limits in that
they are prescribed for a process, rather than resulting from the measurement of the
process.
The data element of control charts varies somewhat among variable and attribute
control charts. We will discuss specific examples as a part of the discussion on
individual control charts.
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·
Control Chart Interpretation:
There are many possibilities for interpreting various kinds of patterns and shifts on
control charts. If properly interpreted, a control chart can tell us much more than
simply whether the process is in or out of control. Experience and training can lead
to much greater skill in extracting clues regarding process behavior, such as that
shown in Figure 38.17. Statistical guidance is invaluable, but an intimate
knowledge of the process being studied is vital in bringing about improvements.
A control chart can tell us when to look for trouble, but it cannot by itself tell us
where to look, or what cause will be found. Actually, in many cases, one of the
greatest benefits from a control chart is that it tells when to leave a process alone.
Sometimes the variability is increased unnecessarily when an operator keeps trying
to make small corrections, rather than letting the natural range of variability
stabilize. The following paragraphs describe some of the ways the underlying
distribution patterns can behave or misbehave.
Figure 38.17: Control Chart Interpretation
Runs: When several successive points line up on one side of the central line, this
pattern is called a run. The number of points in that run is called the length of the
run. As a rule of thumb, if the run has a length of seven points, there is an
abnormality in the process. Figure 38.18 demonstrates an example of a run.
Figure 38.18: Process Run
Trends: If there is a continued rise of all in a series of points, this pattern is called a
trend. In general, if seven consecutive points continue to rise or fall, there is an
abnormality. Often, the points go beyond one of the control limits before reaching
seven. Figure 38.19 demonstrates an example of trends.
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Figure 38.19: Control Chart Trends
Periodicity: Points that show the same pattern of change (rise or fall) over
equal intervals denote periodicity. Figure 38.20 demonstrates an example of
periodicity.
Figure 38.20: Control Chart Periodicity
Hugging the centerline or control limit. Points on the control chart that are close to
the central line or to the control limit are said to hug the line. Often, in this
situation, different types of data or data from different factors have been mixed into
the subgroup. In such cases it is necessary to change the sub-grouping, reassemble
the data, and redraw the control chart. To decide whether there is hugging of the
center line, draw two lines on the control chart, one between the centerline and the
UCL and the other between the center line and the LCL. If most of the points are
between these two lines, there is an abnormality. To see whether there is hugging of
one of the control limits; draw line two-thirds of the distance between the center
line and each of the control lines. There is abnormality if 2 out of 3 points, 3 out of
7 points, or 4 out of 10 points lie within the outer one-third zone. The abnormalities
should be evaluated for their cause(s) ad the corrective action taken. Figure 38.21
demonstrates data hugging the LCL.
Figure 38.21: Hugging the Centerline
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Out of control: An abnormality exists when data points exceed either the
upper or lower control limits. Figure 38.22 illustrates this occurrence.
Figure 38.22: Control Chart Out of Control
In control: No obvious abnormalities appear in the control chart. Figure 38.23
demonstrates this desirable process state.
Figure 38.23: Process in Control
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Table of Contents:
  1. INTRODUCTION TO PROJECT MANAGEMENT:Broad Contents, Functions of Management
  2. CONCEPTS, DEFINITIONS AND NATURE OF PROJECTS:Why Projects are initiated?, Project Participants
  3. CONCEPTS OF PROJECT MANAGEMENT:THE PROJECT MANAGEMENT SYSTEM, Managerial Skills
  4. PROJECT MANAGEMENT METHODOLOGIES AND ORGANIZATIONAL STRUCTURES:Systems, Programs, and Projects
  5. PROJECT LIFE CYCLES:Conceptual Phase, Implementation Phase, Engineering Project
  6. THE PROJECT MANAGER:Team Building Skills, Conflict Resolution Skills, Organizing
  7. THE PROJECT MANAGER (CONTD.):Project Champions, Project Authority Breakdown
  8. PROJECT CONCEPTION AND PROJECT FEASIBILITY:Feasibility Analysis
  9. PROJECT FEASIBILITY (CONTD.):Scope of Feasibility Analysis, Project Impacts
  10. PROJECT FEASIBILITY (CONTD.):Operations and Production, Sales and Marketing
  11. PROJECT SELECTION:Modeling, The Operating Necessity, The Competitive Necessity
  12. PROJECT SELECTION (CONTD.):Payback Period, Internal Rate of Return (IRR)
  13. PROJECT PROPOSAL:Preparation for Future Proposal, Proposal Effort
  14. PROJECT PROPOSAL (CONTD.):Background on the Opportunity, Costs, Resources Required
  15. PROJECT PLANNING:Planning of Execution, Operations, Installation and Use
  16. PROJECT PLANNING (CONTD.):Outside Clients, Quality Control Planning
  17. PROJECT PLANNING (CONTD.):Elements of a Project Plan, Potential Problems
  18. PROJECT PLANNING (CONTD.):Sorting Out Project, Project Mission, Categories of Planning
  19. PROJECT PLANNING (CONTD.):Identifying Strategic Project Variables, Competitive Resources
  20. PROJECT PLANNING (CONTD.):Responsibilities of Key Players, Line manager will define
  21. PROJECT PLANNING (CONTD.):The Statement of Work (Sow)
  22. WORK BREAKDOWN STRUCTURE:Characteristics of Work Package
  23. WORK BREAKDOWN STRUCTURE:Why Do Plans Fail?
  24. SCHEDULES AND CHARTS:Master Production Scheduling, Program Plan
  25. TOTAL PROJECT PLANNING:Management Control, Project Fast-Tracking
  26. PROJECT SCOPE MANAGEMENT:Why is Scope Important?, Scope Management Plan
  27. PROJECT SCOPE MANAGEMENT:Project Scope Definition, Scope Change Control
  28. NETWORK SCHEDULING TECHNIQUES:Historical Evolution of Networks, Dummy Activities
  29. NETWORK SCHEDULING TECHNIQUES:Slack Time Calculation, Network Re-planning
  30. NETWORK SCHEDULING TECHNIQUES:Total PERT/CPM Planning, PERT/CPM Problem Areas
  31. PRICING AND ESTIMATION:GLOBAL PRICING STRATEGIES, TYPES OF ESTIMATES
  32. PRICING AND ESTIMATION (CONTD.):LABOR DISTRIBUTIONS, OVERHEAD RATES
  33. PRICING AND ESTIMATION (CONTD.):MATERIALS/SUPPORT COSTS, PRICING OUT THE WORK
  34. QUALITY IN PROJECT MANAGEMENT:Value-Based Perspective, Customer-Driven Quality
  35. QUALITY IN PROJECT MANAGEMENT (CONTD.):Total Quality Management
  36. PRINCIPLES OF TOTAL QUALITY:EMPOWERMENT, COST OF QUALITY
  37. CUSTOMER FOCUSED PROJECT MANAGEMENT:Threshold Attributes
  38. QUALITY IMPROVEMENT TOOLS:Data Tables, Identify the problem, Random method
  39. PROJECT EFFECTIVENESS THROUGH ENHANCED PRODUCTIVITY:Messages of Productivity, Productivity Improvement
  40. COST MANAGEMENT AND CONTROL IN PROJECTS:Project benefits, Understanding Control
  41. COST MANAGEMENT AND CONTROL IN PROJECTS:Variance, Depreciation
  42. PROJECT MANAGEMENT THROUGH LEADERSHIP:The Tasks of Leadership, The Job of a Leader
  43. COMMUNICATION IN THE PROJECT MANAGEMENT:Cost of Correspondence, CHANNEL
  44. PROJECT RISK MANAGEMENT:Components of Risk, Categories of Risk, Risk Planning
  45. PROJECT PROCUREMENT, CONTRACT MANAGEMENT, AND ETHICS IN PROJECT MANAGEMENT:Procurement Cycles