1. Quality management organizations
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I. Contents of quality management organizations
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1. Introduction
Companies which do not implement Total Quality Management in their firms will not be
competitive in the national and international market within the next 5–10 years! This startling
conclusion is based on research that involved interviews with 142 individuals from 19 owner and
contractor firms involved in heavy industrial, manufacturing, and commercial construction.
Total Quality Management (TQM) is a complete management philosophy that permeates every
aspect of a company and places quality as a strategic issue. It is accomplished through an
integrated effort between all levels of a company to increase customer satisfaction by
continuously improving current performance.
Research Objectives
Identify attributes of quality management organizations and techniques that have been
considered to be effective in the construction industry.
Identify the reasons for the effectiveness of the attributes and how they were developed and
implemented.

2. Recommend generic guidelines for implementing improved quality management in the
construction industry.
Conclusions
An integrated approach of Total Quality Management (TQM) and quality assurance/quality
control (QA/QC) is required to provide quality products and services in the construction
industry. Early indications reveal substantial improvements in achieving quality requirements
among organizations that have implemented TQM.
Construction companies have, with minor modifications, adopted the methods and concepts of
TQM that are being used in U.S. manufacturing and have applied them to their operations.
The development and deployment of a TQM approach must be tailored to the specific needs of
an organization. A program can not simply be adopted from a consultant and deployed.
There must be action behind the words and ceremony, and this can only be accomplished by
senior management understanding and involvement. Management must participate in the
implementation process and be fully committed to it if TQM is to succeed.
The use of small, well-placed, pilot projects is an effective method for gaining acceptance of
TQM among the employees and management of a company.
The TQM process takes about three years before it is accepted throughout a company and
significant results are achieved.
Training for TQM will not succeed unless both the technical and humanistic aspects are
addressed. The more technical the processes, the more the emphasis in training should be placed
on interpersonal and communication skills.
The topics and examples used in the training effort should be integrated with the actual work
processes of the individuals being trained. The employees should apply newly learned skills to
their jobs as quickly as possible.
Statistical methods are being effectively applied to engineering and construction processes and
are being used to identify and solve problems and to improve processes. For tracking to be
utilized effectively, employees and management must first understand the fundamental concepts
of TQM and the purpose for controlling and constantly improving their processes.
Owners and contractors are seeking improved relationships with each other and with vendors and
subcontractors. Partnership agreements are being formed between owners and contractors. Both
owners and contractors are seeking to reduce their numbers of qualified vendors, but the majority
feel that formal partnerships with vendors are not possible at this time.
Recommendations

3. Implement an integrated approach of TQM and QA/QC.
A TOM approach should be adapted to the specific needs of a company and not simply adopted
from a consultant.
Thoroughly investigate the different approaches to TQM and select the most applicable aspects
of each one for developing a corporate approach.
Management must fully understand and support the TQM process and actively participate in its
implementation rather than delegate it.
Use small, well-placed, pilot projects in the early stages of the deployment phase to obtain the
acceptance of TQM among the employees and management of the company.
Both the technical and humanistic aspects of TQM must be addressed by a training effort.
Training should be tailored to the job function of the employees.
The numbers of qualified vendors should be reduced to assist in the development of closer and
more productive relationships.
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)

4.  Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.

5. 3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method

6. A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line

7. exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

8. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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