1. Project quality management processes
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I. Contents of project quality management processes
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In a business, managing workflow is of utmost importance. You don’t need to read the
PMBOK to know that organization and quality sit high on the priority list. For those of you
unfamiliar with the PMBOK, it’s the religious text for management and stands for A Guide to
the Project Management Body of Knowledge. This textbook acts as a guide for everything that
falls within the reign of management. It teaches the five process groups of management:
planning, monitoring, initiating, executing and closing. It also discusses the nine knowledge
areas of project management: scope, time, cost, human resource, communications, risk,
procurement, stakeholders and last but not least – quality. It’s this last knowledge area that we
will focus on in the scope of this article.
Project Management Essentials is a course that explores every aspect of cost, time and
quality of a project. This course primarily focuses on the construction industry, however, the
principles of quality are the same across the board.
So what is quality?
Simply put, and as defined by the International Organization for Standardization
(ISO), quality is the ability of a company to entirely satisfy implied and stated needs. Okay, so
2. what does that mean? The long and short of it means that whatever the company is doing it
needs to do it to 100% and should meet all written requirements and specifications while
ensuring the products and services function as intended. Let’s say you run an ice rink and you
rent out skates for customers to use. As an employee, your job is to sharpen the skates, all of
them, once a week to double check that the skates are still in one piece and that the blades are
sharp. This is a quality assurance measure taken to avoid a lapse in product quality for your
customers.
To adhere to the ISO definition of quality businesses instill project quality management
processes to follow. These processes are quality planning, quality assurance, and quality
control. We’ll get into these in a minute. Project quality management uses the five
management process groups, planning, initiating, monitoring, executing and closing – as
defined by PMBOK – to see that theseproject quality management processes are used and
upheld. In other words, project quality managers devise plans to assure quality and control.
We’ve all heard of big business uh-oh’s like Toyota’s 9 million vehicle recall in 2009 – which
was huge. (There were problems with the removable floor mats that were causing accelerators
to get stuck which resulted in 52 fatal crashes!) The recall occurred because quality assurance
and quality control measures were not executed properly. This not only causes Toyota to lose
money but they damaged their reputation and were held responsible for 52 incident related
deaths.
So if you are wondering why quality management is important – there’s your answer! Learn
more about Quality Management Techniques in this course.
Quality Planning
Quality planning involves much of what the title implies. It’s the steps taken to plan out how to
satisfy quality requirements and specifications as dictated by the company. Without a business
plan you likely couldn’t start a business, right? The same thing goes for the quality plan.
Without it, you have no guidelines on how to achieve those standards. The quality planning
process should be thorough and include a design that soundly communicates how to meet the
needs of the customer. After all, the customer is really who we are trying to serve. Trust me
when I say – quality matters. For example, you’ve got the option between buying a Brookstone
3. massage chair with a lot of unique features for $500. That same chair is offered by a lesser-
known foreign company that is notorious for low quality products – for half the price. Most of
us are going to choose the Brookstone chair even though it’s more expensive, because the
company name is reputable, the quality is high and we all know the saying “you get what you
pay for”. We rather make the investment now than potentially end up with a broken chair and
be out $250. Proper quality planning allows your company to be at the forefront of your
industry. However, creating a plan is only 33% of the process. Let’s see what quality assurance
is about.
Quality Assurance
Alright, so it’s clear that quality planning is a crucial step toward providing top-notch quality
products and services. But words on paper is only a part of it. Quality assurance entails
assessing project performance to make sure what’s being done is meeting the relevant quality
standards. In addition to evaluating these standards, quality assurance managers are responsible
for on-going quality improvement. They do this by performing quality audits and
benchmarking to understand where quality may be lacking and what can be done to correct it.
We can refer back to the Toyota recall for this one. Someone dropped the ball on quality
assurance… big time. Whether management failed to oversee the projects were meeting quality
requirements, or the employees neglected to fulfill their obligation to meet quality standards, I
don’t know. What we do know is that this major oversight caused Toyota a lot of loyal
customers and millions of dollars. Inan Introduction to Quality Management course learn
more about implementing a quality assurance program.
Quality assurance management isn’t just telling your employees what to do. It involves
drafting a quality assurance plan that highlights the purpose, scope, organizational structure,
team leaders and the procedures that are to be followed. The quality assurance team is
responsible for walking through these procedures and doing quality assurance tests to verify
that the techniques in place are actually feasible and beneficial.
Quality Control
At the tail end of Project Quality Management we see quality control. It’s this process that
ensures all of the quality planning and quality assurance measures taken are compliant with the
4. overall quality goals of the company. With quality control, management and workers could
mosey on through their days producing terrible quality products unbeknownst to them. Quality
control management steps in to say, “hey, these [fill-in-the-blank] are not up to our company
standards. Where did we go wrong and what can we do to improve it?”. Quality control
management has a toolbox of techniques to employ when things aren’t looking so hot. They
whip out the quality control charts, do a Pareto analysis and utilize Six Sigma. Let’s go over
these one by one.
Quality Control Charts
As you may have guessed, quality control charts are, well, charts. They are visual
representations of the company quality output over the course of several periods. These charts
illustrate whether or not a quality process is in control. If it is in control, the charts should
convey a steady or upward trend in product quality results. If the process is not in control,
defects will be identified. To analyze if a process is in control or not quality control managers
use the seven run rule. Basically, if seven data points on the chart fall above or below the mean
– there is an issue. The seven points indicate that there are non-random factors at play here and
consistency is lacking. All implied processes should be reassessed for assurance.
Pareto Analysis
Quality control managers love graphs and charts. The Pareto Analysis uses Pareto diagrams, or
histograms, to identify the few contributing factors that are causing most of the quality issues
in the company or system. It’s also called the 80-20 rule because 80% of the quality problems
are frequently due to 20% of the causes. Using the Pareto Analysis, quality control managers
can see what functions of the system could be at fault and then they can prioritize them for
correction.
Six Sigma
The Six Sigma, in essence, encompasses everything we’ve talked about with a focus on an
understanding of customer needs. Through using the Six Sigma system, quality control teams
can strive to sustain and maximize business success by utilizing statistics, attention to detail
and discipline to execute the planning, managing, executing and initiating business processes.
There is a lot of number-fiddling in this quality control technique so I’ll save most of the
5. details for another time. However, the Six Sigma target is to achieve no more than 3.4 defects
per one million opportunities. In other words, if you’re Toyota – you messed up, big time.
Within Six Sigma there is yet another set of processes to ensure that the company does not go
over the meager 3.4 defects per million. This set is called the DMAIC for, define, measure,
analyze, improve and control. You can guess what each of these entails. If you can’t, read Six
Sigma Tools and the details will be unveiled there. In order for Six Sigma to work the
company, as a whole, has to commit to it. This means every department, in every branch, under
every kind of manager has to uphold the DMAIC and 3.4 rule. A lot of big name companies
like Motorola, Honeywell and GE, use Six Sigma and rather effectively.
Total Quality Management (TQM)
Okay, so you know all about the umpteen sets of processes that go into project quality
management. You know that Six Sigma is a company-wide commitment and that brings us to
total quality management (TQM). Within a company there are often several departments, or
branches that have different functions. The goal of the TQM approach is to fully integrate all
of the quality management processes we just went over to ensure company-wide success and
to beat customer expectations. TQM runs from top to bottom assessing every assessment that
every assessor in the company has done. At the end, there is a really well-painted picture of the
overall quality the company is producing. Executive Management should be the driving force
behind TQM, and believe you me they will be with the Malcom Baldridge Award at stake.
(Malcom Baldridge Award is a Quality award that recognizes companies with world-class
quality.) Risk management is also a part of quality management. Risk managers identify and
analyze the risk of any faction of a business. Risk managers and quality managers often work
closely in understanding quality processes. Learn more in the course Risk Management.
In the end we see the quality matters. It matters for the business and it matters for the
customers. It’s a two-way dialogue between the customer needs and feedback and the
company’s ability to meet those needs and address that feedback. Manage superior quality
standards for your company; learn from the best in the courseManage Project Quality.
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6. 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)
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
7. 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.
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.
8. 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
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
9. 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
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
10. Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
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]
III. Other topics related to Project quality management processes (pdf
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