Merz_Hiete Iscram_Vulnerability Indicators for Industrial Sectors

„An Indicator Framework to Assess the Vulnerability of
Industrial Sectors against Indirect Disaster Losses“
Michael Hiete and Mirjam Merz

ISCRAM 2009
10 - 13 May 2009, Göteborg, Sweden

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

KIT – The Cooperation between the Forschungszentrum Karlsruhe GmbH
and the 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

2 ISCRAM 2009, Göteborg 13.05.2009

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

Industrial Risk - Vulnerability

Exposure
Earthquake
Vulnerability
Sensitivity Risk =
Storm

Hazard
Hazard X Flooding
Vulnerability
Resilience Drought
R=H*V
Landslide
Environm.
Economic

…
Social

Vulnerability:
„ Proposition of an element or a system to be affected or
susceptible to damage“



Industrial disaster losses

Direct disaster losses Indirect disaster losses

Primary direct losses: Primary indirect losses
Physical damage to: Loss of production due to:

buildings direct damage
production equipment infrastructure disruptions
raw material supply chain disruptions
products in stock
control installations
service installations

Secondary direct losses Secondary indirect losses
Secondary hazards Market disturbances
Secondary damages (e.g. explosion) Decreased competitiveness
Remediation and emergency costs 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 “ Source: Cutter, 2003

• 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



Existing Approaches

• various vulnerability and risk indicators
• focus mainly on social vulnerability



Fundamentals in indicator development

Data
Datenebene Indicators
Indikatorenebene Vision
Goal
Leitbildebene

Aggregation Indicator Vision &
Biosphere Aggregations- Indikatoren- Leitbild- und
Biosphäre
Human process
prozeß system
system goal system
Zielsystem

human Vision
Leitbild
Industry
Mensch
Meßdaten
Measurement
Determination
Inter-
Inter-
aktionen
actions
Selection
Seclection Ziele
Selektions- Target
Process
process
prozeß
Environment
Umwelt
Standards
Indicator
Measurement
Meßdaten Standards

Objectivity of the information
Objektivität der Information

Normativity derthe information
Normativität of Information

Concentration of the dataauf information
Konzentration der & das
regarding benötigten Aussage
Ziel hin the vision and goal
Source: Birkmann, 1999



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: Social
Risk Index
SRI Sector Specific
Indirect Industrial
Risk Index Risk Index
IDRI SIRI

Total Industrial
Risk Index Risk Index
TRI IRI

Direct Regional
Risk Index Sector
DRI Allocation



Indicator development steps

1 Definition of goals

2 Definition of system boundaries

3 Theoretical framework

5 Selection of indicators

6 Data collection

iterative process
7 Standardization/Weighting/Aggregation

8 Visualization of indicator results

9 Sensitivity/Uncertainty analysis



Theoretical framework and indicator selection
Theoretical framework: 1

• theoretical basis of the assessment (depiction of causal linkages and theoretical
dependencies)
• subjective
• trade-off between accuracy and simplification
Indicator selection: 2 3
• 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
Indicator selection step Source
Identification of the theoretical
vulnerability framework

Identification of production requirements Risk management literature
1 Identification of dependencies Production science literature
Identification of risk factors/determinants of vulnerability Expert judgement

2 Derivation of measurable variables (sub-indicators) No additional sources needed

Statistical Data
3 Assignment of sub-indicator values
Expert judgement



Hierarchical vulnerability framework

index (first level) indicator sub indicators variables alternatives

Value of production equipment
Capital dependency Sector 1
Specialization of
production equipment Sector 2
Input factor
Labour dependency
dependency Number of different materials Sector 3

Material dependency Type of materials Sector 4

Degree of specialization Sector 5

In-house processing Sector 6
Sector specific
Supply dependency
indirect Sector 7
Supply chain Clustering tendency
vulnerability index
dependency
Sector 8
Demand dependency Customer proximity

Water consumption
Sector N
Water dependency Water importance

Degree of water self supply

Infrastructure
Transport dependency Transport volume
dependency

Power consumption

Power dependency Power importance

Degree of power self supply



Sub-indicator „Power dependency”

high vulnerability
Variable I: „Power Consumption“
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) low vulnerability

sectors having high power consumption
are more vulnerable to power disruptions
Operationalisation:
Power Consumption/Gross Value Added

Variable II: „Degree of Power Self Supply“ low vulnerability

Assumption:
in most cases industrial electricity generation
can be operated independently from public
power supply
sectors showing a high degree of power
self supply are less vulnerable to power high vulnerability
disruptions
Operationalisation:
Power Generation/Power Consumption



Sub-indicator „Supply dependency”

• 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)

Variable I: „In-house production“ low vulnerability

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

Problem:
Neglecting of the criticality of the supplied parts



Standardization

• important prerequisite for aggregation
because of different units and scales
• enables integration and comparison of
quantitative and qualitative data
xi = measured value of sub-indicator I
• depiction of measured variables on a xi = 0 lowest vulnerability
scale between 0 an 1 using
xi = 1 highest vulnerability
value functions

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

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



Weighting and Aggregation

Weighted sum aggregation: Weighting procedure in LDW®

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)



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: michael.hiete@wiwi.uni-karlsruhe.de
mirjam.merz@wiwi.uni-karlsruhe.de



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