Merz_Hiete Iscram_Vulnerability Indicators for Industrial Sectors

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Merz_Hiete Iscram_Vulnerability Indicators for Industrial Sectors

  1. 1. „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)
  2. 2. 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)
  3. 3. 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“ 3 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  4. 4. 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 4 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  5. 5. 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 5 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  6. 6. Existing Approaches • various vulnerability and risk indicators • focus mainly on social vulnerability 6 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  7. 7. 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 7 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  8. 8. 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 8 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  9. 9. 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 9 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  10. 10. 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 10 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  11. 11. 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 11 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  12. 12. 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 12 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  13. 13. 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 13 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  14. 14. 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 14 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  15. 15. 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) 15 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  16. 16. 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 16 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  17. 17. Exemplar results - overall vulnerability index Sector Vulnerability Score 17 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  18. 18. Exemplar results – supply chain dependency Sector Vulnerability Score 18 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  19. 19. 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 19 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)
  20. 20. 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 20 ISCRAM 2009, Göteborg 13.05.2009 KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH)

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