Dr. Paulo Gonalves Associate Professor Universit della Svizzera - - PowerPoint PPT Presentation

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
• rganizations 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

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%)

P1 P2 H3 H4 H5

F6 F7 F8

370,000 90,000 140,000 70,000 60,000 15,000 130,000 45,000

6

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

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 – 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)

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

• ptimal 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 – (X + X + X + X ) – (X + X + X + X ) = 130 – – ≥ 0, and ≤

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 50.5 New Routes Old Routes 2009 Actual 65.0

• 22%

13

Transport Cost Savings

20 40 60 80 100 120 140 160 180 200 220 240 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)

14

Transport Cost Increases

20 40 60 80 100 120 140 160 180 200 220 240 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.

17

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

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

• u

Bouak e Abijdan Yamoussoukr

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 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

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

25

The Model

Objective Function:

         

    

          

p c j k pcjk cj p c i j pcij p pc p c k d pckd pckd p c j k pcjk pcjk p c i j pcij pcij

TO cc TS pr nq TP cp TO co TS cs TC min

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

 



k pcjk i pcij

TO TS

 



d pckd j pcjk

TP TO

integer and binary variables

pi c j pc pcij

sc nq TS  



) (

supplier production capacity

d p c pc k pckd

dm nq TP  



) (

demand is satisfied

pcij pij pcij

TS as TS  

pcjk pjk pcjk

TO ao TO  

pckd pkd pckd

TP ap TP  

use only available routes

bigM Z TP

pd c k pckd

 



pd c k pckd

Z TP 



1 



p pd

Z

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 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

Yamoussoukr

• Phase 1

14 3 12 2 9 40 Phase 2 1 1 Phase 3

Bouake

Phase 1 16 2 1 4 1 5 1 30 Phase 2 Phase 3

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

• rigin (depending on the

location of the suppliers)  Shipments from 3 ports of

• rigin (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

• f 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

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

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

• rganizations

Applied Projects Institutional Partnership

Group of up to 4 students realize a field project with a problem of their

• rganization

Tailor-made courses designed to develop capacity of HOs’ field staff

Strategic & Tactical

Strategic & Tactical Decision making courses cutting across

• rganizational 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

• perational modalities. It is a great
• pportunity 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

• ther.”

“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

paulo.goncalves@usi.ch