1. Quality metrics in project management
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I. Contents of quality metrics in project management
“If you don’t measure something, you can’t change it. The process of leadership is one of
painting a vision, then saying how you’re going to get there, and then measuring whether you’re
actually getting there. Otherwise, you risk only talking about great things but not accomplishing
them.” Mitt Romney
Continual improvement is a prerequisite for any organization’s success. A continual
improvement process, also often called a continuous improvement process (abbreviated
as CIP or CI), is an ongoing effort to improve products, services, or processes. These efforts can
seek “incremental” improvement over time or “breakthrough” improvement all at once. Delivery
(customer valued) processes are constantly evaluated and improved in the light of their
efficiency, effectiveness and flexibility (Wiki).
Gauging whether there is incremental improvement and setting up mechanisms to track and
measure these improvements is the difficult part and this is where Metrics come in. I am
passionate about metrics and have written about my favourite performance management metrics
in business,sales and human resourcesearlier. In this guest post, Kavita Verma draws upon her
2. PMO experiences to list the most effective metrics that can be used by project managers to
determine the success of their projects.
‘Metric’ is defined as “Standard of measurement by which efficiency, progress, performance,
productivity, quality of a deliverable, process, project or product can be assessed”. Metrics help
in building predictability, improving organization’s decision making ability, and lay out what is
working and what is not working within the organization and help guide the management focus
in the right directions.
Project management performance metrics enable Project managers to:
Assess status of ongoing project in terms of schedule, cost and profitability.
Foresee any potential risks.
Nail down the problems much before they become severe.
Keep a check on project profitability.
Assess productivity of team.
Assess quality of work products to be delivered.
There can be different project management metrics defined based on complexity and nature of
project. However, following five performance metric groups cover all the important aspects of a
project to measure during execution:
Performance Metric #1: Schedule and Effort/Cost Variance
The goal of this metric is to measure the performance as well as progress of the project against
signed baselines. This metric is very important and is the base for profitability of project. The
EVM (Earned Value Management) concept, as defined by PMI standard PMBOK, is the
commonly used method to track this metric. It integrates project scope, cost and schedule
measures to help the PM to assess and measure project performance and progress. The principles
of EVM can be applied to all projects, in any industry. Under this method, at any given point in
time, project performance to date is used to extrapolate the expected costs and duration at project
completion. This technique uses past performance (i.e. actuals) to more accurately forecast future
performance. EVM develops and monitors three key dimensions of each work package:
3. Planned Value (PV): How much you planned to spend for the work you planned to do i.e. it is
the authorized budget assigned to the work to be accomplished for an activity or work
breakdown structure component. Total PV is also known as Budget at Completion (BAC). PV at
any stage = (Planned % Complete) X (BAC)
Earned Value (EV): Earned value is the value of work performed expressed in terms of the
approved budget assigned to that work for an activity or work breakdown structure component. It
is the authorized work that has been completed, against the authorized budget for such completed
work i.e. EV is ‘how much you planned to spend for the work you actually did’. Earned Value is
also known as the Budgeted Cost of Work Performed (BCWP).
Actual cost (AC): Actual cost is the total cost actually incurred and recorded in accomplishing
work performed for an activity or work breakdown structure component. It is the total cost
incurred in accomplishing the work that the EV measured. I.e. how much you spent for the work
you actually did. Actual Cost is also known as the Actual Cost of Work Performed (ACWP).
Using these three variables project Schedule variance and Cost variance metrics can be derived
which shows if the project is running over or under budget; project is running behind or ahead of
schedule, as follows:
Schedule Variance (SV) is the measure of schedule performance of the project. It is the
difference of Earned value and the planned value i.e. SV = EV – PV
Positive result means that you are ahead of schedule.
Negative result means that you are behind schedule.
Cost Variance (CV) is the measure of cost performance on the project. It is equal to earned value
(EV) minus actual costs (AC). Any negative CV is often non-recoverable to the project.
CV = EV – AC
Positive result means that you are under budget.
Negative result means that you are over budget.
4. Since EVM method allows PM to extrapolate the expected costs and duration at project
completion based on project performance to date, PM can develop a forecast for the estimate at
completion (EAC) which may differ from the budget at completion (BAC) based on project
performance. Forecasting of EAC involves making estimates or prediction of conditions and
events in the project’s future based on information and knowledge available at the time of
forecasting. EAC is typically based on actual cost (AC) incurred for work completed, plus an
estimate to complete (ETC) the remaining work. I.e. EAC = AC + ETC.
Based on this PM can also derive another metric, Variance at completion (VAC) = BAC – EAC
Performance Metric #2 – Productivity: Resource Utilization
The objective of this metric is to measure productivity of resources involved in project and let
PM assess over or under-utilization cases.
Utilization% = Total Effort spent by resource/Total Budgeted Effort for the resource
Budgeted effort is the planned billable work of resource. Any over-utilization and under-
utilization indicated by this metric has an impact on the project’s profitability. It is important for
the PM to track this metric very closely and find out the reason for deviations and the action
items to bring back resource utilization to optimal level. Delayed projects, increased ramp up
activities, less work provided by customer, unplanned vacations, less competent resources can
impact this metric. To get better control over this metric, robust time reporting systems should be
available in the organization. Using this, PM can analyze effort distribution across different
5. project phases/activities. For e.g. Effort distribution can tell PM that how much effort is being
spent on defect resolution, customer support or design activities. PM can take corrective actions
based on this, if required. For instance, if the resource is complaining that customer support is
taking considerable time but the effort distribution shows it otherwise, PM can see where the
corrections are needed on what resource is doing. Effort distribution from time reporting systems
can also tell the areas of improvement for better estimations/planning for the next project.
Performance Metric #3: Change requests to Scope of work
Signed Scope baseline with customer forms the baseline for the entire project planning and
development. Any change to signed scope should happen in controlled manner. So here comes
another important metric for PM to track i.e. the number of change requests coming from
customer for the already signed scope of work. Each and every change request, once approved
by internal change control board (CCB), requires update to Scope baseline which in turn has a
cascade impact on cost baselines and schedule baselines and resource plans. Uncontrolled
change requests often result in project scope creep and further impact negatively on the project
cost/schedule, which is the worst thing to happen for any project. PM should never allow such
scope creep. Based on the magnitude of the variance from original scope baseline, CCB should
decide whether to accept or reject the change request and this decision should be communicated
back to customer. In case of acceptance of change request, the impact on project cost and
schedule should be clearly communicated in written form to customer and a written agreement
from customer secured on those from customer before proceeding.
Performance Metric #4: Quality and Customer Satisfaction
6. Throughout the execution of project, Quality Assurance should always be on the radar of project
manager. Quality here is defined as the number of severe, medium or low defects delivered
through the lifetime of the project. It indicates the health of the deliverable to the end user and
drives the Customer Satisfaction. PM needs to define, based on project type, what severe, low
and medium means. Quality should be reported throughout the life of the project; the later
defects are caught, the more impact they will have on the project. Under quality metrics,
following are the key ones to track:
Defect density = Total number of defects found/ Measure of size.
For e.g. in case of software projects this can be: how many defects are found in 1KLOC (Kilo
line of code). In general, size measure can be considered as planned effort like ‘person day total
Number of days since the defect is open and not fixed. It can also be inferred as the time
customer has been waiting for their issues to get resolved,
Defect resolution rate = Total number of defects resolved/ Total effort spent
Rate of closing the open defects over a period of time. If the rate of resolution is not in line with
the defects being opened over a particular time, this indicates to the PM a situation of concern.
Number of defects reported by customer
PM should keep this as a separate metric to differentiate from the defects reported out of internal
testing and the defects reported by end user i.e. Customer. Customer satisfaction depends a lot on
the quality of deliverable provided and on how fast defects raised by customer are resolved.
7. As said above, the later defects are caught, the more impact they will have on the project, it is
worth to mention here about Pareto’s principle i.e. 80/20 principle, which PM can use to
categorize causes of defects and late time entry relationship. As per this law 80% of the problems
are due to 20% of the causes. PM can concentrate on these 20% causes impacting the project
Performance Metric #5: Gross Margin
Gross Margin (as I wrote in my earlier post on key performance metrics) is the mother of all
metrics and the quickest way to determine if your business in on track or not and acts as an early
warning system to put in place margin improvement initiatives. Ultimate goal of project
execution is to bring revenue to organization with the approved gross margin. Gross margin
(GM) is basically the difference of total revenue and the total cost spent on project i.e. profit.
When a project is started, certain GM levels for the project are approved by project sponsor. This
approved GM value is generally based on project scope definition, duration, a forecast of
resources: onsite, offshore and organization’s investment analysis. Project PNL (Profit and Loss)
statement gives a way to PM for tracking his/her projects GM metric at any point of time. For
this, PNL statements and forecasts should be current documents i.e. changes in project
parameters need to be reflected quickly in this statement to keep the PM informed about any
potential risks to project profitability. All the above four project management performance
metrics impact this metric, if not handled in controlled manner. A good organizational level PNL
tool rather than manual excel sheets reduces the overhead on PM here.
While working with all of these metrics, following points should be very clear in the project
8. What to measure
How to measure
How to gather data to measure and represent
How to analyze gathered data and what actions to be defined based on that
How to show improved effectiveness of metrics by using improved numbers ‘before’ and ‘after’
In my view these five project management performance metrics are critical metrics to be tracked
while taking charge and during execution of a project. The continued analysis of these metrics
provides additional insights into what is working and what is not, allowing the PM to make
appropriate improvements. These metrics also help in building up historical data for similar kind
of projects so that in future, better project planning can be done. Success story of project can be
then built up based on the effectiveness of defined metrics by showing the improved numbers
“before” and “after”. This ensures that the effort spent by team in collection and measurement of
data for these metrics is leading to continuous improvements and not just an overhead activity.
In summary, metrics improve decision making ability by providing the foundation and rationale
for the decision by making explicit what is usually implicit in the decision-making process.
So what are your experiences when it comes to project management performance metrics and
tracking? What are some of the other project management metrics you have been tracking? We
would love to hear and learn from you.
III. Quality management tools
1. Check sheet
9. 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
Check sheets typically employ a heading that answers the
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,
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
10. result in degraded process performance. 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
The control chart is one of the seven basic tools of
quality control. 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
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
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
11. 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. This kind
of plot is also called a scatter chart, scattergram, scatter
diagram, 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
12. 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
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. 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
6. Histogram method
13. 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. 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. The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.
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