Merz_Hiete Iscram_Vulnerability Indicators for Industrial Sectors

KIT – The Cooperation between the Forschungszentrum Karlsruhe GmbH and the Universität Karlsruhe (TH)

INSTITUTE FOR INDUSTRIAL PRODUCTION (IIP)CENTER FOR DISASTER MANAGEMENT AND RISK REDUCTION TECHNOLOGY (CEDIM)

„An Indicator Framework to Assess the Vulnerability of Industrial Sectors against Indirect Disaster Losses“

ISCRAM 2009

10 - 13 May 2009, Göteborg, Sweden

Michael Hiete and Mirjam Merz

ISCRAM 2009, Göteborg 2 13.05.2009

KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)

Overview

Introduction

• Industrial vulnerability and disaster losses

Indicators and decision making

• Vulnerability indicators

• Existing approaches

Development of an indicator framework for indirect industrial vulnerability assessment

• Theoretical framework and indicator selection

• Standardization, weighting and aggregation

• Exemplar results

Conclusion and outlook



Industrial Risk - Vulnerability

Risk =Hazard X

Vulnerability

R = H * VVulnerability

Hazard

Earthquake

Storm

Flooding

Drought

Landslide

…

Vulnerability:

„ Proposition of an element or a system to be affected or

susceptible to damage“

Exposure

Sensitivity

Resilience

Social

Environm.

Economic



Industrial disaster losses

Direct disaster losses Indirect disaster losses

Primary direct losses:

Physical damage to:

buildings

production equipment

raw material

products in stock

control installations

service installations

Secondary direct losses

Secondary hazards

Secondary damages (e.g. explosion)

Remediation and emergency costs

Primary indirect losses

Loss of production due to:

direct damage

infrastructure disruptions

supply chain disruptions

Secondary indirect losses

Market disturbances

Decreased competitiveness

Damage to company’s image

Extra labour for process recovery



Vulnerability indicators for decision making

Decision making for industrial disaster management:

• vulnerability must be measured for disaster risk reduction

• multifaceted concept of vulnerability

• different spatial and contextual dimensions

vulnerability indicators

Vulnerability indicator:

“operational representation of a characteristic or a quality of a system able to

provide information regarding it’s susceptibility, coping capacity and resilience to an

impact of a disaster “

• description of complex system characteristics in a transparent way

• combination of quantitative and qualitative attributes

• rankings, benchmarking, relative vulnerability assessment

• composite-indicators: Aggregation of a set of indicators to one single index

Source: Cutter, 2003



Existing Approaches

• various vulnerability and risk indicators

• focus mainly on social vulnerability



Fundamentals in indicator development

Datenebene Indikatorenebene Leitbildebene

BiosphäreIndikatoren-

system

Leitbild- und

Zielsystem

Mensch

Umwelt

Inter-

aktionen

Meßdaten

Selektions-

prozeß

Aggregations-

prozeß

Meßdaten

Leitbild

Ziele

Standards

Objektivität der Information

Normativität der Information

Konzentration der auf das

Ziel hin benötigten Aussage

Data Vision Indicators

Vision & goal system

Vision

Biosphere Indicator system

Environment

Target

Normativity of the information

Measurement

Aggregation process

Objectivity of the information

Concentration of the data & information regarding the vision and goal

Measurement

human

Seclection process

Inter-actions

Source: Birkmann, 1999

Industry

Human

Determination

Goal

Indicator

Standards

Selection Process



Indicator Framework for indirect industrial vulnerability assessment

Objective of the approach:

• industrial vulnerability: development of an indirect sector specific industrial vulnerability index

• integration of the sector specific industrial vulnerability index into an overall framework

• quantification of the regional indirect disaster risk for decision making (relative ranking of regions)

Overall framework:

Total

Risk Index

TRI

Indirect

Risk Index

IDRI

Direct

Risk Index

DRI

Social

Risk Index

SRI

Industrial

Risk Index

IRI

Sector Specific

Industrial

Risk Index

SIRI

Regional

Sector

Allocation



Indicator development steps

Definition of goals

Definition of system boundaries

Theoretical framework

Selection of indicators

Data collection

Standardization/Weighting/Aggregation

Visualization of indicator results

Sensitivity/Uncertainty analysis

iterative process

1

2

5

6

7

8

9

3



Theoretical framework and indicator selection

SourceIndicator selection step

1

Identification of production requirements

Identification of dependencies

Identification of risk factors/determinants of vulnerability

Derivation of measurable variables (sub-indicators)

Assignment of sub-indicator valuesIden

tifica

tion

of

the

theo

retical

vuln

era

bili

ty f

ram

ew

ork Risk management literature

Production science literature

Expert judgement

Statistical Data

Expert judgement

No additional sources needed

3

2

Theoretical framework:

• theoretical basis of the assessment (depiction of causal linkages and theoretical

dependencies)

• subjective

• trade-off between accuracy and simplification

Indicator selection:

• limited number of sub-indicators in order to keep it transparent

• quality criteria for indicator selection: e. g. measurable, reproducible, comparable, sensitive

• limiting factor: data availability

1

2 3



Hierarchical vulnerability framework

Sector specific

indirect

vulnerability index

Sector 4

Sector 3

Sector 5

Sector 1

Sector 2

Sector 6

Sector 7

Sector 8

Water dependency

Power dependency

Supply chain

dependency

Infrastructuredependency

Supply dependency

Demand dependency

Sector NWater consumption

Power consumption

Power importance

Input factor

dependency

Capital dependency

Labour dependency

Material dependency

Transport dependency

Degree of water self supply

Transport volume

Degree of power self supply

Water importance

index (first level) indicator sub indicators variables alternatives

Value of production equipment

Number of different materials

Type of materials

Degree of specialization

In-house processing

Clustering tendency

Customer proximity

Specialization of


Sector specific

indirect

vulnerability index

Sector 4

Sector 3

Sector 5

Sector 1

Sector 2

Sector 6

Sector 7

Sector 8

Water dependency

Power dependency

Supply chain

dependency

Infrastructuredependency

Supply dependency

Demand dependency

Sector NWater consumption

Power consumption

Power importance

Input factor

dependency

Capital dependency

Labour dependency

Material dependency

Transport dependency

Degree of water self supply

Transport volume

Degree of power self supply

Water importance

index (first level) indicator sub indicators variables alternatives

Value of production equipment

Number of different materials

Type of materials

Degree of specialization

In-house processing

Clustering tendency

Customer proximity

Specialization of




Sub-indicator „Power dependency”

Assumption:

the higher the power demand the more difficult it is to replace the power demand in case of a critical event (e. g. with backup generators)

� sectors having high power consumptionare more vulnerable to power disruptions

Operationalisation:

Power Consumption/Gross Value Added

Variable I: „Power Consumption“

Assumption:

in most cases industrial electricity generation

can be operated independently from public power supply

� sectors showing a high degree of powerself supply are less vulnerable to powerdisruptions

Operationalisation: Power Generation/Power Consumption

Variable II: „Degree of Power Self Supply“

high vulnerability

low vulnerability

low vulnerability

high vulnerability



Sub-indicator „Supply dependency”

Assumption:

If the in-house production is high, less goods must be purchased from suppliers

� sectors showing a high degree of in-house production are less vulnerable to supply chain disruptions

Operationalisation: in-house production input [manufacturing costs]/overall input [manufacturing costs]

Problem:Neglecting of the criticality of the supplied parts

Variable I: „In-house production“

• supply chain design is highly company dependent

• generalizations on the sector level are difficult

• estimation from input-output tables (showing the regional economic linkages of different sectors)

high vulnerability

low vulnerability



Standardization

Linear value function for sub-indicators with aggravating impact on vulnerability

Linear value function for sub-indicators with weakening impact on vulnerability

xi= measured value of sub-indicator I

xi= 0 lowest vulnerability

xi

= 1 highest vulnerability

• important prerequisite for aggregation

because of different units and scales

• enables integration and comparison of

quantitative and qualitative data

• depiction of measured variables on a

scale between 0 an 1 using

value functions

Vu

lner

ab

ilit

y



Weighting and Aggregation

Weighted sum aggregation:

Weighting vector wi = (w1…wn)

wi with

• weights represent the relative

importance of individual

sub-indicators

• different weighting methods, e. g.:

- AHP

- SWING, SMARTER

- direct weighting

• integration of hazard

dependencies via weighting

(e. g. dimension or type of hazard)

Weighting procedure in LDW®



Exemplar results - overall vulnerability index

Sector Vulnerability Score

• not all data available yet

� data assumptions

� substitution of values with similar data

• equal weighting of indicators



Exemplar results - overall vulnerability index




Exemplar results – supply chain dependency




Conclusion

• The presented indicator framework helps to depict the complex and multidimensional

concept of indirect vulnerability of industrial sectors to disasters

• Vulnerability varies strongly between different sectors

• The aggregation into one overall vulnerability index is critical, underlying linkages and

theoretical foundations can be better seen in less aggregated indicators

• This enabled a better understanding of industrial vulnerability and the identification of

particular vulnerable processes and elements

• Limitation: data availability and identification of weights

Outlook:

• consideration of data correlations

• the assessment of uncertainties:

• data uncertainties

• model uncertainties (e.g. indicator selection, weighting, standardization)

• the development of an indicator framework on the company level in order to support

decision making within single companies



Thank you for your attention!

Dr. Michael Hiete and Mirjam Merz

Institute for Industrial Production (IIP)

Universität Karlsruhe (TH)

E-mail: [email protected] [email protected]

Merz_Hiete Iscram_Vulnerability Indicators for Industrial Sectors

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