Chapter 5
Basic Tools
Introduction
• Chapters 2 and 3 discussed histograms, sample mean,
sample standard deviation, attribute/continuous data, and
special/common cause.
• This chapter continues the discussion of basic techniques
and offers a collection of data analysis, data presentation,
and improvement alternatives.
• A wide variety of tools are briefly described for the purpose
of aiding with the efficient building of strategies for
collecting and compiling information that leads to
knowledge. With this knowledge we can make better
decisions.
• Problem identification, defining, and solving.
Introduction
• Descriptive statistics help pull useful information from
data;
• Probability provides, a basis for inferential statistics
and sampling plans.
Introduction
7 management tools (7M tools) or 7 management and
planning tools (7 MP tools).
• affinity diagrams
• interrelationship digraphs
• tree diagrams
• prioritization matrices
• matrix diagrams
• process decision program charts (PDPC)
• activity network diagrams
Introduction
7 quality control tools:
• cause-and-effect diagram
• check sheet
• scatter diagram
• flowchart
• Pareto chart
• histogram
• control chart
Introduction
Tools working with ideas:
• Activity network diagram
• Affinity diagram
• Benchmarking
• Brainstorming
• Cause-and-effect
diagram
• Flowchart
• Force field
• Interrelationship digraph
(ID)
• Matrix diagram nominal
group technique (NGT)
• Prioritization matrices
• Process decision program
chart (PDPC)
• Tree diagram
• Why-why diagram
Introduction
Tools working with numbers:
• Check sheets
• Control chart
• Histogram
• Pareto chart
• Probability plot
• Run chart
• Scatter diagram
5.1 Descriptive Statistics
S4/IEEE Application Examples
• Random sample of last year’s invoices where the number
of days beyond the due date was measured and reported
(i.e., days sales outstanding [DSO])
• Random sample of parts manufactured over the last year,
where the diameters of the parts were measured and
reported
5.1 Descriptive Statistics
• Sample Mean: Arithmetic average of the data values (x1,
x2, x3 ,..., xi), which is mathematically expressed
𝑛
𝑖=1 𝑥𝑖
𝑥=
𝑛
• Sample Median (𝑥_50): the number at (n+1)/2 rank
• TrMean (trimmed mean): Average of the values remaining
after both 5% of the largest and smallest values, rounded
to the nearest integer, are removed.
• Sample standard deviation (SD)
1
𝑛−1
𝑛
( 𝑋𝑖
𝑖=1
− 𝑋 )2
=
1
(
𝑛−1
𝑛
𝑋𝑖 2 −𝑛𝑋 2 )
𝑖=1
5.1 Descriptive Statistics
• SE mean (standard error of mean):
•
•
•
•
𝑆𝐷
𝑛
Minimum: lowest number in data set
Maximum: largest number in data set
Q1: first quartile
Q3: third quartile
5.2 Run Chart (Time Series Plot)
S4/IEE Application Examples
• One random paid invoice was selected each day from last
year’s invoices where the number of days beyond the due
date was measured and reported (i.e., days sales
outstanding [DSO]). The DSO for each sample was
plotted in sequence of occurrence on a run chart.
• One random sample of a manufactured part was selected
each day over the last yean where the diameters of the
parts were measured and reported. The diameter for each
sample was plotted in sequence of occurrence on a run
chart.
5.2 Run Chart (Time Series Plot)
• A run chart permits the study of observed data for trends or
patterns over time.
• Can be used to compare a performance measurement
before and after a solution implementation to measure its
impact.
• 20~25 points are needed to establish pattern and
baselines.
• Problem exists with the interpretation of run chart: all
variation as important (over-reacting)  control charts
5.2 Run Chart (Time Series Plot)
www.pqsystems.com/.../chart_BasicRunChart.png
5.3 Control Chart
S4/IEE Application Examples
• One paid invoice was randomly selected each day from
last year’s invoices where the number of days beyond the
due date was measured and reported (i.e., days sales
outstanding [DSO]). The DSO for each sample was
plotted in sequence of occurrence on a control chart.
• One random sample of a manufactured part was selected
each day over the last year, where the diameters of the
parts were measured and reported. The diameter for each
sample was plotted in sequence of occurrence on a control
chart.
5.3 Control Chart
• Control limits are a function of data variability, not
specification limits.
• It gives not only process monitoring and control, but also
direction for improvements.
• It can separate special cause from common causes
• Early identification of special causes
• 94% of the troubles belong to system (common cause);
only 6% are special cause (Deming 1986)
• Monitoring should be of KPIVs (not only on KPOVs)
• If process is shown to be in-control, issues should be
looked collectively over some period of time  Process
Improvement techniques
5.3 Control Chart
http://support.sas.com/rnd/app/qc/qc/shwclms.gif
5.4 Probability Plot
• Probability plots are most often associated with tests to
assess the validity of normality assumptions.
• When data are a straight line on a normal probability plot,
the data are presumed to be from a normal distribution.
• Probability plots also apply to other distributions, such as
the Weibull distribution.
• Probability plots can also be used to make percentage of
population statements (very useful in describing the
performance of business and other processes)
5.4 Probability Plot
Minitab:
Graph
Probability Plot
• Single
5.5 Check Sheets
• Check sheets contain the systematic recording and
compiling of data from historical or current observations.
• The information can indicate patterns and trends.
• After agreement is reached on the definition of events or
conditions, data are collected over a period of time and
presented in tabular form.
Problem
Week
Total
1
2
3
A
///
////
//
10
B
/
//
//
5
C
////
/
/
6
5.5 Check Sheets
http://www.asq.org/learn-about-quality/datacollection-analysis-tools/overview/check-sheet.html
5.6 Pareto Chart
S4/IEE Application Examples
• Transactional workflow metric (could similarly apply to
manufacturing; e.g., inventory or time to complete a
manufacturing process): Random sample of last year’s
invoices where the number of days beyond the due date
was measured and reported (i.e., days sales outstanding
[DSO] ). If an invoice was beyond 30 days late, it was
considered a failure or defective transaction. A Pareto
chart showed the frequencies of delinquencies by
company invoiced.
5.6 Pareto Chart
S4/IEE Application Examples
• Transactional quality metric: Random sample of last year’s
invoice, where the invoices were examined to determine if
there were any errors when filling out the invoice or within
any other step of the process. Multiple errors or defects
could occur when executing an invoice. The total number
of defects when the invoice was executed was divided by
the total number of opportunities for failure to estimate the
defect per million opportunity (DPMO) rate of the process.
A Pareto chart showed the frequencies of delinquencies by
type of failure.
5.6 Pareto Chart
S4/IEE Application Examples
• Manufacturing quality metric: Random sample of printed circuit
boards over the last year, where the boards were tested for failure.
The number of defective boards was divided by the sample size to
estimate the defective rate of the process. A Pareto chart showed
the frequencies of defective units by printed circuit board type.
• Manufacturing quality metric: Random sample of printed circuit
boards over the last year, where the boards were tested for failure.
Multiple failures could occur on one board. The total number of
defects on the boards was divided by the total number of
opportunities for failure (sum of the number of components and
solder joints from the samples) to estimate the defect per million
opportunity (DPMO) rate of the process. A Pareto chart showed
the frequencies of defects by failure type.
5.6 Pareto Chart
• The Pareto principle basically states that a vital few of the
manufacturing process characteristics cause most of the
quality problems on the line, while a trivial many of the
manufacturing process characteristics cause only a small
portion of the quality problems.
• Pareto chart may need to consider data from different
perspectives. [by machine, or by shift]
5.6 Pareto Chart
Coffee Pareto Chart
1400
100.0%
90.0%
RANK
1200
80.0%
1000
70.0%
800
60.0%
50.0%
600
40.0%
400
30.0%
20.0%
200
0
10.0%
Coffee
Type
Coffee
Amount
Grind
Time
Brew
Time
Water
Temp
Cup Size
Cup
Type
320
280
176
174
144
112
104
Percentage
24.4%
21.4%
13.4%
13.3%
11.0%
8.5%
7.9%
Cumulative %
24.4%
45.8%
59.2%
72.5%
83.5%
92.1%
100.0%
Count
0.0%
5.6 Pareto Chart: Procedure
1. Define the problem and process characteristics to use in the
diagram.
2. Define the period of time for the diagram—for example,
weekly, daily, or shift.
3. Total the number of times each characteristic occurred.
4. Rank the characteristics according to the totals from step 3.
5. Plot the number of occurrences of each characteristic in
descending order in a bar graph form along with a
cumulative plot of the magnitudes from the bars.
6. Trivial columns can be lumped under one column
designation; however, care must be exercised not to forget
a small but important item.
5.7 Benchmarking
•
•
•
Benchmarking involves the search of an organization for the
best practices, adaptation of the practices to its processes,
and improving with the focus of becoming the best in class.
Benchmarking can involve comparisons of products,
processes, methods, and strategies.
Sources of information for benchmarking include the
Internet, in-house published material, professional
associations, universities, advertising, and customer
feedback.
http://totalqualitymanagement.files.wordpress.com
/2008/11/picture24.png
5.7 Types of Benchmarking
•
•
•
•
Internal benchmarking makes comparisons between similar
operations within an organization.
Competitive benchmarking makes comparisons with the
best direct competitor.
Functional benchmarking makes comparisons of similar
process methodologies.
Generic benchmarking makes comparisons of processes
with exemplary and innovative processes of other
companies.
5.8 Brainstorming
•
•
Very valuable means of generating new ideas and involving
a group.
Many ways
• to conduct a brainstorming session
• to compile the information from the session
http://www.howtoplaza.com/wpcontent/uploads/2010/05/productive-brainstorming-session.png
5.8 Brainstorming
Formal process of brainstorming
• Setup:
• Table arranged in a manner to encourage discussion
• Problem or question is written down for everyone to see
• Basic rules:
1. Ask each member in rotation for one idea
2. Rule out all evaluations or critical judgments
3. Encourage wild ideas
4. Encourage good-natured laughter and informality
5. Target for quantity, not quality
6. Look for improvements and combinations of ideas
5.8 Brainstorming
Guideline for the leader:
1. The problem needs to be simply stated.
2. Two or more people should document the ideas in plain sight so
that the participants can see the proposed ideas and build on the
concepts.
3. The name of the participant who suggested the idea should be
placed next to it.
4. Ideas typically start slowly and build speed. Change in speed
often occurs after someone proposes an offbeat idea. This
change typically encourages others to try to surpass it.
5. A single session can produce over 100 ideas, but many will not
be practical.
6. Many innovative ideas can occur after a day or two has passed.
5.8 Brainstorming
• A follow-up session can be used to sort the ideas into
categories and rank them.
• When ranking ideas, members vote on each idea that they
think has value.
• For some idea considerations it is beneficial to have a
discussion of the pros and cons before the vote
• A circle is drawn around the ideas receive the most votes
• Through sorting and ranking, many ideas can be combined
while others are eliminated
5.8 Brainstorming Applications
•
•
•
•
Problem definition
Factors within a DOE
Test strategy
Inputs to the cause-and-effect diagram
5.9 Nominal Group Technique
(NGT)
Nominal group technique expedites team consensus on relative
importance of problems, issues, or solutions.
• An NGT is conducted by displaying a generated list of items,
perhaps from a brainstorming session, on a flipchart or board.
• Eliminating duplications and making clarifications, then creates a
final list.
• The new final list of statements is then prominently displayed,
each item is assigned a letter, A, B ,...
• On a sheet of paper, each person ranks the statements, assigning
the most important a number equal to the number of statements
and the least important the value of one.
• Results from the individual sheets are combined to create a total
overall prioritization number for each statement.
5.9 Nominal Group Technique
(NGT)
http://www.wsa-intl.com/Portals/70018/images/202.jpg
5.10 Force Field Analysis
Force field analysis can be used to analyze what forces in an
organization are supporting and which are restraining progress.
• After an issue or problem is identified, a brainstorming session is
conducted to create a list of driving forces and then a list of
restraining force.
• A prioritization is then conducted of the driving forces that could be
strengthened.
• There is then a prioritization of the restraining forces that could be
reduced to better achieve the desired result.
5.10 Force Field Analysis
Restraining Forces
Action Items
Create reward and recognition programs that reinforce
Programs reinforce the old
the new
People do not have skills sets needed Create training programs so that people have the
to succeed
opportunity to learn new
Introduce new vocabulary so people know you are
Phrases still refer to the old
speaking of the new
Current cultural supports staying the
Change culture so that it supports the new
same
People concerned about making
Reassure them that mistakes are learning
mistakes
opportunities and failure will not be punished
People concerned how changes
Provide information that clarifies how performance will
affect their job
be evaluated
People isolated in the ways they are
Celebrate successes
going about the new
People wonder how serious the
Provide consistent communications that clarify how
company is about this change
serious the company is about completing this change
5.11 Cause-and-Effect Diagram
S4/IEE Application Examples
• An S4/IEE project was created to improve the 30,000 footlevel metric days sales outstanding (DSO). A process
flowchart was created to describe the existing process. A
team created a cause-and-effect diagram in a
brainstorming session to trigger a list of potential causal
inputs and improvement ideas.
• An S4/IEE project was created to improve the 30,000 footlevel metric, the diameter of a manufactured part. A
process flow chart was created to describe the existing
process. A team created a cause-and-effect diagram in a
brainstorming session to trigger a list of potential causal
inputs and improvement ideas.
5.11 Cause-and-Effect Diagram
• also known as an Ishikawa diagram (after its originator
Karoru Ishikawa) or fishbone diagram.
• This technique is useful to trigger ideas and promote a
balanced approach in group brainstorming sessions in
which individuals list the perceived sources (causes) of a
problem (effect).
• A cause-and-effect diagram provides a means for teams to
focus on the creation of a list of process input variables
that could affect key process output variables.
• Addresses strata issues based on key characteristics (e.g.,
who, what, where, and when).
5.11 Cause-and-Effect Diagram
• When constructing a cause-and-effect diagram, it is often
appropriate to consider six areas of causes that can
contribute to an effect: materials, machine, method,
personnel, measurement, and environment.
• Each one of these characteristics is then investigated for
sub-causes. Sub-causes are specific items or difficulties
that are identified as a factual or potential cause to the
problem (effect).
5.11 Cause-and-Effect Diagram
Variations in creating a cause-and-effect diagram:
• A team may choose to emphasize the most likely causes
by circling them.
• it can also be beneficial to identify noise factors (n) (e.g.,
ambient room temperature and a raw material
characteristic that cannot be controlled), and factors that
can be controlled (c) (e.g., process temperature or speed)
by placing the letter n or c next to the named effect.
• Include score with an importance and ease-of-resolution
matrix.
Cause-Effect (CE) Analysis
www.syncfusion.com/.../img/Fishbone_larger.png
5.11 Cause-and-Effect Diagram
Impact
Impl
High
Low
Easy
1
2
Hard
3
4
5.12 Affinity Diagram
Affinity diagram can organize and summarize the natural
grouping from a large number of ideas and issues.
• Boldly record each brainstorming idea individually on a
post note, using at a minimum a noun and verb.
• Next, place the post note on a wall and ask everyone,
without talking, to move the notes to the place where they
think the issue best fits.
• Upon completion of this sorting, create a summary or
header sentence for each grouping. (Create subgroups for
large groupings as needed with a subhead description.)
• Connect all finalized headers with their groupings by
drawing lines around the groupings.
Affinity
Diagram
http://www.asq.org/learnabout-quality/ideacreationtools/overview/affinity.html
5.12 Affinity Diagram
•
•
•
•
•
•
•
•
•
•
•
Infrastructure
Align projects with business needs
Create system to pull projects from business metrics needs
Establish project accountability
Plan steering committee meetings
Select champions, sponsors, and team leaders
Determine strategic projects and metrics
Communication plans
Incentive plans
Schedule project report outs
Champion/sponsor training
Compile lessons learned from past projects
Project Execution
• Project scoping
• Project approval
• Phase report outs
• Project closure
• Project leveraging
•
•
•
•
Training
Champion training
Black belt training
Green belt training
Use training material
w/roadmap
•
•
•
•
Culture
Create buy-in
Evaluate obstacles
and facilitate change
Integrate 6σ into
daily activities
Communication plan
5.13 Interrelationship Digraph (ID)
Interrelationship digraph permits systematic identification,
analysis, and classification of cause-and-effect relationships,
enabling teams to focus on key drivers or outcomes to
determine effective solutions.
• Assemble a team of 4 to 6 members
• Arrange the 5-25 items from another tool (e.g., an affinity
diagram) in a circular pattern on a flipchart.
• Draw relationship between the items by choosing any one
of the items as a starting point, with a stronger cause or
influence indicated by the origination of an arrow.
5.13 Interrelationship Digraph (ID)
• Upon the completion of a chart, get additional input from
others and then tally the number of outgoing and input
arrows for each item.
• A high number of outgoing arrows indicates that the item is
a root cause or driver that should be addressed initially.
• A high number of incoming arrows indicates a key outcome
item.
• A summary ID shows the total number of incoming and
outgoing arrows next to each item.
• Driver and outcome items can be highlighted using a
double box or bold box.
5.13 Interrelationship Digraph (ID)
http://www4.asq.org/blogs/statistics/Images/Interrelationship.jpg
5.14 Tree Diagram
• Tree diagrams can help people uncover, describe, and
communicate a logical relationship that is hierarchical
between important events or goals.
• Similarly, a tree can describe the hierarchy that leads to a
desirable or undesirable event. (fault tree or FT).
• With this approach a big idea or problem is partitioned into
smaller components.
• Logical operators such as AND or OR gates can connect
lower elements to higher elements in the hierarchy.
5.14 Tree Diagram
5.14 Tree Diagram
http://www.conceptdraw.com/resources/images/solutionsscreens/business/Root_Cause_Tree_Diagram.png
5.15 Why-Why Diagram
• A variation of the cause-and-effect diagram and tree
diagram (Higgins 1994; Majaro 1988).
• These final responses can be used as wisdom of the
organization inputs for further investigation.
WhyWhy
Diagram
http://www.syque.com/improvement/images/image343.gif
5.16 Matrix Diagram and
Prioritization Matrices
• A matrix diagram is useful to discover relationships
between two groups of ideas.
• A prioritization matrix quantifies and prioritizes items within
a matrix diagram: activities, goals, or characteristics
• Applications: cause-and-effect matrix and quality function
deployment (QFD)
5.16 Matrix Diagram and
Prioritization Matrices
• Within a prioritization matrix, one can assign relative
importance weights.
• Simply assigned by the organization or team.
• Other techniques: The analytical hierarchy process
(AHP)
• Within the AHP approach, a number of decisionmakers can integrate their priorities into a single
priority matrix using a pairwise fashion.
• This result of this matrix is a prioritization of the
factors.
5.16 Analytical Hierarchy Process
(AHP)
A
A. Fire in the belly
B
C
D
E
B2
A2
A2
A3
B1
B2
B3
C1
C3
B. Soft skill
C. Project management
D. Analytical skill
D2
E. Statistical knowledge
A. Fire in the belly
B. Soft skill
C. Project management
D. Analytical skill
E. Statistical knowledge
A
B
C
D
E
1
1/2
2/1
2/1
3/1
1
1/1
2/1
3/1
1
1/1
3/1
1
2/1
1
5.16 Analytical Hierarchy Process
(AHP)
A
B
C
D
E
1
1/2
2/1
2/1
3/1
B. Soft skill
2/1
1
1/1
2/1
3/1
C. Project management
1/2
1/1
1
1/1
3/1
D. Analytical skill
1/2
1/2
1/1
1
2/1
E. Statistical knowledge
1/3
1/3
1/3
1/2
1
A
B
C
D
E
A. Fire in the belly
A. Fire in the belly
1.0000
0.5000
2.0000
2.0000 3.0000
B. Soft skill
2.0000
1.0000
1.0000
2.0000 3.0000
C. Project management
0.5000
1.0000
1.0000
1.0000 3.0000
D. Analytical skill
0.5000
0.5000
1.0000
1.0000 2.0000
E. Statistical knowledge
0.3333
0.3333
0.3333
0.5000 1.0000
5.16 Analytical Hierarchy Process
(AHP)
A
B
C
D
E
Sum
Weights
A. Fire
5.0000
5.0000 7.5000 8.5000 17.5000
43.5000
0.2652
B. Soft
6.5000
5.0000 9.0000 10.5000 19.0000
50.0000
0.3049
C. Project
4.5000
3.7500 5.0000 6.5000 12.5000
32.2500
0.1966
D. Ana skill
3.1667
2.9167 4.1667 5.0000 10.0000
25.2500
0.1540
E. Stat
1.7500
1.4167 2.1667 2.6667
13.0000
0.0793
5.0000
164.0000
5.17 Process Decision Program Chart
(PDPC)
• A process decision program chart (PDPC) helps with the
organization and evaluation of processes and the creation
of contingency plans.
• PDPC can help anticipate deviations from expected events
and provide insight to the creation of effective contingency
plans.
• PDPC can help determine the impact of problems or
failures on project schedules. Specific actions can be
undertaken for problem prevention or mitigation of impact
when they do occur.
• Subjective probabilities of occurrence can be assigned and
then used for the assignment of priorities.
5.17 Process Decision Program Chart
(PDPC)
Berger et al. (2002) highlight the following steps, which are
common to all PDPC formats:
• Identify the process purpose.
• Identify the basic activities and related events associated
with the process.
• Annotate the basic activities and related events.
• Superimpose the possible (conceivable) deviations.
• Annotate the possible deviations.
• Identify and annotate contingency activities.
• Weight the possible contingencies.
5.17 Process Decision Program Chart
(PDPC)
5.17 Process Decision Program Chart
(PDPC)
http://www.syque.com/quality_tools/tools/TOOL
S12_files/image002.gif
5.18 Activity Network Diagram or
Arrow Diagram
• Activity network diagrams (arrow diagrams) help with the
definition, organization, and management of activities with
respect to time.
• The arrow diagram is used in program evaluation and
review technique (PERT) and critical path method (CPM)
methodologies.
5.18 Activity Network Diagram or
Arrow Diagram
Equipment testing
and modification
2
Final
debugging
Dummy
Equipment
installation
1
6
System
development
3
Manual
Testing
5
System
Training
7
System
changeover
System
Testing
Job
training
Position
recruiting
4
Orientation
Dummy
8
9
5.19 Scatter Diagram (Plot of Two Variables)
S4/IEE Application Examples
• An S4/IEE project was created to improve the 30,000-footlevel metric, days sales outstanding (DSO). A process
flowchart was created to describe the existing process. A
team created a cause-and-effect diagram in a
brainstorming session to trigger a list of potential causal
inputs and improvement ideas. One input that the team
thought could be an important cause was the size of the
invoice. A scatter diagram of DSO versus size of invoice
was created.
5.19 Scatter Diagram (Plot of Two Variables)
S4/IEE Application Examples
• An S4/IEE project was created to improve the 30,000-footlevel metric, the diameter of a manufactured part. A
process flowchart was created to describe the existing
process. A team created a cause-and-effect diagram in a
brainstorming session to trigger a list of potential causal
inputs and improvement ideas. One input that the team
thought could be an important cause was the temperature
of the manufacturing process. A scatter diagram of part
diameter versus process temperature was created.
5.19 Scatter Diagram (Plot of Two Variables)
• A scatter diagram (plot) assesses the relationship between
two variables (follow-up procedure to validate the
consensus relationship from a cause-and-effect diagram)
• 50 to 100 pairs of samples should be plotted
• the independent variable is on the x-axis while the
dependent variable is on the y-axis.
• A scatter diagram relationship does not predict a true
cause-and-effect relationship.
• The plot only shows the strength of the relationship
between two variables
• The correlation and regression techniques can be used to
test the statistical significance of relationships.
5.19 Scatter Diagram (Plot of Two Variables)
5.20 Example 5.1:
Improving a Process that has Defects
5.20 Example 5.1:
Improving a Process that has Defects
• From p-chart, the process is in control; no special causes
are noted.
• Defect rate is too high
5.20 Example 5.1:
Improving a Process that has Defects
From 3200
manufactured
PCBs
5.20 Example 5.1:
Improving a Process that has Defects
• A brainstorming session with experts in the field could then
be conducted to create a cause-and-effect diagram for the
purpose of identifying the most likely sources of the
defects.
• Regression analysis followed by a DOE might then be
most appropriate to determine which of the factors has the
most impact on the defect rate.
• After Changes are made to the process
• New Pareto chart to identify the next major cause
• Control charts to monitor the “insufficient solder”
problem
5.21 Example 5.2:
Reducing Total Cycle Time of A Process
• Development cycle time: 2~3 years
• Survey indicates the biggest deterrent is “procurement of
standard equipment and components”.
• Loss time and revenue due to delay is 22 times higher than
the equipment itself.
5.22 Example 5.3:
Improving A Service Process
• cycle time in a claim processing center, accounts payable
organization, or typing center.
• Current process flow
• Cause-and-effect diagram for improvement opportunities
• Pareto chart: simple solution
• Benefit/Cost analysis (B/C96)