Bridging the Gap in Humanitarian Operations Through Effective Partnerships

Dr. Paulo Gonçalves

Associate Professor – Università della Svizzera italiana (USI), LuganoFounder & Director – Master Humanitarian Logistics & Management – Master Humanitarian Operations & SC Management – Humanitarian Operations Research CenterResearch Affiliate – MIT Sloan School of Management

Nairobi, September 17, 2014

2

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

Optimizing Distribution of World Food Program’s Food Aid in Ethiopia

Bervery ChawagutaLogistics OfficerWFP Ethiopia

bervery.chawaguta@wfp.org

Paulo GonçalvesAssociate Professor

University of Lugano, Switzerlandpaulo.goncalves@usi.ch

WFP EthiopiaAddis Ababa

November 3, 2010

4

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

WFP Distribution on Somali Region

WFP Ethiopia distributed 970,000 metric tones of food aid in 2009

• Transportation cost: US $65 millionPrimary transportation (from ports to hubs):

• Cost: US$ 48 millionSecondary transportation (from hubs to final destinations)

• Cost : US$ 17 millionDistribution context:

• 3 Ports , 20 Hubs, 80 FDPs

6

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 70,00060,000

15,000 130,00045,000

7

Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply

P1 Djibuti 40,000 15,000 130,000 120,000 55,000 10,000 370,000P2 Berbera 25,000 45,000 20,000 90,000H3 Dire Dawa 10,000 15,000 15,000 5,000 15,000 60,000H4 Jijiga 5,000 25,000 30,000H5 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.

SupplyDemand

8

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)

9

Simplified WFP Distribution Cost

Dire Dawa Jijiga Nazareth Kombol cha Mekele Woreta Supply

P1 Djibuti 1600000 1050000 6500000 14400000 7150000 1500000 32,200,000P2 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

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 370,000P2 Berbera X23 X24 X25 X26 X27 X28 90,000H3 Dire Dawa – X34 X35 X36 X37 X38 60,000H4 Jijiga X43 – X45 X46 X47 X48 30,000H5 Nazareth X53 X54 – X56 X57 X58 40,000F6 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

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.

12

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 370,000P2 Berbera X23 X24 X25 X26 X27 X28 90,000H3 Dire Dawa – X34 X35 X36 X37 X38 60,000H4 Jijiga X43 – X45 X46 X47 X48 30,000H5 Nazareth X53 X54 – X56 X57 X58 40,000F6 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

Optimal Food Aid DistributionPotential Cost Savings

10.3

4.1

Costs (Million US$)

65

60

55

50

0

50.5

New RoutesOld Routes2009 Actual

65.0

-22%

14

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)

15

Transport Cost Increases

0

20

40

60

80

100

120

140

160

180

200

220

240

0 50,000 100,000 150,000 200,000Quantities (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)

16

ConclusionsTransshipment 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

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

Irineu de Brito Junior 

USP

(ibritojr@yahoo.com.br)

Silvia Uneddu 

UNICEF (suneddu@unicef.org)

Paulo Gonçalves

USI (paulo.goncalves@usi.ch)

SUPPLY CHAIN OPTIMIZATION OF THE DISTRIBUTION OF MOSQUITO NETS IN

IVORY COAST

19

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

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

Project overview

San Pedro

Ferkessedougou

Bouake

Abijdan

Yamoussoukro

22

SUPPLIERS AND PORTS LOCATION

23

Health District

24

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 IVp4

Unstuffing

25

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

26

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

27

The Model

Constraints:

flow conservation k

pcjki

pcij 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 pd

c kpckd ZTP 1

ppdZ

assures that a district is supplied exclusively by one product

28

Results: Transport from suppliers to ports at destination

Supplier Supplier I Supplier III Supplier IV

TotalOrigin Port Haiphong Chennai Qingdao

CIV Port / Hub 40ft40ft

HC20ft 40ft

40ft

HC20ft 40ft

40ft

HC20ft

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

Yamoussoukro

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

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

Districts34 24 10 6 74*

*A district could be supplied by more than 1 hub/port

30

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.

31

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

MASHOM – IOM Partnership for Capacity Building

Mike PillingerChief of Mission

IOM Iraqmpillinger@iom.int

Lado GvilavaGlobal Logistics Coordinator

IOM lgvilava@iom.int

Paulo GonçalvesAssociate ProfessorUniversity of Lugano

paulo.goncalves@usi.ch

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

VisionVision MissionMission

34

Approach: Combines projects with institutional partnerships

Program at a GlanceProgram at a Glance Project-based approach with impactProject-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

35

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)

36

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.”

37

MASHOM Project Has Lead to Higher Fundraising

MASHOM Skills Applied toReduce Logistic ComplexityMASHOM Skills Applied to

Reduce Logistic ComplexitySuccessful Project Led to Additional

FundingSuccessful 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 refugeesThe Government of Japan funded IOM with USD 300’000 to provide aid to populations (urban refugees) affected by the Syrian crisis in Iraq.

Complex logisticsThis 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

MASHOM Led to Immediate & Measurable Savings

MASHOM Transport OptimizationMASHOM Transport Optimization MASHOM Lean Six SigmaMASHOM 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

39

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

40

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

41

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

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), LuganoFounder & Director – Master Humanitarian Logistics & Management – Master Humanitarian Operations & SC Management – Humanitarian Operations Research CenterResearch Affiliate – MIT Sloan School of Management

paulo.goncalves@usi.ch