Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014
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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
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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
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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
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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
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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
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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
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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
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