This document discusses quality risk management tools. It provides definitions and background on risk analysis and management tools. Key criteria for selecting the right tool are that it must be aligned with risk analysis objectives, support decision making, be accessible to users, have available data, and integrate with other processes. The document describes MITRE-developed tools like RiskNav that facilitate risk management. It also discusses common quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Other related quality topics are also listed.

1. Quality risk management tools
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I. Contents of quality risk management tools
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Definition: Risk management tools support the implementation and execution of program risk
management in systems engineering programs.
Keywords: risk analysis tools, risk management tools, risk tools
MITRE SE Roles & Expectations: MITRE systems engineers (SEs) working on government
programs are expected to use risk analysis and management tools to support risk management
efforts. MITRE systems engineers also are expected to understand the purpose, outputs,
strengths, and limitations of the risk tool being used.
Background
Risk analysis and management tools serve multiple purposes and come in many shapes and sizes.
Some risk analysis and management tools include those used for:
 Strategic and Capability Risk Analysis——Focuses on identifying, analyzing, and
prioritizing risks to achieve strategic goals, objectives, and capabilities.
 Threat Analysis——Focuses on identifying, analyzing, and prioritizing threats to
minimize their impact on national security.
 Investment and Portfolio Risk Analysis——Focuses on identifying, analyzing, and
prioritizing investments and possible alternatives based on risk.
 Program Risk Management——Focuses on identifying, analyzing, prioritizing, and
managing risks to eliminate or minimize their impact on a program's objectives and
probability of success.

2.  Cost Risk Analysis—Focuses on quantifying how technological and economic risks may
affect a system's cost. Applies probability methods to model, measure, and manage risk
in the cost of engineering advanced systems.
Each specialized risk analysis and management area has developed tools to support its objectives
with various levels of maturity. This article focuses on tools that support the implementation and
execution of program risk management.
Selecting the Right Tool
It is important that the organization defines the risk analysis and management process before
selecting a tool. Ultimately, the tool must support the process. Below are criteria to consider
when selecting a risk analysis and management tool:
 Aligned to risk analysis objectives——Does the tool support the analysis that the
organization is trying to accomplish? Is the organization attempting to implement an
ongoing risk management process or conduct a one-time risk analysis?
 Supports decision making——Does the tool provide the necessary information to support
decision making?
 Accessibility——Is the tool accessible to all users and key stakeholders? Can the tool be
located/hosted where all necessary personnel can access it?
 Availability of data——Is data available for the tool's analysis?
 Level of detail——Is the tool detailed enough to support decision making?
 Integration with other program management/system engineering processes——Does the
tool support integration with other program management/system engineering processes?
Program Risk Management Tools
In program risk management, it is important to select a tool that supports the risk management
process steps outlined in Figure 1. Refer to the other articles in the Risk Management topic area
of this guide for additional information on each of the process steps. Many tools are available
that support the implementation of program risk management. Many tools also can be used to
support the management of project, enterprise, and system-of-systems risks.

3. Figure 1. Risk
Management: Fundamental Steps [1]
MITRE Developed Tools
RiskNav®1
RiskNAV® is a well-tested tool developed by MITRE to facilitate the risk process and help
program managers handle their risk space. RiskNav lets you collect, analyze, prioritize, monitor,
and visualize risk information in a collaborative fashion. This tool provides three dimensions of
information graphically—risk priority, probability, and mitigation/management status.
RiskNav, originally produced for the U.S. government, is designed to capture, analyze, and
display risks at a project or enterprise level. RiskNav is currently deployed throughout numerous
and MITRE sponsors or clients.
Since January 2005, the Technology Transfer Office at MITRE has licensed RiskNav technology
to commercial companies. Current licensees include KEYW Corporation (Risk Mitigation
Tool)and NMR Consulting. The Technology Transfer Office will support the tool for contractor
and other government acquisition, and will ensure that proper licensing forms are obtained and
signed by new users. There is no cost for government usage. This formal procedure is not needed
if MITRE is hosting a risk management effort.
RiskNav presents the risk space in tabular and graphical form. The tabular form, shown below,
presents key information for each risk, and allows the risk space to be filtered and sorted to focus

4. on the most important risks. The information in the tables and figures is artificial and for
illustrative purposes only. It does not represent real programs, past or present.
==================
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

5. 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.
3. Pareto chart

6. 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
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

7. 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
exactly.

8. 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

9. 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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