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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
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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 !
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Optimizing Distribution of World Food Program’s Food Aid in Ethiopia
Bervery Chawaguta Logistics Officer WFP Ethiopia
Paulo Gonçalves Associate Professor
University of Lugano, Switzerland [email protected]
WFP Ethiopia Addis Ababa
November 3, 2010
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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
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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%)
P1 P2
H3 H4 H5
F6 F7 F8
370,000 90,000
140,000 70,000 60,000
15,000 130,000 45,000
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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 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 –
WFP Distribution Quantities
Note: Disguised quantities (supply and demand) to protect WFP’s confidentiality.
Supply Demand
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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 – 40 45 40 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.
Cost (US$/MT)
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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.
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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.
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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 – (X15 + X25 + X35 + X45) – (X53 + X56 + X57 + X58 ) = 130
H5 – X16 + X26 + X36 + X46 + X56 = 140
F6 ...
– Xij ≥ 0, and Xij ≤ max cap for all ij.
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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
New RoutesOld Routes2009 Actual
65.0
-22%
13
Transport Cost Savings
0
20
40
60
80
100
120
140
160
180
200
220
240
0 50,000 100,000 150,000 200,000
DIRE DAWA (4384)
Average Rate (US$/MT)
Quantities (MT)
MOYALE (8)
BARE (9)
GELADIN (13)
BOH (24)
HARSHIN (43)
MEKELE (130)
ADDIS ABABA (150)
AWASSA (159)
KEBRIBEYAH (306)
JIJIGA (1763)
KEBRIDEHAR (2116)
NAZARETH (2206)
GODE (3156)
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Transport Cost Increases
0
20
40
60
80
100
120
140
160
180
200
220
240
0 50,000 100,000 150,000 200,000
Quantities (MT)
Average Rate ($/MT)
AWBERE (34)
SHEKOSH (28)
GERBO (21)
SEGEG (16)
GURSUM (3)
WARDER (2)
DANOT (2)
DIHUN (1)
SHINILE (3208)
DEGEHABOR (689)
KOMBOLCHA (203)
HOSSANA (84)
MOJO (77)
DEBEWEIN (74)
SHILABO (68)
SHASHEMENE (55)
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.
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Irineu de Brito Junior USP
Silvia Uneddu UNICEF
Paulo Gonçalves USI
SUPPLY CHAIN OPTIMIZATION OF THE DISTRIBUTION OF MOSQUITO NETS IN IVORY COAST
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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.
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Project overview
San Pedro
Ferkessedougou
Bouake
Abijdan
Yamoussoukro
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SUPPLIERS AND PORTS LOCATION
22
Health District
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Supplier I
Supplier (i)
Origin Port (j)
CIV Port (k)
Supplier II
Supplier III
Supplier V
Haiphong
Ho Chi Minh
Chennai
Tianjin
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
Supplier IV p4
Unstuffing
24
Supplier I
Supplier (i)
Origin Port (j)
CIV Port / Hub (k)
Supplier II
Supplier III
Supplier IV
Haiphong
Ho Chi Minh
Chennai
Qingdao
Shanghai
Abidjan
Ferkessedougou
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
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The Model
Objective Function:
p c j kpcjkcj
p c i jpcijppc
p c k dpckdpckd
p c j kpcjkpcjk
p c i jpcijpcij
TOcc
TSprnq
TPcpTOcoTScsTCmin
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
kpcjk
ipcij TOTS
d
pckdj
pcjk TPTO
integer and binary variables
pic j
pcpcij scnqTS )( supplier production capacity
dp c
pck
pckd dmnqTP )( demand is satisfied
pcijpijpcij TSasTS
pcjkpjkpcjk TOaoTO
pckdpkdpckd TPapTP
use only available routes
bigMZTP pdc k
pckd
pdc k
pckd ZTP
1p
pdZ
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
• Shipment and distribution of 12 million LLINs requires a strong coordination requirement among the stakeholders and a meticulous supply chain plan.
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.
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 [email protected]
Lado Gvilava Global Logistics Coordinator
Paulo Gonçalves Associate Professor University of Lugano
MAS in Humanitarian Operations & Supply Chain Mgt
MASHOM Humanitarian Operations & Supply Chain Management
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
Vision Mission
33
Approach: Combines projects with institutional partnerships
Program at a Glance Project-based approach with impact
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 & Scenario Planning
Don Greer Greer Black Company
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)
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 teaching methods are interactive and very dynamic. The course material as well the content and references to literature are extremely interesting and useful.”
“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.”
“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.”
“The program is very interesting, the courses are very well adapted to the humanitarian context, and are nicely complementing each other.”
“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
workplace and in life than I was before the MASHOM, and thanks to the MASHOM I am able to do my work exponentially better.”
“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.”
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 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$67,000 per month (since February) Removed non-valued added components, reduced lead time and increase speed of NFI delivery
• Methods: Process management and lean six sigma
• Opportunity: Diversification to similar organizational processes
MASHOM Impact on Iraq Mission US$ 800,000+ in Savings
38
MASHOM Impact on IOM Iraq Emergency Response Anbar IDP Crisis
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
39
IOM Benefitted from Improved Relations w/Stakeholders
Enhanced Flexibility & Adaptability of Operational Approaches 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
