smarter manufacturing sustainable futures 5 flexinet project risk management

Smarter Manufacturing: Sustainable FuturesCoventry University, 8 Oct 2014

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Risk Management in FLEXINET

Prof. Dobrila Petrovic , Ali Niknejad, Prof. Keith Popplewell


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


3 of XXRisk Applications

� Initial Risk Analysis and Documentation Application

� Strategic Risk Assessment Application

� Operational Risk Assessment Application

� Early Warning Notification Application


4 of XXStrategic Risk Assessment

GPN

Configuration

Risk AnalysisCost/Value

Analysis

Business

Model

GPN Evaluation and

Comparison


5 of XXRisk Concepts

Risk Concepts

Risk Factor

Disruptive

event

Risk

scenario

Perturbation

Inter

dependency

Mitigation

Resilience

Propagation

Inoperability

Economic

loss of risk


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


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 ✓


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


9 of XX

Aff

ect

ed

Re

gio

n

An Illustrative Example of a Risk Scenario: Period 1


Products/Packaging


Plant


Suppliers of Apples

Suppliers of Yeast



• External Perturbation: ‘Political instability’ in the region

• Suppliers of Yeast are fairly affected

• Suppliers of Flavourings are slightly affected

Loss of Risk: Zero


10 of XX

Aff

ect

ed

Re

gio

n



Products/Packaging


Plant


Suppliers of Apples

Suppliers of Yeast



• External Perturbation: None

Loss of Risk: Very Low


11 of XX

Aff

ect

ed

Re

gio

n



Products/Packaging


Plant


Suppliers of Apples

Suppliers of Yeast



• 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


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Aff

ect

ed

Re

gio

n



Products/Packaging


Plant


Suppliers of Apples

Suppliers of Yeast




Loss of Risk: Medium


13 of XX

Aff

ect

ed

Re

gio

n



Products/Packaging


Plant


Suppliers of Apples

Suppliers of Yeast




Loss of Risk: Medium to High


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



� Rates of interdependency criteriaVery Low, Low, Medium, High, Very High

� Perturbations of nodes

� Expected node’s revenue



� 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


17 of XXInoperability model

� Economic loss of risk

o Multiplication of intended economic revenue and inoperability


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


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


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


21 of XX

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