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Presentation of FLEXINET Research Project (EU FP7 Factories of the Future) at the Smarter Manufacturing Sustainable Futures Workshop for collaboration between EU research projects investigating aspects of IT support to Manufacturing.
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Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
1 of XX
Risk Management in FLEXINET
Prof. Dobrila Petrovic , Ali Niknejad, Prof. Keith Popplewell
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
2 of XXOutline
� Risk Applications
� Strategic Risk Assessment� Risk Concepts� Steps in Strategic Risk Analysis� Risk Factors
� An Illustrative Example of a Risk Scenario
� Inoperability Model
� Static Inoperability model� Dynamic Inoperability model� Fuzzy Dynamic Inoperability Model
� Current Research
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
3 of XXRisk Applications
� Initial Risk Analysis and Documentation Application
� Strategic Risk Assessment Application
� Operational Risk Assessment Application
� Early Warning Notification Application
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
4 of XXStrategic Risk Assessment
GPN
Configuration
Risk AnalysisCost/Value
Analysis
Business
Model
GPN Evaluation and
Comparison
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
5 of XXRisk Concepts
Risk Concepts
Risk Factor
Disruptive
event
Risk
scenario
Perturbation
Inter
dependency
Mitigation
Resilience
Propagation
Inoperability
Economic
loss of risk
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
6 of XXSteps in Strategic Risk Assessment
� Choosing and customising risk factors
� Define risk scenarios
�Sequence of disruptive events, their perturbation, timing and zone of effect
� Customising the Inoperability model
� Analysing and comparing alternative GPN configurations with respect to risk
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
7 of XXRisk Factors
Risk factor
Classification
Su
pp
ly
Pro
du
ction
Info
rma
tion
an
d C
on
trol
Log
istics
De
ma
nd
Exte
rna
l
Delayed deliveries ✓ ✓
Unreliable supply ✓
Unavailability of materials ✓
Unanticipated level of demand ✓
Food safety Issues ✓ ✓ ✓
Technological challenge ✓ ✓
Uncertainty in new markets ✓
Import or export controls ✓
Future regulation ✓
Political instability ✓
Price and currency risk ✓
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
8 of XX
Aff
ect
ed
Re
gio
n
An Illustrative Example of a Risk Scenario
Suppliers of Bottling
Products/Packaging
Suppliers of Sugar Cider Fermentation
Plant
Bottling Plant Customers
Suppliers of Apples
Suppliers of Yeast
Suppliers of Flavourings
Full Operability Low Inoperability Medium Inoperability High Inoperability
• ‘Political instability’ in a region that involves two types of suppliers
• Interdependent risk factor: ‘Price and currency risk’ (with a delay of 2 periods)
• Risk scenario likelihood: Low
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
9 of XX
Aff
ect
ed
Re
gio
n
An Illustrative Example of a Risk Scenario: Period 1
Suppliers of Bottling
Products/Packaging
Suppliers of Sugar Cider Fermentation
Plant
Bottling Plant Customers
Suppliers of Apples
Suppliers of Yeast
Suppliers of Flavourings
Full Operability Low Inoperability Medium Inoperability High Inoperability
• External Perturbation: ‘Political instability’ in the region
• Suppliers of Yeast are fairly affected
• Suppliers of Flavourings are slightly affected
Loss of Risk: Zero
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
10 of XX
Aff
ect
ed
Re
gio
n
An Illustrative Example of a Risk Scenario: Period 2
Suppliers of Bottling
Products/Packaging
Suppliers of Sugar Cider Fermentation
Plant
Bottling Plant Customers
Suppliers of Apples
Suppliers of Yeast
Suppliers of Flavourings
Full Operability Low Inoperability Medium Inoperability High Inoperability
• External Perturbation: None
Loss of Risk: Very Low
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
11 of XX
Aff
ect
ed
Re
gio
n
An Illustrative Example of a Risk Scenario: Period 3
Suppliers of Bottling
Products/Packaging
Suppliers of Sugar Cider Fermentation
Plant
Bottling Plant Customers
Suppliers of Apples
Suppliers of Yeast
Suppliers of Flavourings
Full Operability Low Inoperability Medium Inoperability High Inoperability
• External Perturbation: ‘Price and currency risks’ in the region
• Suppliers of Yeast are slightly affected
• Suppliers of Flavouring are much affected
Loss of Risk: Low
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
12 of XX
Aff
ect
ed
Re
gio
n
An Illustrative Example of a Risk Scenario: Period 4
Suppliers of Bottling
Products/Packaging
Suppliers of Sugar Cider Fermentation
Plant
Bottling Plant Customers
Suppliers of Apples
Suppliers of Yeast
Suppliers of Flavourings
Full Operability Low Inoperability Medium Inoperability High Inoperability
• External Perturbation: None
Loss of Risk: Medium
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
13 of XX
Aff
ect
ed
Re
gio
n
An Illustrative Example of a Risk Scenario: Period 5
Suppliers of Bottling
Products/Packaging
Suppliers of Sugar Cider Fermentation
Plant
Bottling Plant Customers
Suppliers of Apples
Suppliers of Yeast
Suppliers of Flavourings
Full Operability Low Inoperability Medium Inoperability High Inoperability
• External Perturbation: None
Loss of Risk: Medium to High
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
14 of XXInoperability Model
�Input
� Network Configuration
� Interdependency criteria
- Trade volume - Inventory - Security of
information flow
- Substitutability of the product - Compatibility of IT systems - Distance /
Lead-time
- Substitutability of the
supplier/customer
- Information transparency - Collaboration
agreement
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
15 of XXInoperability Model
� Rates of interdependency criteriaVery Low, Low, Medium, High, Very High
� Perturbations of nodes
� Expected node’s revenue
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
16 of XXInoperability Model
� Outputs� Inoperability rate of nodes
� = �∗� + �∗ where:
o �∗ − percentage vector of reduced final demand/supply
o �∗ − normalized interdependency matrix
o � − inoperability vector
qi1
qi
ci
qi2
cj
qj
qji
qij
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
17 of XXInoperability model
� Economic loss of risk
o Multiplication of intended economic revenue and inoperability
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
18 of XXDynamic Inoperability Model
�Time-varying perturbations
� Resilience
� + 1 = � + � �∗� + �∗ − �
o � − inoperability vector
o �∗ − percentage vector of reduced demand/supply
o �∗ − normalized interdependency matrix
o � − industry resilient coefficient matrix
- Effective Risk Management Methods
- Adaptability
- Financial Liquidity
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
19 of XXFuzzy Dynamic Inoperability Model
� Uncertain and vague parameters
�Interdependency of nodes �Perturbations�Resilience
o Around 2, Between 4 and 10 but most likely 8
o Low, medium, higho Limited impact, some degradation, considerable impact
� Fuzzy sets�Partial memberships to the set
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
20 of XXCurrent Research
�Development of a fuzzy dynamic inoperability model
� Fuzzy perturbation, risk interdependency and resilience
�Development of a generic database of risk factors, their interdependencies and relevant risk scenarios
Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
21 of XX