Enterprise Architecture for decision making in...
Transcript of Enterprise Architecture for decision making in...
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Enterprise Architecture for
decision making in MODAF
Ulrik Franke, Ph.D. student
Industrial Information and Control Systems
Royal Institute of Technology, Stockholm
SESAM, Stockholm, April 27, 2009
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‘It defines a way of representing an EnterpriseArchitecture which enables stakeholders tofocus in on specific areas of interests in theenterprise, whilst retaining sight of the “bigpicture”.’
Ministry of Defence
Architecture Framework
‘To assist decision-makers, MODAF providesthe means of abstracting essential informationfrom the underlying complexity and presentingit in a way that maintains coherence andconsistency.’
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‘Would you tell me,
please, which way I
ought to go from here?’
‘That depends a good
deal on where you
want to get to,’ said
the Cat.
‘I don’t much care
where—’ said Alice.
‘Then it doesn’t matter
which way you go,’
said the Cat.
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LESSON #1
An Enterprise Architecture effort is not an
end in itself; it is a means to something
else. Never ever start an EA effort before
you know what you want to achieve.
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IT DepartmentDecision Domain
Information System sDecision Domain
BusinessGoal Domain
DECISION MAKING
Delivery QualityDelivery Quality
100%0%
AvailabilityAvailability
100%0%
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?
DECISION MAKING
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MaturityMaturity
54321
MaturityMaturity
54321
Delivery QualityDelivery Quality
100%0%
BusinessElectricity Distribution
IT DepartmentAvailability Management
Information SystemsSCADA System
AvailabilityAvailability
100%0%
DECISION MAKING
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BusinessElectricity Distribution
Delivery QualityDelivery Quality
100%0%
Delivery QualityDelivery Quality
100%0%
IT DepartmentAvailability Management
Information SystemsSCADA System
AvailabilityAvailability
100%0%
DECISION MAKING
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?
DECISION MAKING
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MaturityMaturity
54321
MaturityMaturity
54321
Delivery QualityDelivery Quality
100%0%
BusinessElectricity Distribution
IT DepartmentAvailability Management
Information SystemsSCADA System
AvailabilityAvailability
100%0%
AvailabilityAvailability
100%0%
Delivery QualityDelivery Quality
100%0%
DECISION MAKING
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BusinessElectricity Distribution
Delivery QualityDelivery Quality
100%0%
Delivery QualityDelivery Quality
100%0%
IT DepartmentAvailability Management
Information SystemsSCADA System
AvailabilityAvailability
100%0%
AvailabilityAvailability
100%0%
DECISION MAKING
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?Delivery QualityDelivery Quality
100%0%
Delivery QualityDelivery Quality
100%0%
Delivery QualityDelivery Quality
100%0%
Delivery QualityDelivery Quality
100%0%
Delivery QualityDelivery Quality
100%0%
Delivery QualityDelivery Quality
100%0%
DECISION MAKING
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LESSON #2
Different decisions require different
information. Good models structure what
you know before making decisions, and
enable scenario analysis.
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Dependency analysis
• How do high-level operationalconcepts (airlift capability, searchand rescue, etc.) depend uponparticular technical systems(vehicles, radars, IT systems, etc.)?
• There is a gap between theenterprise-level decision making andthe low-level implementation
• If this gap is not bridged, decisionswill not be rational
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Dependencies in MODAF
• ‘Dependencies of interest to
MOD include: capability
dependencies, programmatic
dependencies, technology
dependencies etc. Analysis of
dependencies of this type is
considered a key use of an
Enterprise Architecture.’
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Sample MODAF products
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How should MODAF modelslook?
• The challenge is to give just
enough contents to MODAF
models to enable the relevant
kind of decision making – no
more, no less!
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LESSON #3
MODAF models are good for visualizing
dependencies, but not so good for
analyzing them. Therefore, they are
difficult to use for scenario analysis.
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Can MODAF become a morepowerful decision making tool?
• Information on causal relationsenables decision making usingscenarios.
• As part of KTH research, we havedeveloped a method for extendingMODAF models with attributes andattribute relations for dependencyanalysis using Fault Tree Analysisand Bayesian networks
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Simple FT-BN analysis example
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From a MODAF model…
Ope
ration
alSystem
s/Services
<<System>>UGV TA
<<Op. Activity>>Kill target
<<System>>
Comms satellite to
TA system
<<Op. Activity>>
Target acquisition
<<Op. Activity>>
C2<<Op. Activity>>
Strike
<<System>>UAV TA
<<System>>
Comms satellite to
striking system
<<System>>
Armed UAV
<<System>>
Artillery
<<needlin
e>>
<<needline>>
<<
need
line>
>
<<needline>>
<<n
eedlin
e>>
<<ne
edline>>
<<
ne
ed
line>
> <<needlin
e>>
<<
ne
edl in
e>
>
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…to a fault tree…
Ope
ration
alSystem
s/Services
<<System>>UGV TA
<<Op. Activity>>Kill target
<<System>>
Comms satellite to
TA system
<<Op. Activity>>
Target acquisition
<<Op. Activity>>
C2
<<Op. Activity>>
Strike
AND
<<System>>UAV TA
OR
<<System>>
Comms satellite to
striking system
<<System>>
Armed UAV
<<System>>
Artillery
AND OR
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… to a Bayesian network
Antonov et. al and Dixon et. al
System
s/Services
Operational <<Node>>
UAV operator
platform
<<Node>>
Target
<<System>>
UGV TA
<<Op. Activity>>
Kill target
<<System>>
Comms satellite
<<Op. Activity>>
Target acquisition
<<Op. Activity>>
C2
<<Op. Activity>>
Strike
AND
<<System>>
UAV TA
OR
<<System>>
Armed UAV
<<System>>
Artillery
AND OR
- Quality - Quality - Precision
- Successful kill
- Signal latency
- Autopilot
status
- Video UI
enhancement
status
- System
status
- System
status
- System status
- System
status
- System
status
- Moving
- Moving
System Status
satellite
Signal delay
Moving target Yes No Yes No Yes No Yes No
High 0.3 0.4 0.6 0.7 0 0 0 0
Medium 0.1 0.2 0.2 0.2 0 0 0 0
None 0.6 0.4 0.2 0.1 1 1 1 1
Quality
of C2
Non-failed Failed
Yes No Yes No
System status
of UGV
System status
of UAV
Video UI
enhancement
status
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
High 0.6 0.6 0.4 0.4 0.6 0.4 0 0
Medium 0.3 0.3 0.2 0.2 0.3 0.2 0 0
None 0.1 0.1 0.4 0.4 0.1 0.4 1 1
Quality
of TA
Failed
Non-failed Failed
Non-failed
Non-failed Failed
Inspired by Fincannon et. al [10]
Inspired by Dougherty [7]
Inspired by Antonov et. al [1] and Dixon et al. [6]
System Status of Armed UAV
System Status of Artillery
Moving UAV op. Plattform
Autopilot status
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
Non-
failed Failed
High 0.6 0.6 0.8 0.7 0.6 0.5 0.8 0.6 0.6 0.6 0.6 0.6 0 0 0 0
Medium 0.2 0.2 0.1 0.2 0.2 0.3 0.1 0.2 0.2 0.2 0.2 0.2 0 0 0 0
None 0.2 0.2 0.1 0.1 0.2 0.2 0.1 0.2 0.2 0.2 0.2 0.2 1 1 1 1
Failed
Failed
Yes No Yes No Yes No Yes No
Non-failedNon-failed Failed
Non-failed
Precision
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Scenarios for decision making
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LESSON #4
Fault Tree Analysis and Bayesian networks
enable causality based analysis in close
support of decision making needs
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Summary of the lessons
1. Set the goals before you choose themeans
2. Scenario analysis is a powerful wayto visualize the impact of decisions
3. Traditional MODAF analysis is weakon causality and not very good forscenario driven decision making
4. Fault Tree Analysis and Bayesiannetworks enable causality basedanalysis in close support of decisionmaking needs
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Thank you!
Questions and feedback?
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References
• Ulrik Franke, Waldo Rocha Flores, Pontus Johnson:
Enterprise Architecture Dependency Analysis using
Fault Trees and Bayesian Networks, Proc. 42nd
Annual Simulation Symposium (ANSS), pp. 209-
216, March 2009
• Ulrik Franke, Pontus Johnson, Evelina Ericsson,
Waldo Rocha Flores, Kun Zhu: Enterprise
Architecture analysis using Fault Trees and MODAF,
Proc. CAiSE Forum 2009, June 2009, to appear
• Read more on www.ics.kth.se