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Calculating Humanitarian Response Capacity Authors: Kathryn K. - - PowerPoint PPT Presentation

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Calculating Humanitarian Response Capacity Authors: Kathryn K. Nishimura and Jian Wang Advisors: Jarrod Goentzel and Jason Acimovic Sponsor: MIT Humanitarian Response Lab MIT SCM ResearchFest May 22-23, 2013 Disaster Victims from 2003 to 2012

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SLIDE 1

Calculating Humanitarian Response Capacity

Authors: Kathryn K. Nishimura and Jian Wang Advisors: Jarrod Goentzel and Jason Acimovic Sponsor: MIT Humanitarian Response Lab MIT SCM ResearchFest May 22-23, 2013

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SLIDE 2

Disaster Victims from 2003 to 2012

May 22-23, 2013 MIT SCM ResearchFest 2

  • 50

100 150 200 250 300 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 TAP (in millions) Total Affected Population (TAP)

website: www.emdat.be

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SLIDE 3

UNHRD Network

  • 5 United Nations Humanitarian Response Depots (UNHRDs)
  • Over 1,000 SKUs
  • Over 5 million emergency relief items
  • Stockpiles owned by over 50 humanitarian organizations

May 22-23, 2013 MIT SCM ResearchFest 3

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SLIDE 4

Disaster Response

  • Phases

May 22-23, 2013 MIT SCM ResearchFest

Preparedness Initial Response Restoration Rebuilding

4

  • Deployment of humanitarian relief items to TAPs
  • Food and cooking supplies
  • Water and water purification equipment
  • Sanitation equipment
  • Shelter and blankets
  • Medical supplies
  • Etc.
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SLIDE 5

Research Objectives

May 22-23, 2013 MIT SCM ResearchFest 5

  • Build a model to minimize response times
  • Prescribe where items should be placed
  • Evaluate the inventory capacity to respond to a typical disaster

(Photos: wfp.org)

UNHRD stockpiles can serve what expected percentage of people affected by a large disaster? What is the expected time to deploy a certain type of relief item?

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SLIDE 6

Agenda

  • Background on humanitarian response
  • Research objectives
  • Development of LP model and datasets
  • Results
  • Insights
  • Q&A

May 22-23, 2013 MIT SCM ResearchFest 6

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SLIDE 7

Stochastic LP Model

  • Objective
  • Minimize the average expected delivery time to deploy emergency

relief items to meet a disaster’s initial needs

min

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May 22-23, 2013 MIT SCM ResearchFest 7

xij: inventory to be deployed from depot, i, to disaster site, j cij: delivery time between depot, i, and disaster site, j pk: probability of disaster scenario, k, occurring

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SLIDE 8

Dataset #1: Demand

  • Filtered disaster records from 2008 to 2012
  • Includes natural and manmade disasters
  • Does not include epidemics, industrial accidents
  • Size
  • 852 demand scenarios

– TAP and location

May 22-23, 2013 MIT SCM ResearchFest 8 website: www.emdat.be

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SLIDE 9

Dataset #2: Delivery Times

  • Classified disasters into EM-DAT’s 23 disaster sub-regions
  • Measured 115 (5x23) β€œas-the-crow-flies” arcs
  • Converted distances to times, assuming 500 mph
  • Example: Accra to the Caribbean

May 22-23, 2013 MIT SCM ResearchFest 9

5,225 miles =10.4 hours

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SLIDE 10

Dataset #3: Supply

May 22-23, 2013 MIT SCM ResearchFest 10

Item

  • Blankets
  • Buckets
  • Jerry cans
  • Kitchen sets
  • Latrine plates
  • Mosquito nets
  • Soap bars

website: www.hrdlab.eu

Example: Blanket Conversion (2 per family of 5)

  • 164,624 units in inventory
  • 411,560 units of capita inventory (CI)
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SLIDE 11

Applying the Model

  • Understand how CI affects the optimal pre-positioning of

inventory

  • Keep demand scenarios and delivery times constant
  • Change initial CI from one to over 25 million units
  • See which depots the model uses and assess why
  • Measure the expected response capacity of the 7 selected

UNHRD items

  • Percentage of people the item serves
  • Time to deliver the items

May 22-23, 2013 MIT SCM ResearchFest 11

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SLIDE 12

CI and Optimal Distribution

May 22-23, 2013 MIT SCM ResearchFest 12

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Pre-Positioned Levels (CI units) Capita Inventory Dubai, UAE Brindisi, Italy Subang, Malaysia Panama City, Panama Accra, Ghana

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SLIDE 13

CI and Response Capacity

May 22-23, 2013 MIT SCM ResearchFest 13

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 2 3 4 5 6 7 8 9 10 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 Expected People Served (%) Time to Respond (Hours) Capita Inventory Average Time To Respond People Served

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SLIDE 14

Example: Response Capacity of Blankets

May 22-23, 2013 MIT SCM ResearchFest 14

Item Capita Inv0 Expected People Served (%) Expected Delivery Time (Hours) Re- Allocation (%) Status Quo Optimal Blankets 411,561 41% 8.9 7.2 23%

50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 Status Quo Optimal Capita Inventory (CI) Dubai, UAE Brindisi, Italy Subang, Malaysia Panama City, Panama Accra, Ghana

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SLIDE 15

Stockpile Evaluation

May 22-23, 2013 MIT SCM ResearchFest 15

Item Capita Inv0 Expected People Served (%) Expected Delivery Time (Hours) Re- Allocation (%) Status Quo Optimal Blankets 411,561 41% 8.9 7.2 23% Buckets 196,570 27% 9.1 7.8 17% Jerry Cans 391,389 40% 8.4 7.2 16% Kitchen Sets 125,030 21% 9.2 8.0 15% Latrine Plates 297,000 35% 9.1 7.5 22% Mosquito Nets 269,863 33% 8.3 7.5 10% Soap Bars 25,240 7% 9.3 8.6 8%

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SLIDE 16

Insights

  • Depot importance
  • Optimal number
  • Order of usage
  • Ideal response capacity
  • Diminishing marginal returns at different CI levels
  • Inventory capacity
  • 8-23% potential time savings

May 22-23, 2013 MIT SCM ResearchFest 16

β€œWe must, and we can, do better to be more predictable in our response to vulnerable populations around the globe1.”

  • 1. Humanitarian Response Review, 2005
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SLIDE 17

Calculating Humanitarian Response Capacity

Authors: Kathryn K. Nishimura and Jian Wang Advisors: Jarrod Goentzel and Jason Acimovic Sponsor: MIT Humanitarian Response Lab MIT SCM ResearchFest May 22-23, 2013