Calculating Humanitarian Response Capacity

Transcription

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

2 TAP (in millions) Disaster Victims from 2003 to Total Affected Population (TAP) website: May 22-23, 2013 MIT SCM ResearchFest 2

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

4 Disaster Response Phases Preparedness Initial Response Restoration Rebuilding Deployment of humanitarian relief items to TAPs Food and cooking supplies Water and water purification equipment Sanitation equipment Shelter and blankets Medical supplies Etc. May 22-23, 2013 MIT SCM ResearchFest 4

5 Research Objectives Build a model to minimize response times Prescribe where items should be placed Evaluate the inventory capacity to respond to a typical disaster 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? (Photos: wfp.org) May 22-23, 2013 MIT SCM ResearchFest 5

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

7 Stochastic LP Model Objective Minimize the average expected delivery time to deploy emergency relief items to meet a disaster s initial needs min x k p k i,j x ij k c ij x ij : inventory to be deployed from depot, i, to disaster site, j c ij : delivery time between depot, i, and disaster site, j p k : probability of disaster scenario, k, occurring May 22-23, 2013 MIT SCM ResearchFest 7

8 Dataset #1: Demand website: 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

9 Dataset #2: Delivery Times Classified disasters into EM-DAT s 23 disaster sub-regions Measured 115 (5x23) as-the-crow-flies arcs Converted distances to times, assuming 500 mph Example: Accra to the Caribbean 5,225 miles =10.4 hours May 22-23, 2013 MIT SCM ResearchFest 9

10 Dataset #3: Supply Item Blankets Buckets Jerry cans Kitchen sets Latrine plates Mosquito nets Soap bars Example: Blanket Conversion (2 per family of 5) 164,624 units in inventory 411,560 units of capita inventory (CI) website: May 22-23, 2013 MIT SCM ResearchFest 10

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

12 Pre-Positioned Levels (CI units) CI and Optimal Distribution 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Capita Inventory Dubai, UAE Brindisi, Italy Subang, Malaysia Panama City, Panama Accra, Ghana May 22-23, 2013 MIT SCM ResearchFest 12

13 Time to Respond (Hours) CI and Response Capacity % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% ,000 10, ,000 1,000,000 10,000,000 Capita Inventory Expected People Served (%) Average Time To Respond People Served May 22-23, 2013 MIT SCM ResearchFest 13

14 Capita Inventory (CI) Example: Response Capacity of Blankets Item Capita Inv 0 Expected People Served (%) Expected Delivery Time (Hours) Status Quo Optimal Re- Allocation (%) Blankets 411,561 41% % 450, , , , , , , ,000 50,000 0 Status Quo Optimal Dubai, UAE Brindisi, Italy Subang, Malaysia Panama City, Panama Accra, Ghana May 22-23, 2013 MIT SCM ResearchFest 14

15 Stockpile Evaluation Item Capita Inv 0 Expected People Served (%) Expected Delivery Time (Hours) Status Quo Optimal Re- Allocation (%) Blankets 411,561 41% % Buckets 196,570 27% % Jerry Cans 391,389 40% % Kitchen Sets 125,030 21% % Latrine Plates 297,000 35% % Mosquito Nets 269,863 33% % Soap Bars 25,240 7% % May 22-23, 2013 MIT SCM ResearchFest 15

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 We must, and we can, do better to be more predictable in our response to vulnerable populations around the globe Humanitarian Response Review, 2005 May 22-23, 2013 MIT SCM ResearchFest 16

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