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Bridging the Gap in Humanitarian 
Operations Through Effective Partnerships 
Dr. Paulo Gonçalves 
Associate Professor – Università della Svizzera italiana (USI), Lugano 
Founder & Director – Master Humanitarian Logistics & Management 
– Master Humanitarian Operations & SC Management 
– Humanitarian Operations Research Center 
Research Affiliate – MIT Sloan School of Management 
Nairobi, September 17, 2014
1 
Three Partnership Stories & Lessons Learned 
MASHLM & World Food Program (WFP) 2010 
 Optimizing Distribution of WfP’s Food Aid in Ethiopia 
MASHLM & United Children’s Fund (UNICEF) 2013/2014 
 Supply Chain Optimization of the distribution of mosquito 
nets in Ivory Coast in 2014 
MASHOM & International Organization for Migration (IOM) 2013 
 MASHOM-IOM educational & capacity building partnership 
Different engagement & partnership models with different outcomes !
2 
Optimizing Distribution of World Food 
Program’s Food Aid in Ethiopia 
Bervery Chawaguta 
Logistics Officer 
WFP Ethiopia 
bervery.chawaguta@wfp.org 
Paulo Gonçalves 
Associate Professor 
University of Lugano, Switzerland 
paulo.goncalves@usi.ch 
WFP Ethiopia 
Addis Ababa 
November 3, 2010
3 
Introduction 
Transportation: 
• Major cost component of humanitarian operations 
• Opportunity to increase cost-effectiveness 
• Opportunity to improve HO’s effectiveness 
Challenge: 
• Lack of systematic and reliable field data prevent 
organizations from optimizing distribution 
• Most existing optimization models applied to synthetic 
data
4 
WFP Distribution on Somali Region 
WFP Ethiopia distributed 970,000 metric tones of 
food aid in 2009 
• Transportation cost: US $65 million 
Primary transportation (from ports to hubs): 
• Cost: US$ 48 million 
Secondary transportation (from hubs to final 
destinations) 
• Cost : US$ 17 million 
Distribution context: 
• 3 Ports , 20 Hubs, 80 FDPs
15,000 45,000 130,000 
5 
Simplified WFP Distribution Example 
Ports: 
• Djibouti (78%) 
• Berbera (13%) 
Hubs: 
• Dire Dawa (29%) 
• Jijiga (18%) 
• Nazareth (15%) 
FDPs: 
• Nazareth (19%) 
• Kombolcha (16%) 
• Jijiga (9%) 
• Dire Dawa (9%) 
• Mekele (7%) 
• Woreta (7%) 
370,000 90,000 
P1 P2 
H3 H4 H5 
F6 F7 F8 
140,000 60,000 70,000
6 
WFP Distribution Quantities 
Dire 
Dawa 
Jijiga Nazareth Kombol cha Mekele Woreta Supply 
P1 Djibuti 40,000 15,000 130,000 120,000 55,000 10,000 370,000 
P2 Berbera 25,000 45,000 20,000 90,000 
H3 Dire Dawa 10,000 15,000 15,000 5,000 15,000 60,000 
H4 Jijiga SuDpepmlaynd 
5,000 25,000 30,000 
H5 Nazareth 10,000 5,000 5,000 20,000 40,000 
F6 Kombolcha – – – – – – – 
F7 Mekele – – – – – – – 
F8 Woreta – – – – – – – 
Demand 75,000 75,000 170,000 140,000 60,000 70,000 – 
Note: Disguised quantities (supply and demand) to protect WFP’s confidentiality.
7 
WFP Distribution: Rates, Supply & Demand 
Dire 
Dawa Jijiga Nazareth Kombol 
cha Mekele Woreta Supply 
P1 Djibuti 40 70 50 120 130 150 370,000 
P2 Berbera 52 50 83 80 180 200 90,000 
H3 Dire Dawa – Cost 40 45 (US$/40 MT) 
80 100 60,000 
H4 Jijiga 20 – 35 40 70 35 30,000 
H5 Nazareth 20 30 – 50 60 30 40,000 
F6 Kombolcha – – – – – – – 
F7 Mekele – – – – – – – 
F8 Woreta – – – – – – – 
Demand 75,000 75,000 170,000 140,000 60,000 70,000 – 
Note: Disguised rates to protect WFP’s confidentiality.
8 
Simplified WFP Distribution Cost 
Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply 
P1 Djibuti 1600000 1050000 6500000 14400000 7150000 1500000 32,200,000 
P2 Berbera 1300000 2250000 1660000 – – – 5,210,000 
H3 Dire Dawa – 400000 675000 600000 400000 1500000 3,575,000 
H4 Jijiga – – 175000 – – 875000 1,050,000 
H5 Nazareth 200000 150000 – 250000 – 600000 1,200,000 
F6 Kombolcha – – – – – – – 
F7 Mekele – – – – – – – 
F8 Woreta – – – – – – – 
Demand 3,100,000 3,850,000 9,010,000 15,250,000 7,550,000 4,475,000 43,235,000 
Note: Disguised costs (from disguised rates and quantities) but useful as a reference to 
optimal values.
9 
WFP Distribution: How much to ship? 
Dire 
Dawa Jijiga Nazareth Kombol 
cha Mekele Woreta Supply 
H3 H4 H5 F6 F7 F8 
P1 Djibuti X13 X14 X15 X16 X17 X18 370,000 
P2 Berbera X23 X24 X25 X26 X27 X28 90,000 
H3 Dire Dawa – X34 X35 X36 X37 X38 60,000 
H4 Jijiga X43 – X45 X46 X47 X48 30,000 
H5 Nazareth X53 X54 – X56 X57 X58 40,000 
F6 Kombolcha – – – – – – – 
F7 Mekele – – – – – – – 
F8 Woreta – – – – – – – 
Demand 75,000 75,000 170,000 140,000 60,000 70,000 – 
Note: Optimal quantities shipped are not know a priori, but can be solved using linear 
programing.
10 
WFP Transshipment Formulation 
• Decision Variables: 
– Xij = Quantity shipped in arc ij, from node i to node j. 
• Objective: 
– Minimize total transportation costs 
• Subject to balance of flow constraints: 
– X13 + X14 + X15 + X16 + X17 + X18 = 370 
P1 
– X23 + X24 + X25 + X26 + X27 + X28 = 88 
P2 
– (X13 + X23 + X43 + X53) – (X34 + X35 + X36 + X37 + X38 ) = 15 
H3 
– (X14 + X24 + X34 + X54) – (X43 + X45 + X46 + X47 + X48 ) = 45 
H4
11 
WFP Distribution: How much to ship? 
Dire 
Dawa Jijiga Nazareth Kombol 
cha Mekele Woreta Supply 
H3 H4 H5 F6 F7 F8 
P1 Djibuti X13 X14 X15 X16 X17 X18 370,000 
P2 Berbera X23 X24 X25 X26 X27 X28 90,000 
H3 Dire Dawa – X34 X35 X36 X37 X38 60,000 
H4 Jijiga X43 – X45 X46 X47 X48 30,000 
H5 Nazareth X53 X54 – X56 X57 X58 40,000 
F6 Kombolcha – – – – – – – 
F7 Mekele – – – – – – – 
F8 Woreta – – – – – – – 
Demand 75,000 75,000 170,000 140,000 60,000 70,000 – 
Note: Optimal quantities shipped are not know a priori, but can be solved using linear 
programing.
12 
Optimal Food Aid Distribution 
Potential Cost Savings 
10.3 
4.1 
Costs (Million US$) 
65 
60 
55 
50 
0 
50.5 
65.0 
-22% 
2009 Actual Old Routes New Routes
NAZARETH (2206) 
Quantities (MT) 
13 
Transport Cost Savings 
Average Rate (US$/MT) 
240 
220 
200 
180 
160 
140 
120 
100 
80 
60 
40 
20 
0 
DIRE DAWA (4384) 
GODE (3156) 
MOYALE (8) 
BARE (9) 
BOH (24) 
GELADIN (13) 
HARSHIN (43) 
KEBRIDEHAR (2116) 
KEBRIBEYAH (306) 
MEKELE (130) 
ADDIS ABABA (150) 
AWASSA (159) 
JIJIGA (1763) 
0 50,000 100,000 150,000 200,000
Quantities (MT) 
14 
Transport Cost Increases 
Average Rate ($/MT) 
240 
220 
200 
180 
160 
140 
120 
100 
80 
60 
40 
20 
0 
DEGEHABOR (689) 
SHASHEMENE (55) 
SHINILE (3208) 
AWBERE (34) 
DIHUN (1) 
WARDER (2) 
SEGEG (16) 
DANOT (2) 
GERBO (21) 
DEBEWEIN (74) 
SHILABO (68) 
SHEKOSH (28) 
MOJO (77) 
HOSSANA (84) 
GURSUM (3) 
KOMBOLCHA (203) 
0 50,000 100,000 150,000 200,000
15 
Conclusions 
Transshipment optimization model can lead to significant 
cost savings: 
• Potential savings : 
– Existing routes: US $10.3 Mi 85,000 Mt 
– New routes: US $14.4 Mi 100,000 Mt 
• Clearly identified areas for improvement 
Significant commitment: 
• Shift from short- to long-term operations 
• Planning critical for success 
• Investment in new tools required 
• Systematic collection and analysis of data required
16 
MASHLM-WFP Partnership – Failure Factors 
Lack of Senior Manager Support 
 Senior managers interested in optimization tool and savings, 
but marginally involved in the process. 
Short-term Perspective 
 Focus remained on short-term operations. No shift in focus or 
allocation of resources. 
Real, But Not Practical Application 
 Focus on one year planning tool inadequate! WE tried to 
move into a shorter time horizon to influence current 
decisions, but no human resources were available.
17 
SUPPLY CHAIN OPTIMIZATION OF THE 
DISTRIBUTION OF MOSQUITO NETS IN IVORY COAST 
Irineu de Brito Junior 
USP 
(ibritojr@yahoo.com.br) 
Silvia Uneddu 
UNICEF 
(suneddu@unicef.org) 
Paulo Gonçalves 
USI 
(paulo.goncalves@usi.ch)
18 
Malaria in Ivory Coast 
• Malaria is still endemic in CIV and is a priority of the National 
Health Development Plan 2012-2015 
• Malaria is the leading cause of morbidity and mortality 
 43% of cases seen in health facilities 
 24% of hospital cases 
 26% of hospital deaths 
• From 2006 to 2008, utilization of LLINs has increased going 
from 3% to 14,8%, but only 26% of children under 5 received 
an appropriate malaria treatment. 
• To achieve and maintain universal coverage UNICEF planned 
a mass LLIN distribution campaign so that at least 80% of the 
population sleeps under the LLINs by 2015.
19 
Modeling Tasks 
• Planning for the distribution of 12 million LLINs scheduled to 
take place in CIV in 2014. 
• Adopt quantitative project management tools to identify 
critical tasks and risk exposure (CPM, risk management). 
• Develop a linear programming model (transhipment) to 
identify constrains and possible bottlenecks 
• Review concept of operations to propose the most cost 
effective and efficient solution.
20 
Project overview 
San 
Pedro 
Ferkessedoug 
ou 
Bouak 
e 
Abijdan 
Yamoussoukr 
o
21 
SUPPLIERS AND PORTS LOCATION
22 
Health District
23 
Supplier 
(i) 
Supplier I 
Origin Port 
(j) 
CIV Port (k) 
Supplier II 
Supplier III 
Supplier V 
Haiphong 
Ho Chi Minh 
Tianjin 
Chennai 
Districts 
(d) 
District 1 
District 2 
District 3 
District 4 
. . . . . . 
District d 
Containers (c) 
Products 
(p) 
p1 
p2 
p3 
p5 
Incoterm: 
CIP 
Abidjan 
Status Quo 1 - July 2013 
p4 Supplier IV 
Unstuffing
24 
Supplier 
(i) 
Supplier I 
Origin Port 
(j) 
CIV Port / Hub 
(k) 
Supplier II 
Supplier III 
Supplier IV 
Haiphong 
Ho Chi Minh 
Shanghai 
Qingdao 
Chennai 
Abidjan 
Ferkessedougo 
u 
Districts 
(d) 
District 1 
District 2 
District 3 
District 4 
District d 
Tianjin 
. . . . . . 
Containers (c) 
Products 
(p) 
p1 
p2 
p3 
p4 
Bangkok 
Yamoussoukro 
Bouake 
San Pedro 
Incoterm: 
CIP 
Abidjan 
San Pedro 
Status Quo 3 – October -2013 - Optimization model
minTC cs TS co TO cp TP 
25 
The Model 
Objective Function: 
      
   
      
  
 
nq pr TS 
   
  
  
p c j k 
cj pcjk 
p c i j 
pc p pcij 
p c k d 
pckd pckd 
p c j k 
pcjk pcjk 
p c i j 
pcij pcij 
cc TO 
1. Transportation costs from suppliers to ports of origin 
2. Transportation costs from ports of origin to CIV ports/hubs 
3. Transportation costs from CIV ports/hubs to the 71 Health 
Districts. 
4. Purchasing costs of the LLINs 
5. Purchasing costs of the containers
26 
The Model 
Constraints: 
flow conservation 
  
TS TO 
pcij k 
  
pcjk 
i 
d 
pckd 
j 
pcjk TO TP 
pckd (TP nq )  dm demand is satisfied 
pcij pc (TS nq )  sc supplier production capacity 
integer and binary variables 
pi 
c j 
d 
p c 
pc 
k 
pcij pij pcij TS as  TS 
pcjk pjk pcjk TO  ao  TO 
pckd pkd pckd TP  ap  TP 
use only available routes 
pckd    
TP Z bigM pd 
c k 
pd 
pckd TP  Z 
c k 
Z 
 1 pd p 
assures that a district is supplied 
exclusively by one product
27 
Results: 
Transport from suppliers to ports at destination 
Supplier Supplier I Supplier III Supplier IV 
Total 
Origin Port Haiphong Chennai Qingdao 
CIV Port / Hub 40ft 
40ft 
HC 
20ft 40ft 
40ft 
HC 
20ft 40ft 
40ft 
HC 
20ft 
Abidjan 
Phase 1 1 23 2 2 8 2 38 
Phase 2 4 1 24 0 1 30 
Phase 3 3 121 7 2 35 1 169 
San Pedro 
Phase 1 2 1 5 1 9 18 
Phase 2 1 68 5 5 30 1 4 41 1 156 
Phase 3 0 
Yamoussoukr 
o 
Phase 1 14 3 12 2 9 40 
Phase 2 1 1 
Phase 3 0 
Bouake 
Phase 1 16 2 1 4 0 1 5 1 30 
Phase 2 0 
Phase 3 0 
Total 5 248 14 11 77 2 12 107 6 484
28 
Results: Number of districts supplied/served by CIV 
ports and/or hubs 
Port / Hub Abidjan San Pedro Yamoussoukro Bouake 
Total Health 
Districts 
Phase 1 
9 4 9 6 28 
Phase 2 
4 20 1 25 
Phase 3 
21 21 
Total Health 
Districts 
34 24 10 6 74* 
*A district could be supplied by more than 1 hub/port
29 
Conclusions 
Initial Situation: After Optimization 
 5 suppliers based in Asia  3 suppliers based in Asia 
 Shipments from 4 ports of 
origin (depending on the 
location of the suppliers) 
 Shipments from 3 ports of 
origin (depending on the 
location of the suppliers) 
 Use over 500 40-feet 
containers, 
 Use 482 (40ft, 40ftHC, 20ft) 
containers, 
 Abidjan as the only port of 
arrival in CIV. 
 Abidjan and San Pedro as port 
of arrival and 2 inland cities as 
transshipment point. 
• Shipment and distribution of 12 million LLINs requires a strong 
coordination requirement among the stakeholders and a meticulous 
supply chain plan.
30 
MASHOM- UNICEF Partnership – Success Factors 
Senior Manager Support 
 Emergency & Logistics Coordinators worked diligently to 
explain the project management methods and opportunities. 
Captured the interest of senior managers. 
Long-term Perspective 
 Senior managers requested project become the foundation for 
a standard bednets campaign to be used in all other 
campaigns. 
Practical & Real Projects 
 Focus on practical implications and improvement of real 
challenges.
31 
MASHOM – IOM Partnership for 
Capacity Building 
Mike Pillinger 
Chief of Mission 
IOM Iraq 
mpillinger@iom.int 
Lado Gvilava 
Global Logistics Coordinator 
IOM 
lgvilava@iom.int 
Paulo Gonçalves 
Associate Professor 
University of Lugano 
paulo.goncalves@usi.ch
MAS in Humanitarian Operations & Supply Chain Mgt 
Vision Mission 
To become the premier global 
educational program to: 
• Support humanitarian organizations 
improve their performance through 
project implementation and staff 
development 
To achieve its vision the MASHLM 
program actively: 
• Offers a tailor-made program 
focused on operations and supply 
chain management (Integrated 
Management Approach) 
• Has created a flying master 
program to meet the field needs of 
humanitarian organizations 
MASHOM Humanitarian Operations & Supply Chain Management
Approach: Combines projects with institutional partnerships 
Program at a Glance Project-based approach with impact 
33 
Executive master Part-time 
Audience: Full-time humanitarians 
Duration of master 15 months 
Blocks 6 blocks (field) 
Graduation Theses defenses in Lugano 
Duration of each block 1 week 
Courses per block 2 courses/block 
+ project discussions 
Number of courses 12 tailor-made courses 
Group of 4 students work on 
real problems faced by their 
organizations 
Applied 
Projects 
Institutional 
Partnership 
Group of up to 4 students realize a 
field project with a problem of their 
organization 
Tailor-made courses designed to 
develop capacity of HOs’ field 
staff 
Strategic & 
Tactical 
Strategic & Tactical Decision 
making courses cutting across 
organizational boundaries
34 
Courses: Focuses on Strategy, Tactics & Operations 
Course Faculty Institution 
Process 
Management 
Uday Apte Naval Postgraduate School (NPS) 
Lean Six Sigma Uday Apte Naval Postgraduate School (NPS) 
Project 
Management 
Principles 
Afreen Siddiqi Massachusetts Institute of Technology 
(MIT)/Harvard Kennedy School of 
Government (Harvard KSG) 
Supply Chain 
Principles 
Olaf Janssen Kuehne Foundation 
Strategic Planning Laura Black Montana State University 
Project Strategy & 
Don Greer Greer Black Company 
Scenario Planning 
Research I: 
Analytical Thinking 
Nikolaus Beck Università della Svizzera italiana (USI) 
Research II: 
Statistics 
Nicolas Stier- 
Moses 
University of Chile/Columbia Business 
School 
Supply Chain 
Design 
Paulo 
Gonçalves 
Università della Svizzera italiana (USI)/ 
Massachusetts Institute of Technology 
(MIT Sloan) 
Decision Making 
Models 
Fernando 
Ordoñez 
University of Chile/ University of 
Southern California (USC) 
Supply Chain 
Management 
Paulo 
Gonçalves 
Università della Svizzera italiana (USI)/ 
Massachusetts Institute of Technology 
(MIT Sloan) 
Supply Chain 
Modeling 
Brad Morrison Massachusetts Institute of Technology 
(MIT Sloan)/Brandeis University 
Green Belt 
Lean Six Sigma 
Certification 
Master Advanced Studies 
(MAS) 
Humanitarian Operations & 
Supply Chain Management 
(HOM)
“There is no single module from which I 
have not been using one or more methods, 
techniques, ideas in my daily work. These 
are very applicable tools and make a big 
difference in the way I operate, 
make decisions, plan, implement, etc.” 
“I enjoyed learning every course, and I feel that I have 
gained an excellent tool set, skill set and 
mind set support me with any role in the future.” “I am a better person today in the 
35 
Student’s Feedback: High Praise for MASHOM 
“Overall on this MASHOM, the 
subjects, methods and 
tools are very useful for our 
daily work.” 
“The most important benefits of MASHOM 
courses is that teaching modules are 
tailor-made and adjusted to IOM 
operational modalities. It is a great 
opportunity to be able to utilize the 
advanced tools and methodologies learned 
in the classroom in IOM projects 
implemented in various field missions.” 
“The program is very interesting, the 
courses are very well adapted to 
the humanitarian context, and 
are nicely complementing each 
other.” 
“The teaching methods are 
interactive and very dynamic. 
The course material as well the content 
and references to literature are 
extremely interesting and useful.” 
“In general MASHOM was able 
to change my way of 
thinking , now for me 
Humanitarian assistance 
projects management differs 
from before.” 
“This program should be a 
mandatory course for any 
program manager, developer, 
implementer that work in the 
international humanitarian 
community.” 
“All courses have been very effective and I 
have applied the principles and 
techniques in a large scale emergency 
that emerged while I am in the course.” 
workplace and in life than I was 
before the MASHOM, and thanks to 
the MASHOM I am able to do 
my work exponentially 
better.”
36 
MASHOM Project Has Lead to Higher Fundraising 
MASHOM Skills Applied to 
Reduce Logistic Complexity 
Successful Project Led to Additional 
Funding 
Additional USD 1.8 million received from 
Japanese government for next project phase 
Emergency Assistance to Syrian Refugees in 
Northern Iraq allows to provide for the next 9 
months: 
• Transportation assistance 
• Provision of food and medical 
• Provision of non-food items (NFI) 
Additional USD 800’000 has been provided in 
form of material, tents etc. 
Japanese’s funding for Syrian refugees 
The Government of Japan funded IOM with USD 
300’000 to provide aid to populations (urban 
refugees) affected by the Syrian crisis in Iraq. 
Complex logistics 
This logistically very complex project was 
accomplished in record speed largely utilizing 
skills acquired at MASHOM courses and 
addressing the immediate needs of 590 vulnerable 
refugee and returnee families, reaching more than 
3’500 people in 10 cities all over Iraq.
37 
MASHOM Led to Immediate & Measurable Savings 
MASHOM Impact on Iraq Mission  US$ 800,000+ in Savings 
MASHOM Transport Optimization MASHOM Lean Six Sigma 
• Problem: 
Provide transport to Syrian refugees 
crossing from Syria to KRG Iraq? 
• Results: 
Savings US$150,000 (single operation, 
for project last three months). 
• Methods: 
Diversified procurement optimizing costs 
to specific destinations. 
• Opportunity: 
Diversification to other transport efforts 
and other missions. 
• Problem: 
How to Eliminate redundant processes? 
• Results: 
Savings US$67,000 per month (since 
February) 
Removed non-valued added components, 
reduced lead time and increase speed of 
NFI delivery 
• Methods: 
Process management and lean six sigma 
• Opportunity: 
Diversification to similar organizational 
processes
MASHOM Impact on IOM Iraq Emergency Response Anbar IDP Crisis 
38 
Increased Responsiveness: - IOM first International Organization to respond 
Mainstreamed SCM: - Rapid adjustment of services to increased and 
diverse needs of most vulnerable population 
Optimized Processes: - Highest number of services delivered among UN 
agencies 
 NFIs 8,255 kits in 5 months to 49,530 benefic. 
Increased Capacity: - Distributing aid on behalf of other UN agencies 
 WFP 15,122 food parcels to 90,732 benefic. 
 WHO 5 emergency medical kits for public 
hospitals 
IOM today has highest funding among all Anbar crisis responders
IOM Benefitted from Improved Relations w/Stakeholders 
Enhanced Flexibility & Adaptability of Operational Approaches 
39 
 Diversification of services and increased numbers of 
beneficiaries reached 
Increased Response Capacity 
 Recognition & strengthened operational and strategic 
partnerships 
Overall Impact of Mainstreamed Supply Chain Management 
 Diversified funding streams & increased number of donors
40 
MASHOM- IOM Partnership – Success Factors 
Senior Manager Support 
 Senior managers participated in the program and 
encouraged others to implement changes. 
Long-term Perspective 
 Focus on a long-term perspective removed people from only 
day-to-day concerns, allowing them to seize project 
implementation opportunities. 
Practical & Real Projects 
 Focus on practical implications and real challenges provided a 
great opportunity to implement frameworks and concepts from 
MASHOM courses.
41 
Academia- HOs Partnership Success Factors 
Senior Manager Support 
 Senior managers engaged and supportive 
 Support for disseminating impact and engaging others 
 Focus on implementing changes through senior influence 
Long-term Perspective 
 Focus on long-term improvement goals 
 Shield personnel from short-term pressures 
 Aim for setting standards and generalizable applications 
Practical & Real Projects 
 Focus on clear objectives and specific projects 
 Focus on important and pressing problems 
 Focus on possible practical implications and improvements
Thank you ! 
Paulo Gonçalves 
Associate Professor – Università della Svizzera italiana (USI), Lugano 
Founder & Director – Master Humanitarian Logistics & Management 
– Master Humanitarian Operations & SC Management 
– Humanitarian Operations Research Center 
Research Affiliate – MIT Sloan School of Management 
paulo.goncalves@usi.ch

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Bridging the Gap in Humanitarian Operations Through Effective Partnerships

  • 1. Bridging the Gap in Humanitarian Operations Through Effective Partnerships Dr. Paulo Gonçalves Associate Professor – Università della Svizzera italiana (USI), Lugano Founder & Director – Master Humanitarian Logistics & Management – Master Humanitarian Operations & SC Management – Humanitarian Operations Research Center Research Affiliate – MIT Sloan School of Management Nairobi, September 17, 2014
  • 2. 1 Three Partnership Stories & Lessons Learned MASHLM & World Food Program (WFP) 2010  Optimizing Distribution of WfP’s Food Aid in Ethiopia MASHLM & United Children’s Fund (UNICEF) 2013/2014  Supply Chain Optimization of the distribution of mosquito nets in Ivory Coast in 2014 MASHOM & International Organization for Migration (IOM) 2013  MASHOM-IOM educational & capacity building partnership Different engagement & partnership models with different outcomes !
  • 3. 2 Optimizing Distribution of World Food Program’s Food Aid in Ethiopia Bervery Chawaguta Logistics Officer WFP Ethiopia bervery.chawaguta@wfp.org Paulo Gonçalves Associate Professor University of Lugano, Switzerland paulo.goncalves@usi.ch WFP Ethiopia Addis Ababa November 3, 2010
  • 4. 3 Introduction Transportation: • Major cost component of humanitarian operations • Opportunity to increase cost-effectiveness • Opportunity to improve HO’s effectiveness Challenge: • Lack of systematic and reliable field data prevent organizations from optimizing distribution • Most existing optimization models applied to synthetic data
  • 5. 4 WFP Distribution on Somali Region WFP Ethiopia distributed 970,000 metric tones of food aid in 2009 • Transportation cost: US $65 million Primary transportation (from ports to hubs): • Cost: US$ 48 million Secondary transportation (from hubs to final destinations) • Cost : US$ 17 million Distribution context: • 3 Ports , 20 Hubs, 80 FDPs
  • 6. 15,000 45,000 130,000 5 Simplified WFP Distribution Example Ports: • Djibouti (78%) • Berbera (13%) Hubs: • Dire Dawa (29%) • Jijiga (18%) • Nazareth (15%) FDPs: • Nazareth (19%) • Kombolcha (16%) • Jijiga (9%) • Dire Dawa (9%) • Mekele (7%) • Woreta (7%) 370,000 90,000 P1 P2 H3 H4 H5 F6 F7 F8 140,000 60,000 70,000
  • 7. 6 WFP Distribution Quantities Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply P1 Djibuti 40,000 15,000 130,000 120,000 55,000 10,000 370,000 P2 Berbera 25,000 45,000 20,000 90,000 H3 Dire Dawa 10,000 15,000 15,000 5,000 15,000 60,000 H4 Jijiga SuDpepmlaynd 5,000 25,000 30,000 H5 Nazareth 10,000 5,000 5,000 20,000 40,000 F6 Kombolcha – – – – – – – F7 Mekele – – – – – – – F8 Woreta – – – – – – – Demand 75,000 75,000 170,000 140,000 60,000 70,000 – Note: Disguised quantities (supply and demand) to protect WFP’s confidentiality.
  • 8. 7 WFP Distribution: Rates, Supply & Demand Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply P1 Djibuti 40 70 50 120 130 150 370,000 P2 Berbera 52 50 83 80 180 200 90,000 H3 Dire Dawa – Cost 40 45 (US$/40 MT) 80 100 60,000 H4 Jijiga 20 – 35 40 70 35 30,000 H5 Nazareth 20 30 – 50 60 30 40,000 F6 Kombolcha – – – – – – – F7 Mekele – – – – – – – F8 Woreta – – – – – – – Demand 75,000 75,000 170,000 140,000 60,000 70,000 – Note: Disguised rates to protect WFP’s confidentiality.
  • 9. 8 Simplified WFP Distribution Cost Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply P1 Djibuti 1600000 1050000 6500000 14400000 7150000 1500000 32,200,000 P2 Berbera 1300000 2250000 1660000 – – – 5,210,000 H3 Dire Dawa – 400000 675000 600000 400000 1500000 3,575,000 H4 Jijiga – – 175000 – – 875000 1,050,000 H5 Nazareth 200000 150000 – 250000 – 600000 1,200,000 F6 Kombolcha – – – – – – – F7 Mekele – – – – – – – F8 Woreta – – – – – – – Demand 3,100,000 3,850,000 9,010,000 15,250,000 7,550,000 4,475,000 43,235,000 Note: Disguised costs (from disguised rates and quantities) but useful as a reference to optimal values.
  • 10. 9 WFP Distribution: How much to ship? Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply H3 H4 H5 F6 F7 F8 P1 Djibuti X13 X14 X15 X16 X17 X18 370,000 P2 Berbera X23 X24 X25 X26 X27 X28 90,000 H3 Dire Dawa – X34 X35 X36 X37 X38 60,000 H4 Jijiga X43 – X45 X46 X47 X48 30,000 H5 Nazareth X53 X54 – X56 X57 X58 40,000 F6 Kombolcha – – – – – – – F7 Mekele – – – – – – – F8 Woreta – – – – – – – Demand 75,000 75,000 170,000 140,000 60,000 70,000 – Note: Optimal quantities shipped are not know a priori, but can be solved using linear programing.
  • 11. 10 WFP Transshipment Formulation • Decision Variables: – Xij = Quantity shipped in arc ij, from node i to node j. • Objective: – Minimize total transportation costs • Subject to balance of flow constraints: – X13 + X14 + X15 + X16 + X17 + X18 = 370 P1 – X23 + X24 + X25 + X26 + X27 + X28 = 88 P2 – (X13 + X23 + X43 + X53) – (X34 + X35 + X36 + X37 + X38 ) = 15 H3 – (X14 + X24 + X34 + X54) – (X43 + X45 + X46 + X47 + X48 ) = 45 H4
  • 12. 11 WFP Distribution: How much to ship? Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply H3 H4 H5 F6 F7 F8 P1 Djibuti X13 X14 X15 X16 X17 X18 370,000 P2 Berbera X23 X24 X25 X26 X27 X28 90,000 H3 Dire Dawa – X34 X35 X36 X37 X38 60,000 H4 Jijiga X43 – X45 X46 X47 X48 30,000 H5 Nazareth X53 X54 – X56 X57 X58 40,000 F6 Kombolcha – – – – – – – F7 Mekele – – – – – – – F8 Woreta – – – – – – – Demand 75,000 75,000 170,000 140,000 60,000 70,000 – Note: Optimal quantities shipped are not know a priori, but can be solved using linear programing.
  • 13. 12 Optimal Food Aid Distribution Potential Cost Savings 10.3 4.1 Costs (Million US$) 65 60 55 50 0 50.5 65.0 -22% 2009 Actual Old Routes New Routes
  • 14. NAZARETH (2206) Quantities (MT) 13 Transport Cost Savings Average Rate (US$/MT) 240 220 200 180 160 140 120 100 80 60 40 20 0 DIRE DAWA (4384) GODE (3156) MOYALE (8) BARE (9) BOH (24) GELADIN (13) HARSHIN (43) KEBRIDEHAR (2116) KEBRIBEYAH (306) MEKELE (130) ADDIS ABABA (150) AWASSA (159) JIJIGA (1763) 0 50,000 100,000 150,000 200,000
  • 15. Quantities (MT) 14 Transport Cost Increases Average Rate ($/MT) 240 220 200 180 160 140 120 100 80 60 40 20 0 DEGEHABOR (689) SHASHEMENE (55) SHINILE (3208) AWBERE (34) DIHUN (1) WARDER (2) SEGEG (16) DANOT (2) GERBO (21) DEBEWEIN (74) SHILABO (68) SHEKOSH (28) MOJO (77) HOSSANA (84) GURSUM (3) KOMBOLCHA (203) 0 50,000 100,000 150,000 200,000
  • 16. 15 Conclusions Transshipment optimization model can lead to significant cost savings: • Potential savings : – Existing routes: US $10.3 Mi 85,000 Mt – New routes: US $14.4 Mi 100,000 Mt • Clearly identified areas for improvement Significant commitment: • Shift from short- to long-term operations • Planning critical for success • Investment in new tools required • Systematic collection and analysis of data required
  • 17. 16 MASHLM-WFP Partnership – Failure Factors Lack of Senior Manager Support  Senior managers interested in optimization tool and savings, but marginally involved in the process. Short-term Perspective  Focus remained on short-term operations. No shift in focus or allocation of resources. Real, But Not Practical Application  Focus on one year planning tool inadequate! WE tried to move into a shorter time horizon to influence current decisions, but no human resources were available.
  • 18. 17 SUPPLY CHAIN OPTIMIZATION OF THE DISTRIBUTION OF MOSQUITO NETS IN IVORY COAST Irineu de Brito Junior USP (ibritojr@yahoo.com.br) Silvia Uneddu UNICEF (suneddu@unicef.org) Paulo Gonçalves USI (paulo.goncalves@usi.ch)
  • 19. 18 Malaria in Ivory Coast • Malaria is still endemic in CIV and is a priority of the National Health Development Plan 2012-2015 • Malaria is the leading cause of morbidity and mortality  43% of cases seen in health facilities  24% of hospital cases  26% of hospital deaths • From 2006 to 2008, utilization of LLINs has increased going from 3% to 14,8%, but only 26% of children under 5 received an appropriate malaria treatment. • To achieve and maintain universal coverage UNICEF planned a mass LLIN distribution campaign so that at least 80% of the population sleeps under the LLINs by 2015.
  • 20. 19 Modeling Tasks • Planning for the distribution of 12 million LLINs scheduled to take place in CIV in 2014. • Adopt quantitative project management tools to identify critical tasks and risk exposure (CPM, risk management). • Develop a linear programming model (transhipment) to identify constrains and possible bottlenecks • Review concept of operations to propose the most cost effective and efficient solution.
  • 21. 20 Project overview San Pedro Ferkessedoug ou Bouak e Abijdan Yamoussoukr o
  • 22. 21 SUPPLIERS AND PORTS LOCATION
  • 24. 23 Supplier (i) Supplier I Origin Port (j) CIV Port (k) Supplier II Supplier III Supplier V Haiphong Ho Chi Minh Tianjin Chennai Districts (d) District 1 District 2 District 3 District 4 . . . . . . District d Containers (c) Products (p) p1 p2 p3 p5 Incoterm: CIP Abidjan Status Quo 1 - July 2013 p4 Supplier IV Unstuffing
  • 25. 24 Supplier (i) Supplier I Origin Port (j) CIV Port / Hub (k) Supplier II Supplier III Supplier IV Haiphong Ho Chi Minh Shanghai Qingdao Chennai Abidjan Ferkessedougo u Districts (d) District 1 District 2 District 3 District 4 District d Tianjin . . . . . . Containers (c) Products (p) p1 p2 p3 p4 Bangkok Yamoussoukro Bouake San Pedro Incoterm: CIP Abidjan San Pedro Status Quo 3 – October -2013 - Optimization model
  • 26. minTC cs TS co TO cp TP 25 The Model Objective Function:                   nq pr TS        p c j k cj pcjk p c i j pc p pcij p c k d pckd pckd p c j k pcjk pcjk p c i j pcij pcij cc TO 1. Transportation costs from suppliers to ports of origin 2. Transportation costs from ports of origin to CIV ports/hubs 3. Transportation costs from CIV ports/hubs to the 71 Health Districts. 4. Purchasing costs of the LLINs 5. Purchasing costs of the containers
  • 27. 26 The Model Constraints: flow conservation   TS TO pcij k   pcjk i d pckd j pcjk TO TP pckd (TP nq )  dm demand is satisfied pcij pc (TS nq )  sc supplier production capacity integer and binary variables pi c j d p c pc k pcij pij pcij TS as  TS pcjk pjk pcjk TO  ao  TO pckd pkd pckd TP  ap  TP use only available routes pckd    TP Z bigM pd c k pd pckd TP  Z c k Z  1 pd p assures that a district is supplied exclusively by one product
  • 28. 27 Results: Transport from suppliers to ports at destination Supplier Supplier I Supplier III Supplier IV Total Origin Port Haiphong Chennai Qingdao CIV Port / Hub 40ft 40ft HC 20ft 40ft 40ft HC 20ft 40ft 40ft HC 20ft Abidjan Phase 1 1 23 2 2 8 2 38 Phase 2 4 1 24 0 1 30 Phase 3 3 121 7 2 35 1 169 San Pedro Phase 1 2 1 5 1 9 18 Phase 2 1 68 5 5 30 1 4 41 1 156 Phase 3 0 Yamoussoukr o Phase 1 14 3 12 2 9 40 Phase 2 1 1 Phase 3 0 Bouake Phase 1 16 2 1 4 0 1 5 1 30 Phase 2 0 Phase 3 0 Total 5 248 14 11 77 2 12 107 6 484
  • 29. 28 Results: Number of districts supplied/served by CIV ports and/or hubs Port / Hub Abidjan San Pedro Yamoussoukro Bouake Total Health Districts Phase 1 9 4 9 6 28 Phase 2 4 20 1 25 Phase 3 21 21 Total Health Districts 34 24 10 6 74* *A district could be supplied by more than 1 hub/port
  • 30. 29 Conclusions Initial Situation: After Optimization  5 suppliers based in Asia  3 suppliers based in Asia  Shipments from 4 ports of origin (depending on the location of the suppliers)  Shipments from 3 ports of origin (depending on the location of the suppliers)  Use over 500 40-feet containers,  Use 482 (40ft, 40ftHC, 20ft) containers,  Abidjan as the only port of arrival in CIV.  Abidjan and San Pedro as port of arrival and 2 inland cities as transshipment point. • Shipment and distribution of 12 million LLINs requires a strong coordination requirement among the stakeholders and a meticulous supply chain plan.
  • 31. 30 MASHOM- UNICEF Partnership – Success Factors Senior Manager Support  Emergency & Logistics Coordinators worked diligently to explain the project management methods and opportunities. Captured the interest of senior managers. Long-term Perspective  Senior managers requested project become the foundation for a standard bednets campaign to be used in all other campaigns. Practical & Real Projects  Focus on practical implications and improvement of real challenges.
  • 32. 31 MASHOM – IOM Partnership for Capacity Building Mike Pillinger Chief of Mission IOM Iraq mpillinger@iom.int Lado Gvilava Global Logistics Coordinator IOM lgvilava@iom.int Paulo Gonçalves Associate Professor University of Lugano paulo.goncalves@usi.ch
  • 33. MAS in Humanitarian Operations & Supply Chain Mgt Vision Mission To become the premier global educational program to: • Support humanitarian organizations improve their performance through project implementation and staff development To achieve its vision the MASHLM program actively: • Offers a tailor-made program focused on operations and supply chain management (Integrated Management Approach) • Has created a flying master program to meet the field needs of humanitarian organizations MASHOM Humanitarian Operations & Supply Chain Management
  • 34. Approach: Combines projects with institutional partnerships Program at a Glance Project-based approach with impact 33 Executive master Part-time Audience: Full-time humanitarians Duration of master 15 months Blocks 6 blocks (field) Graduation Theses defenses in Lugano Duration of each block 1 week Courses per block 2 courses/block + project discussions Number of courses 12 tailor-made courses Group of 4 students work on real problems faced by their organizations Applied Projects Institutional Partnership Group of up to 4 students realize a field project with a problem of their organization Tailor-made courses designed to develop capacity of HOs’ field staff Strategic & Tactical Strategic & Tactical Decision making courses cutting across organizational boundaries
  • 35. 34 Courses: Focuses on Strategy, Tactics & Operations Course Faculty Institution Process Management Uday Apte Naval Postgraduate School (NPS) Lean Six Sigma Uday Apte Naval Postgraduate School (NPS) Project Management Principles Afreen Siddiqi Massachusetts Institute of Technology (MIT)/Harvard Kennedy School of Government (Harvard KSG) Supply Chain Principles Olaf Janssen Kuehne Foundation Strategic Planning Laura Black Montana State University Project Strategy & Don Greer Greer Black Company Scenario Planning Research I: Analytical Thinking Nikolaus Beck Università della Svizzera italiana (USI) Research II: Statistics Nicolas Stier- Moses University of Chile/Columbia Business School Supply Chain Design Paulo Gonçalves Università della Svizzera italiana (USI)/ Massachusetts Institute of Technology (MIT Sloan) Decision Making Models Fernando Ordoñez University of Chile/ University of Southern California (USC) Supply Chain Management Paulo Gonçalves Università della Svizzera italiana (USI)/ Massachusetts Institute of Technology (MIT Sloan) Supply Chain Modeling Brad Morrison Massachusetts Institute of Technology (MIT Sloan)/Brandeis University Green Belt Lean Six Sigma Certification Master Advanced Studies (MAS) Humanitarian Operations & Supply Chain Management (HOM)
  • 36. “There is no single module from which I have not been using one or more methods, techniques, ideas in my daily work. These are very applicable tools and make a big difference in the way I operate, make decisions, plan, implement, etc.” “I enjoyed learning every course, and I feel that I have gained an excellent tool set, skill set and mind set support me with any role in the future.” “I am a better person today in the 35 Student’s Feedback: High Praise for MASHOM “Overall on this MASHOM, the subjects, methods and tools are very useful for our daily work.” “The most important benefits of MASHOM courses is that teaching modules are tailor-made and adjusted to IOM operational modalities. It is a great opportunity to be able to utilize the advanced tools and methodologies learned in the classroom in IOM projects implemented in various field missions.” “The program is very interesting, the courses are very well adapted to the humanitarian context, and are nicely complementing each other.” “The teaching methods are interactive and very dynamic. The course material as well the content and references to literature are extremely interesting and useful.” “In general MASHOM was able to change my way of thinking , now for me Humanitarian assistance projects management differs from before.” “This program should be a mandatory course for any program manager, developer, implementer that work in the international humanitarian community.” “All courses have been very effective and I have applied the principles and techniques in a large scale emergency that emerged while I am in the course.” workplace and in life than I was before the MASHOM, and thanks to the MASHOM I am able to do my work exponentially better.”
  • 37. 36 MASHOM Project Has Lead to Higher Fundraising MASHOM Skills Applied to Reduce Logistic Complexity Successful Project Led to Additional Funding Additional USD 1.8 million received from Japanese government for next project phase Emergency Assistance to Syrian Refugees in Northern Iraq allows to provide for the next 9 months: • Transportation assistance • Provision of food and medical • Provision of non-food items (NFI) Additional USD 800’000 has been provided in form of material, tents etc. Japanese’s funding for Syrian refugees The Government of Japan funded IOM with USD 300’000 to provide aid to populations (urban refugees) affected by the Syrian crisis in Iraq. Complex logistics This logistically very complex project was accomplished in record speed largely utilizing skills acquired at MASHOM courses and addressing the immediate needs of 590 vulnerable refugee and returnee families, reaching more than 3’500 people in 10 cities all over Iraq.
  • 38. 37 MASHOM Led to Immediate & Measurable Savings MASHOM Impact on Iraq Mission  US$ 800,000+ in Savings MASHOM Transport Optimization MASHOM Lean Six Sigma • Problem: Provide transport to Syrian refugees crossing from Syria to KRG Iraq? • Results: Savings US$150,000 (single operation, for project last three months). • Methods: Diversified procurement optimizing costs to specific destinations. • Opportunity: Diversification to other transport efforts and other missions. • Problem: How to Eliminate redundant processes? • Results: Savings US$67,000 per month (since February) Removed non-valued added components, reduced lead time and increase speed of NFI delivery • Methods: Process management and lean six sigma • Opportunity: Diversification to similar organizational processes
  • 39. MASHOM Impact on IOM Iraq Emergency Response Anbar IDP Crisis 38 Increased Responsiveness: - IOM first International Organization to respond Mainstreamed SCM: - Rapid adjustment of services to increased and diverse needs of most vulnerable population Optimized Processes: - Highest number of services delivered among UN agencies  NFIs 8,255 kits in 5 months to 49,530 benefic. Increased Capacity: - Distributing aid on behalf of other UN agencies  WFP 15,122 food parcels to 90,732 benefic.  WHO 5 emergency medical kits for public hospitals IOM today has highest funding among all Anbar crisis responders
  • 40. IOM Benefitted from Improved Relations w/Stakeholders Enhanced Flexibility & Adaptability of Operational Approaches 39  Diversification of services and increased numbers of beneficiaries reached Increased Response Capacity  Recognition & strengthened operational and strategic partnerships Overall Impact of Mainstreamed Supply Chain Management  Diversified funding streams & increased number of donors
  • 41. 40 MASHOM- IOM Partnership – Success Factors Senior Manager Support  Senior managers participated in the program and encouraged others to implement changes. Long-term Perspective  Focus on a long-term perspective removed people from only day-to-day concerns, allowing them to seize project implementation opportunities. Practical & Real Projects  Focus on practical implications and real challenges provided a great opportunity to implement frameworks and concepts from MASHOM courses.
  • 42. 41 Academia- HOs Partnership Success Factors Senior Manager Support  Senior managers engaged and supportive  Support for disseminating impact and engaging others  Focus on implementing changes through senior influence Long-term Perspective  Focus on long-term improvement goals  Shield personnel from short-term pressures  Aim for setting standards and generalizable applications Practical & Real Projects  Focus on clear objectives and specific projects  Focus on important and pressing problems  Focus on possible practical implications and improvements
  • 43. Thank you ! Paulo Gonçalves Associate Professor – Università della Svizzera italiana (USI), Lugano Founder & Director – Master Humanitarian Logistics & Management – Master Humanitarian Operations & SC Management – Humanitarian Operations Research Center Research Affiliate – MIT Sloan School of Management paulo.goncalves@usi.ch