Heidtke

Information about Heidtke

Published on February 29, 2008

Author: Flemel

Source: authorstream.com

Content

Slide1:  Reducing the “Gap of Pain”: Optimizing Federal Resource Availability After Major Incidents Major Curtis L. Heidtke Chief, Operations Law Division Joint Force Headquarters – National Capital Region September 5, 2007 HSDEC Fall Symposium, Colorado Springs, CO Role of Humanitarian Logistics:  2 Role of Humanitarian Logistics “Disaster relief is 80% logistics.” Lynn Fritz, The Fritz Institute Humanitarian logistics has been problematic for the US Government for a long time 1953 Netherlands floods 1989 Hurricane Hugo 1993 Hurricane Andrew 1999 Hurricane Floyd 2005 Hurricane Katrina Examples:  3 Examples 1989 Hurricane Hugo FEMA takes 10 days to open first claim processing center 1993 Hurricane Andrew FEMA faulted in GAO report for slow delivery of vital services 1999 Hurricane Floyd FEMA criticized for no swiftwater/flood rescue boats and equipment Hurricane Katrina:  4 Hurricane Katrina “FEMA’s logistics system failed out of the box.” Hurricane Katrina: A Nation Still Unprepared, S. Rep. 109-322, 2006 MEMA receives <15% of requested quantities of water, ice, MREs AEMA asks for 100 trucks each of water and ice; receives 17 water trucks, 16 ice trucks No commodity tracking capability – “It has been a problem at every disaster I’m aware of.” William Lokey, Federal Coordinating Officer for Louisiana “You could have taken ‘Andrew’ out and put ‘Katrina’ in.” R. David Paulison, current FEMA Director Consequences:  5 Consequences “Ordinary people forced to endure inhuman circumstances were the victims of these failures.” -- Hurricane Katrina: A Nation Still Unprepared, S. Rep. 109-322, 2006 The “Gap of Pain”:  6 The “Gap of Pain” Time Requirements = State & local resources exhausted  = Federal resources arrive   What Causes the Gap?:  7 What Causes the Gap? Structural causes Tiered response; local  state  federal Built-in delays Assumes locals and states can accurately and quickly assess their needs and capabilities Bureaucratic stovepipes and inefficiencies Procedural causes Lack of coordinated strategic planning Failure to adopt best practices Failure to leverage technology Status Quo:  8 Status Quo NIMS/NRP Local attempts to meet needs; if it can’t – State fills carryover requirements; if it can’t – State requests federal assistance Feds respond Coordinated through JFO Resources on-hand, MAs, commercial contracts Many discrete logistics operations – ESFs within JFO (e.g. ETC), DHS, NRCC, RRCC, GSA, DoD, DOT, DHHS, etc. No common operating picture No single, integrated logistics plan Prototypical State Process:  9 Prototypical State Process COUNTY EMA’S Identify Resource requirement Submits request to State EMA State EMA Determines best method of support Submits Request to FEMA or Vendors FEMA/FOSA STATE RESOURCES OR VENDOR County Staging Area Points of Distribution (POD) Points of Distribution (POD) Points of Distribution (POD) STATE OPERATIONS STAGING AREA DAILY TELECONF/0900 EMITS EMAIL TELEPHONE SL DoD Process:  10 DoD Process What Causes the Gap?:  11 What Causes the Gap? Structural causes Tiered response; local  state  federal Bureaucratic stovepipes and inefficiencies Procedural causes Lack of coordinated strategic planning Failure to adopt best practices Failure to leverage technology Alternatives – Pull vs. Push:  12 Alternatives – Pull vs. Push Pull system – respond upon request Pull system deficiencies: Reactive by nature, thus built-in delays Assumes states/locals can accurately and quickly assess needs and capabilities Push system – respond ahead of the disaster Proactive in nature Bypasses many bureaucratic hurdles Obviates need for timely/accurate post-incident info Requires accurate needs/capabilities forecasts Alternatives:  13 Alternatives Pre-positioning Risk analysis  identification of asset types Utility analysis: costs, alternative uses Proactive deployment Would require legislation Funding? Surge transportation Requires asset availability Inherent delays Phased deployment Slide14:  14 Pre-incident Post-incident Requirements Alternatives Comparison The Case for Stochastic Modeling:  15 The Case for Stochastic Modeling Such a model would: Generate raw supply chain data Identify persistent shortfalls Spot data trends Evaluate effects of various actions Perform sensitivity testing Quickly test large numbers of alternative scenarios and conditions Objectives:  Objectives … model the allocation of resources: as a two-stage stochastic model optimizes expected rescued survivors and commodities delivered to affected areas … provide guidance on strategic decisions, while factoring in ensuing operational considerations … offer insights into trends over an independent variable range (e.g. budget) Stochastic Optimization Model:  Stochastic Optimization Model Decision variables are divided into two sets: first-stage: decided well before disaster strikes, and second-stage: based on the concept of recourse Anticipation Of Disaster Disaster Strikes Take Corrective Action First-stage Decisions Second-stage Decisions Expected survivors Scenario 1 First-Stage Decisions:  First-Stage Decisions Decided before the disaster strikes Take into account all possible scenarios of disaster Examples: Expansion of warehouses, medical facilities, ramp space Addition of workers, transportation means Second-Stage Decisions:  Second-Stage Decisions Decided after the disaster strikes Based on which scenario occurred Optimize expected number of rescued survivors given first-stage decisions Using the Model:  20 Using the Model Place = National Capital Region Two hypothetical scenarios: Category IV hurricane direct strike Terrorist attack – 1 kT nuclear explosion near Union Station in Washington, D.C. Data derived from best available sources Approximates EMA planning factors DTRA modeling Transportation Means:  21 Transportation Means Military cargo aircraft CH-53 Sea Stallion helicopter MV-22 Osprey VSTOL aircraft C-17 C-130J Commercial cargo aircraft B747 (also pax capable) DC-10 A300 MD-11 Transportation Means (cont.):  22 Transportation Means (cont.) Ground transportation vehicles Tractor-trailer Box van Passenger bus Transportation Means Data:  23 Transportation Means Data Variable Expansion Cost Replacement cost vs. ACMI Load Capacity Workers Survivors Cargo Number available Maximum expansion Hours available Operating range (hours) Commodities:  24 Commodities Water Food (MREs) Shelter Electric generators Medical supplies Cots Blankets Tarps Clothing Building supplies Commodities Calculations:  25 Commodities Calculations Commodity Requirements :  26 Commodity Requirements Affected Areas:  27 Affected Areas Affected Area Data:  28 Affected Area Data Initial ramp capacity (ft3 x 1000) Reflects useful commodity staging space 2-D raw data converted to 3-D (volume) by assuming entire space is filled with LD3 container units, one deep Maximum ramp expansion (ft3 x 1000) Expansion cost ($ per ft3 x 1000) Relief Locations:  29 Relief Locations Largest air/land commercial cargo facilities in the same general region as Washington D.C. All have large ground freight facilities Memphis (TN) Int’l Airport (IAP) FedEx national air/land hub Louisville (KY) IAP UPS national air/land hub Indianapolis (IN) IAP USPS national airborne freight hub Smaller FedEx hub Philadelphia (PA) IAP UPS regional air/land hub Relief Location Data:  30 Relief Location Data Initial warehouse capacity (ft3 x 1000) Does not include ramp space Assumes only 20% of capacity is available for disaster relief 2-D raw data converted to 3-D (volume) by assuming LD3s stacked three deep Maximum expansion (ft3 x 1000) Where available, company projections used Expansion cost ($ per ft3 x 1000) Assumes 50% of cost government subsidized Major Assumptions:  31 Major Assumptions 72-hour period of examination All displaced persons need commodities, in equal amounts Commodity requirements are aggregated for the entire period All survivors have equal evacuation priority Each health care worker is able to care for five survivors All transportation means are immediately available for use All available cargo space is used on each trip Test Cases:  32 Test Cases WMD – 1 kT nuclear explosion Scenario 1 (ω1) – Winds NE at 15 knots Scenario 2 (ω2) – Winds W at 15 knots Category IV hurricane Scenario 1 (ω1) – Widespread flooding Scenario 2 (ω2) – Minimal flooding WMD Plume Analysis:  33 WMD Plume Analysis ω2 winds ω 1 winds RFK Stadium WMD – Potential Survivors and Commodity Requirements:  34 WMD – Potential Survivors and Commodity Requirements Hurricane Storm Surge Forecast:  35 Hurricane Storm Surge Forecast Cat IV Hurricane – Potential Survivors and Commodity Requirements:  36 Cat IV Hurricane – Potential Survivors and Commodity Requirements Methodology:  37 Methodology Model run through a budget range of $8M to $200M Stochastic solution only; no deterministic runs made All input made through data files Standard data: Probabilities: p(ω1)= 0.25; p(ω2)= 0.75 Penalty = 10 survivors per 1000 ft3 of unmet commodity demand # survivors a health care worker can treat = 5 General Findings:  38 General Findings For “better case” (ω2) scenarios: All commodities delivered Almost all survivors evacuated Modest expenditures, always < budget For “worst case” (ω1) scenarios: All commodities delivered Persistent core of unrescued survivors for budgets from $8M – $50M Expenditures close to full budget amount General Findings (cont.):  39 General Findings (cont.) No ramp space expansion Warehouse space expansion only in mass flooding scenario Preference for land transportation Exception: B747 outfitted for passengers Expended $ Per Rescued Survivor:  40 Expended $ Per Rescued Survivor Conclusions:  41 Conclusions Strategic planning is at least as important as tactical operations The model is a valuable strategic tool Allows “what if” analysis of many scenarios Can reveal trends and persistent shortfalls Outputs can inform strategic decision-making Private sector participation is essential It’s not all about money Way Forward:  42 Way Forward Improve the model: Add ability to test alternative objective functions Extend observation period beyond 72 hours Add GUI for intuitive output displays Increase level of detail Operationally validate Integrate with other products (e.g. HAZUS-MH) to build a complete operational picture Establish user groups and COPs Partner with commercial cargo carriers Fund continued research into tech solutions

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