vFinal Sarah Dewilde Cold chain distribution network design Management... · Cold chain...

Cold chain distribution network design in developing countries A robust and resilient vaccine distribution network in Madagascar

FACULTY OF ECONOMICS AND BUSINESS

Sarah Dewilde

r0681489

Thesis submitted to obtain the degree of

Major Production and Logistics

Promoter: Prof. Dr. Nico Vandaele Assistant: Catherine Decouttere

Academic year 2017-2018

Master of Business Engineering

Sarah Dewilde Cold chain distribution network design in developing countries Promoter: Prof. Dr. Nico Vandaele

List of abbreviations

BHC Basic health center

CCE Cold chain equipment

DRSP Direction Régionale de la Santé Publique – Regional warehouse

EPI Expanded program on immunization

EVM Effective vaccine management

GAVI Global Alliance for Vaccines and Immunization

IC Immunized children

LMIS Logistics management and information system

SC Supply chain

SS Sub Saharan

UNICEF United Nations Children’s Fund

WHO World Health Organization

1 Sarah Dewilde Cold chain distribution network design in developing countries Promoter: Prof. Dr. Nico Vandaele

Cold chain distribution network design in developing countries

A robust and resilient vaccine distribution network in Madagascar

Sarah Dewilde Master of Business Engineering

Faculty of economics and business, KU Leuven

This dissertation evaluates an immunization supply chain, its challenges and some strategies to

reduce the impact of these challenges. Key concepts from state-of-the-art literature on supply

chain disruption and mitigation strategies are applied on a vaccine supply chain and are used in

a case study in which Madagascar’s vaccine distribution network was simulated using the

Anylogic software. The goal was to identify design changes to reduce the burden of the rainy

season. The simulation of the current situation suggests an immunization coverage of 64%, with

a gap of 22% between the rainy-season affected areas and the non-rainy-season affected areas,

which provides the means to immunize 530.000 children on an annual basis. This research

points out that by (1) anticipating the missed resupply moments during the rainy season by

implementing a buffer, which increases the requested order sizes of the rainy-season affected

areas by 100% during the dry season, and (2) shifting to a bimonthly supply system, an overall

improvement of 8% in immunization coverage can be obtained. More important improvements

are found when also the cold chain equipment is partially renewed, resulting in approximately

120.000 extra immunized children, an immunization coverage of 76% and a gap of only 7%.

This solution, however, comes at a cost estimated at 36 eurocents per immunized child. Yet,

the macro-economic benefits of immunizing extra children are far-reaching.

Keywords: simulation, immunization supply chain, vaccines, Madagascar, resilience, Anylogic

1. Introduction Although more than 5 million deaths were averted annually between 2010 and 2015, thanks to

vaccines delivered around the world, one out of ten children have remained unreached by immunization

programs in 2015 (Path & WHO, 2015). As long as immunization supply chains cannot safely and

reliably manage, store, transport and deliver vaccines to all people under all circumstances,

immunization and child and maternal health services will fall short of their full potential. With vaccines

being one of the most important means to enhance the wellbeing of the population, it is hard to

overestimate the importance of a properly functioning cold chain network.


Globally, many countries are facing a broad range of humanitarian incidents resulting from various

hazards which differ in scale, complexity and scope. This complex setting does not facilitate the process

of getting the right vaccine, in the right quantities, at the right time, in the right condition, to the right

place, at the right cost. Incidents have extensive political, economic, social, and public health impact

through disruption of the health systems and basic infrastructure (Path & WHO, 2015).

This dissertation evaluates an immunization supply chain (further referred to as SC) and highlights

potential challenges. Next, plausible strategies are put forward to lessen their impact. The ultimate

deliverable is the case study of Madagascar, in which a thorough analysis of the country's context

indicates potential challenges, e.g. seasonal geographical isolation due to the rainy season. A simulation

study allows to determine strategies to strengthen the immunization service delivery and improve the

availability of vaccines in the affected regions.

2. Problem statement and literature review “The best vaccine imaginable is only valuable to the extent we get it to everyone who needs it”

– Seth Berkley, CEO GAVI Alliance

2.1. Problem statement

If vaccines are the solution, then why aren’t children receiving them? The answer to this question

lays within the journey any vaccine follows from manufacturer to final destination. A journey in which

they are exposed to extreme heat, inappropriate cold chain equipment, long distances between facilities

without adequate infrastructure and many more challenges that can disrupt the supply chain. Without

an adequate immunization supply chain, children cannot be vaccinated against life-threatening diseases.

Numerous researchers have acknowledged the importance of the SC in the challenge to increase

the immunization coverage. In 2011, Kauffman et al. declared that SC considerations should become

an integral part of vaccine development and production research. This was followed by a statement by

Zaffran et al. in 2013, that stronger vaccine supply and logistics systems are needed in order to cope

with the introduction of new vaccines. Zaffran et al. claim that by adopting commercial best practice,

the transparency and efficiency of supply chains and potency of vaccines can be improved. This

approach was used to evaluate and strengthen the immunization SC in Nigeria (Sarley et al., 2017).

In addition to this, in 2009, the HERMES simulation software was developed by John Hopkins

University and the Pittsburgh Supercomputing Center. HERMES generates detailed discrete-event

simulation models that include virtual representations of all vaccine vials, storage and immunization

locations, storage and transportation devices, ordering and shipping policies, and logistics costs

associated with SC operations. A HERMES-generated model tracks each vial as it moves through the

system to be shipped, stored, and ultimately used or wasted.


Hereafter, a whole range of case studies were published assessing the possibilities to enhance the

immunization SC using this software. Assi et al (Assi et al., 2011, 2013) and Lee et al. (Lee, Assi, et

al., 2012) evaluated the situation in Niger. Brown et al. (Brown et al., 2014) analysed Benin and Lee et

al. (Lee et al., 2016) assessed Mozambique. Parallel to this, Chen et al. (Chen et al., 2014) constructed

a mathematical model which allows to assess the impact of removing a layer in the supply chain,

changing the vial size of the vaccines, introducing a new vaccine or expanding capacity on the

immunization rates. It can be used to better understand resource constraints in existing networks so as

to improve vaccine delivery and coverage.

Despite all the knowledge on the optimal setup of immunization supply chains, no studies have

been found in which challenges of SC disruptions are taken into account when designing the network.

SC disruptions, such as inaccessibility due to rainy season, floods, hurricanes, epidemics or pandemics,

etc. can have a substantial influence on key SC parameters such as demand, supply, delivery time of

products, and costs. They may also result in reducing capacity of SC facilities and transportation links

or even eliminating them (Govindan, Fattahi, & Keyvanshokooh, 2017). It is clear that this leads to

interrupted vaccine availability, which leads to missed opportunities to vaccinate and to populations

running the risk of not being protected against deadly preventable diseases.

Leading authorities in immunization advocate the need of more resilient health systems. Unicef’s

vision for the future of immunization SC clearly illustrates this. It states that “strong health systems

should be flexible, resilient to shocks and emergencies, and adaptable to new or unanticipated

developments” (WHO/UNICEF, 2016). This dissertation aims to assess which elements may break the

cold chain, how they disrupt the SC and what strategies should be considered to adapt the design of the

network to prevent or avoid the disastrous effects a shock or an emergency could have. This includes a

thorough analysis of the parameters that influence demand, the factors that hinder the distribution of

the vaccines and the broader context of the developing. It can be considered as obvious that this specific

context brings along several challenges such as a complex stakeholder structure, the occurrence of

armed conflicts and natural disasters which all add uncertainty to the distribution process.

This dissertation is structured as follows: first a literature review (Section 2.2) answers the

question: “Which elements may break a cold chain; how do they disrupt the SC and what strategies

should be considered to adapt the design of the network to prevent or avoid the disastrous effects a

shock or emergency could have?” Next, the frequently used research techniques in literature and the

reasoning for opting for a simulation study are discussed in Chapter 3. Finally, the case study on

Madagascar provides an answer to an adjusted version of the research question, being: “What strategies

can be considered to adapt the immunization distribution network in Madagascar to limit the impact of

the five months of isolation caused by the rainy season?” in Chapter 0.


2.2. Literature review

Plentiful research has been conducted regarding the design of a resilient SC network. Although the

literature covers a wide variety of application areas, no authors have discussed how the theory applies

to an immunization distribution network. A schematic representation of the chapter can be found in

Appendix I. Firstly, it provides the key concepts of a SC network (see Section 2.2.1) and dives deeper

into the vaccine SC characteristics (see Section 2.2.2). Next, the challenges to which a SC network is

exposed are described (see Section 2.2.3), and an analysis of how to strengthen a network is given (see

Section 2.2.4). Ultimately, the goal of Chapter 2.2 is to determine the challenges that affect the

performance of immunization distribution networks and identify strategies that are applicable to

construct networks that perform well under major disruptions and challenges.

2.2.1. Supply chain network

A SC consists of four levels, i.e. suppliers, plants, distribution centers and customer markets. The

design of such a network involves multiple irreversible, long-term, strategic decisions (Lemmens,

Decouttere, Vandaele, & Bernuzzi, 2014). A SC network consists of all the operations to provide goods

and services through to the end consumer (Slack, Chambers, Johnston, & Betts, 2009).

Lemmens et al. (Lemmens et al., 2014) states that a main difficulty for designing a SC is the

incorporation of uncertainty. A SC network is exposed to risks from multiple hazards, ranging from

natural events and technological failures to intentional malicious acts. Moreover, disruptions in the

operation of these systems can have cascading impacts within the system. In addition to the effects of

direct damage to the physical transportation infrastructure, indirect damage to, for example the economy

and social systems may result (Faturechi & Miller-Hooks, 2014). The importance of thoroughly

assessing these disruptions is stressed by Klibi et al. (Klibi & Martel, 2013) who assert that, since

networks are designed to last for several years, they should be robust enough to cope with all the random

environmental factors affecting normal operations of a company as well as sustain their performance in

case of major disruptions.

2.2.2. Vaccine supply chain network

Lee et al define a country's vaccine SC as the complex system of locations, storage equipment,

vehicles, transportation routes and personnel that bring vaccines from a central location to the people

in need (Lee et al., 2016). UNICEF adds to this definition the importance of the cold chain and defines

the immunization cold chain as a series of storage and transportation links, all of which are designed to

keep the vaccine at the recommended temperature from the point of manufacturing until it reaches the

target beneficiary (Appendix II) (Government of India Ministry of Health & Family Welfare, 2016). A


typical immunization SC in a GAVI-eligible1 Sub Saharan (SS) country is set up in four levels. That is

national, regional, district and county levels and follow the administrative boundaries (Lee et al., 2015).

Unlike a commercial supply chain, the vaccine SC is not profit driven and all areas must be served,

despite limitations in infrastructure and resources (Yadav, Stapleton, & Wassenhove, 2013).

An efficient SC is essential to guarantee consistent availability of affordable, high quality vaccines

at all health service delivery points (Foster, Laing, Melgaard, & Zaffran, 2006; Tangcharoensathien et

al., 2008; Yadav, 2015). Unfortunately, inefficiencies remain ubiquitous and are stalling new vaccine

introductions (GAVI Alliance, 2016c), contributing to prolonged vaccine stock outs due to forecasting

errors (Lydon et al., 2017), wasting vaccines by accidentally exposing them to freezing or too warm

temperatures (Hanson, George, Sawadogo, & Schreiber, 2017), and constraining coverage by vaccines

not being available when and where they are needed (Lee, Assi, et al., 2012; Van Den Ent et al., 2017).

These inefficiencies occur due to the previously described complex nature of a vaccine SC as well as

to additional challenges specific to the design of an immunization distribution network, e.g. temperature

instability, unreliable power supply, lack of refrigerated trucks, unpredictable travel time, etc.

In order to help countries increase the effectiveness and efficiency of immunization delivery, strong

supply chains are key (GAVI Alliance, 2016a). GAVI has defined a strategy to strengthen immunization

supply chains and consists of five fundamentals (GAVI Alliance, 2016c), i.e. SC leadership, continuous

monitoring and improvement, reliable data, well-maintained and cost-effective cold chain equipment

(further referred to as CCE) and implementing SC redesign projects. In addition to the focus points of

GAVI, Yadav et al. (Yadav, Lydon, Oswald, Dicko, & Zaffran, 2014) advocate the importance of SC

integration between vaccines and other public health products. The most significant integration

opportunities can be found within storage and distribution through the bundling of assets and the

creation of economies of scale.

2.2.3. Supply chain challenges

It is clear that given the complexity and dynamic nature of supply chains, the potential sources of

challenges are manifold. Challenges are defined, following Wagner and Bode (Wagner & Bode, 2008)

as “the combination of an unintended, anomalous triggering event that materialises somewhere in the

SC or its environment, and a consequential situation which significantly threatens normal business

operations of the firms in the supply chain.” To obtain a clear overview of the challenges and where in

the SC they occur, a categorisation based on the source of the challenges is put forward. This

categorisation is, first, set out as applied to a general supply chain. Hereafter, vaccine SC challenges

are categorized accordingly.

1 Gavi is an international organization that was created to improve access to new and underused vaccines for children living in the world’s poorest country.


2.2.3.1. Categorization of challenges based on their source

Many scholars have proposed typologies and/or taxonomies of risks (Chopra & Sodhi, 2004;

Christopher & Peck, 2004; Hallikas, Karvonen, Pulkkinen, Virolainen, & Tuominen, 2004; Jüttner,

2005; Jüttner, Peck, & Christopher, 2003; Norrman & Lindroth, 2004; Spekman & Davis, 2004;

Svensson, 2000). This study divides the SC risk sources into two distinct classes: endogenous and

exogenous challenges. This is proposed by, among others, Trkmand & McCormack (Trkman &

McCormack, 2009) and Christopher & Peck (Christopher & Peck, 2004). It has been applied in the

context of a developing country by Tukamuhabwa (Tukamuhabwa Rwakira, 2015).

Both categories can be further subdivided. The endogenous challenges consist of three sub-

categories: supply-side, firm-level and demand-side challenges, which is in line with the categorisation

as proposed by Christopher & Peck (Christopher & Peck, 2004). In contrast to firm-level challenges,

supply and demand side challenges can be seen as internal to the SC network but external to the firm.

A schematic representation of this structure can be found in Figure 1.

The first originates from suppliers that are unable to deliver the materials that the company needs

to meet its production requirements and/or demand forecasts (Vereecke, Pandelaere, & Boeykens,

2011), e.g. inability to handle volume demand changes, failures to make delivery requirements, inability

to meet quality requirements, single supply sourcing, high capacity utilisation at any supply source,

leadtimes, etc (Ho, Zheng, Yildiz, & Talluri, 2015). The latter is the risk that the company will

experience demand that is not anticipated, and provisioned for, in the chain (Vereecke et al., 2011). In

particular, it relates to the processes, controls, assets and infrastructure dependencies of the

organizations downstream and adjacent to the focal firm (Christopher & Peck, 2004). Examples of

demand side challenges are, inaccurate demand forecasts, sudden shoot-up demand, high level of

service required by customers, and inadequate lead-times (Ho et al., 2015).

Demand challengesSupply challenges

Control challenges

Process challenges

Environmental challenges (geopolitical and economic)

Figure 1 - Schematic representation of SC risk sources


The firm level challenges include process challenges and control challenges. Process challenges

are associated with the variability of a company's operational processes, such as variations in production

cost, quality problems or the possibility of a product recall (Vereecke et al., 2011). Examples of process

challenges are operator absence, lack of experience or training, warehouse and production disruption,

insufficient maintenance, centralized storage of finished products, transportation breakdowns, capacity

etc. Controls are the assumptions, rules, systems and procedures that govern how an organization exerts

control over the processes. In terms of the SC they may be order quantities, batch sizes, safety stock

policies etc. plus the policies and procedures that govern asset and transportation management.

Examples of control challenges are e.g. unreliable planning and control systems, the impact of

introducing a new product and a lack of skilled employees (Vereecke et al., 2011).

In addition to challenges internal to the network, there are also challenges external to the network,

i.e. geopolitical challenges and economic challenges. The first category is best described as potential

governmental, natural and societal disruptions of SC operations across different geographical locations,

e.g. war, terrorism, natural disasters and data security (O’Marah, 2017). The latter category

encompasses challenges such as informal sectors, unfair competition, poor transportation infrastructure,

unstable taxation, exchange rate fluctuations and power shortages (Tukamuhabwa Rwakira, 2015).

2.2.3.2. Categorization of challenges specific to a vaccine supply chain

Weak health-care systems fail to reach children due to insufficient funds, limited human resources

and/or the inability to operate in certain areas (UNICEF, 2014). In what follows, each paragraph, first,

sets the boundaries of the challenge and provides examples mentioned during expert interviews.

Supply challenges

Supply risks include all challenges linked to the availability of vaccines at national level. 38 percent

of the 47 countries in SS Africa have reported at least one national-level stock out event for at least one

vaccine and for at least one month during 2015 (Lydon et al., 2017). A further analysis of these results

has indicated that in nine percent of cases, the stock-outs were due to a global supply shortage. Whereas

this master dissertation studies the in-country distribution of vaccines, a multitude of supply challenges

are found during the transportation to the country in question, e.g. tarmac time where vaccines are in

stored in non-cold chain environments waiting for the customs processes to be finished, long lead times

with ocean freight, stock in transit and the impact of the number of different parties involved

(Molenaers, 2017).

It is important to note that decisions taken at this stage in the SC can have a major impact further

downstream. Examples of such decisions are packaging, which changes how much storage is needed;

the use and size of various pallets and containers, which affects the equipment needed to move the

vaccines; and shipment timing, which can affect getting the vaccine to the point of delivery in time

(Kaufmann, Miller, & Cheyne, 2011).


Demand challenges

Demand challenges are primarily related to the sources of uncertainty linked to forecasting the

demand. The main challenge in terms of demand for vaccines is the large number of dispersed people

with different cultural backgrounds and beliefs. Furthermore, the strive for equity implies that also the

people in the most geographically isolated areas should have access to vaccines.

The lack of information throughout the system troubles the decision makers to properly forecast

the need for vaccines. Many countries do not have reliable data about past vaccine usage or accurate

projections of target populations and their locations. National vaccine forecasting is done using

population estimates, birth rates, infant mortality rates, vaccine waste rates, and prior-year estimates of

usage (Kaufmann et al., 2011). All of these estimates are inaccurate to some degree, because census

data are typically only brought up to date every ten years. The combination of inaccurate estimates

means that incorrect vaccine forecasts are replicated year after year (Kaufmann et al., 2011). Besides,

in the context of a developing country, additional difficulties to properly estimate the geographical

spread of demand are faced due to for example seasonally migrating populations and internally

displaced people due to conflict or disasters.

Process challenges

Process challenges cover all risks related to the physical flow of the vaccines from the national

warehouse to the final consignee. The challenges in this category are manifold. Firstly, cold chain

capacity is insufficient, and equipment is of poor in quality. In 2014, only 2 percent of the facilities

possessed optimal equipment and 34 percent of the facilities had none or only non-functional equipment

(Path & WHO, 2015). Related challenges include substandard installation, inconsistent preventive

maintenance and inadequate supplies of parts for repairs (Lennon et al., 2017). The lack of a stable

power supply is mostly countered through the utilisation of generator-driven or solar-driven fridges.

Generators are fuel driven, hence, need a timely supply of fuel. Moreover, they, too, can break down

and thus, timely maintenance, spare-parts and, ideally, back-up generators are required (Vleugels &

Van Roey, 2017). Inadequate transportation modes, absence of fuel, infrequent maintenance and the

lack of spare parts prevent vaccines to get to their destination. Furthermore, no warehouse or

transportation mode can be operated without people. The absence of skilled staff and geographical and

professional isolation in rural and remote environments are only two aspects mentioned that can pose a

threat to the SC (Vleugels & Van Roey, 2017).

Most systems are set up following a standard four-tier design template, without adjusting it for

country specific circumstances. This may cause excess of storage locations and transportation routes

(Lee et al., 2015). Additionally, risks can be related to the fact that delivery routes often follow

administrative levels rather than the shortest distance (Zaffran et al., 2013). Furthermore, information

systems are often non-existent (Zaffran et al., 2013) delivery frequencies are often not adapted to the

countries and large inventories are stored at multiple levels (Kaufmann et al., 2011).


Control challenges

Control challenges include those elements that pose a risk to the quality of the process. The lack of

data and information systems throughout the SC limits the potential of the supply chain. Inventory

management systems remain predominantly manual systems, especially further downstream in the SC

(Dowling, 2011). The shortfall of data visibility induces a lack of coordination on many levels, which

can cause high quantities of vaccines to be ordered, resulting in improper storage and ultimately waste

(Kaufmann et al., 2011).

A reliable SC requires a reliable workforce. Yet, the staff in immunization supply chains in

developing countries are often unqualified, poorly trained, un-empowered and poorly managed

(Vleugels & Van Roey, 2017). Besides training, other elements such as a lack of supervision or contact

with supervisors, absence of a trust relationship between the employee and the supervisor, inadequate

professional and personal facilities, misaligned pay and conditions and workload affect staff

satisfaction, turnover, and the ability of staff to complete their job satisfactorily (Dowling, 2011;

Hawthorne & Anderson, 2009; WHO, 2010).

Another point of misalignment originates from the way the distribution is organized. Demand in

the SC is typically based on pull mechanisms. That is, health centres determine their own requirements

and place orders themselves. In practice this has a number of drawbacks. Medical professionals are not

logisticians and often cannot forecast effectively. This can cause over-ordering or emergency ordering

to occur, resulting in dramatic fluctuations in stock levels. Arranging dedicated transport can also be

problematic (GAVI Alliance, 2016b).

Environmental challenges

Although significant strides on the social, political and economic fronts are made since the turn of

the 21st century, contextual challenges remain ubiquitous in SS Africa (UNDP, 2017). Sub-Saharan

Africa is made up of 48 extremely diverse countries, differing in size of their economies, level of

development, dependency on exports of commodities and the effectiveness of state institutions and

domestic political arrangements. Despite all the differences some generally present challenges can be

observed.

Geopolitical challenges

Conflicts remain pervasive throughout the continent and have major consequences to successfully

deliver vaccines to the people in need. Civil wars fought along ethnic and political lines result in, among

others, killed and displaced people, unavailable health workers, broken infrastructure, lack of funds,

increased insecurity and demolished health facilities. Islamist extremist groups, e.g. Boko Haram, are

likely to target Western interests putting people related to Western organisations at risk (Cummings,

2016). Furthermore, coups and corruption undermine the attractiveness of the region for international

funding and private investments that could enhance the availability of vaccines.


Above all, the unstable political climate limits the development of the region, resulting in

inadequate economic and social infrastructure such as transport, energy, health and education.

Underdeveloped road and transportation infrastructure might as well be one of the major challenges for

the immunization supply chain. It prolongs the time in transport, it can impede the quality and safety of

the vaccines and it curbs the accessibility of certain areas either seasonally or all-year round (Vleugels

& Van Roey, 2017). The inaccessibility puts stress on the equitable coverage of the immunization

supply chain. Furthermore, the absence of reliable power supply in numerous countries in SS Africa

makes cold chain maintenance difficult. The health facilities and intermediate depots must rely on costly

generators, frozen icepacks, cold boxes and consistent transportation to remote health facilities (Li,

2011). Certain countries in the region face difficulties with regards to mobile connectivity, limiting data

visibility and information sharing throughout the supply chain.

One cannot deny the importance of climate change anymore. Its repercussions will be felt in various

ways throughout both natural and human systems in Sub-Saharan Africa (Serdeczny et al., 2017). With

rainy seasons occurring every year and weather models doing a good job at predicting tropical storms

these challenges are rather easy to predict, and thus should be included in the design of the supply chain.

Economic challenges

The economic environment challenges many markets in SS Africa. Dramatic currency fluctuations,

depressed prices on commodities such as oil and copper, and sluggish demand from China and Europe

(Africa's largest trade partners) have put pressure on the region's economies (Rosenberg, 2015). Even

in the regions where growth remains strong, in many cases it continues to rely on public sector spending,

often at the cost of rising debt and crowding out of the private sector (IMF, 2017). The severity of this

threat can be illustrated by the fact that, in 2015, 39 percent of the national level vaccine stock-outs

were caused by funding delays2.

Additionally, financial constraints lead to an insufficient level of resources allocated to health care.

One of many examples that illustrate the impact of the lack of funding at peripheral level is the

unavailability of funds to buy fuel for transportation or generators, or the delayed payment of wages.

2.2.4. Distribution network resilience – How to cope with the challenges?

Analysing and classifying the challenges is only the tip of the iceberg. The essence evolves around

adapting the design of the network accordingly, i.e. creating resilient networks (Besiou, Pedraza-

Martinez, & Van Wassenhove, 2014). In 2017, a review of resilience in transportation was conducted

and a scheme linking all concepts related to resilience in current literature was developed (Figure 2)

(Abubakar, Mahfouz, & Arisha, 2017; Sheffi & Rice Jr, 2005; Wan, Yang, Zhang, Yan, & Fan, 2017).

2UNICEF is unable to ship vaccines to a country unless payment is received upfront, the common delays in government releasing funds to pay UNCEF for the purchasing of vaccines invariably leads to a stockout (Lydon et al., 2017).


2.2.4.1. A resilient system

A network is characterised as resilient when it performs well and delivers products and services

under challenges such as discussed in Section 2.2.3. This is defined as the ability to resist, absorb and

adapt to challenges (Bruneau et al., 2003) and return to normal functionality (Faturechi & Miller-Hooks,

2014). In general, a resilient system can be summarised into five capabilities: the ability to anticipate,

adapt, respond, recover and learn (Abubakar et al., 2017). A definition of each capability can be found

in Appendix III. The five relate to the full range of SC risk management strategies and thus provide a

mechanism to cope with risks and changes from varied sources (Abubakar et al., 2017). The abundance

of challenges and the widespread impact underscores the importance of creating resilient systems.

Figure 2 provides an overview of all concepts related to a resilient system, based on the disruption

profile created by Yossi Sheffi (Sheffi & Rice Jr, 2005). Firstly, it can be seen that the resilience of a

system is summarized in four concepts: reliability, redundancy, robustness and recoverability of the

system. Redundancy refers to the existence of alternatives to mitigate adverse impacts of challenges.

Robustness can be understood as the extent to which the network is able to create value for any plausible

future scenario (Klibi & Martel, 2013). It serves as a buffer to remain stable when exposed to a

disruption (Faturechi & Miller-Hooks, 2014) and entails proactive anticipation of change before it

occurs (Wieland & Marcus Wallenburg, 2013). Recoverability includes flexibility and adaptability.

Flexibility designates the ability to respond to a changing environment in a timely matter. Adaptability

can be understood as the ability to develop different responses to match the nature of the challenges it

faces. These characteristics determine the overall performance of a distribution system on how long it

can perform without failing, what actions it will take in the face of a disruptive event, how performant

the system will be after being disrupted and how it reaches a new equilibrium (Wan et al., 2017).

The framework in Figure 2 is especially useful since it demonstrates the important distinction

between pre-disruption elements and during disruption elements. It can be seen that the notions of

Figure 2 - A resilient system


redundancy and robustness, relate mainly to actions taken before a disruption occurs, whereas,

flexibility can be associated with actions occurring after a disruptive event. Sheffi stresses that there is

significantly more leverage in making supply chain flexible than there is in adding redundancy (Sheffi

& Rice Jr, 2005). In literature discussing mitigation strategies, a similar classification is put forward,

using the ideas of proactive, concurrent and reactive strategies (Abubakar et al., 2017).

2.2.4.2. Mitigation strategies

The aim of mitigation strategies is to prepare for, respond to and recover from SC disruptions,

which can be linked to the disaster management cycle often referred to in humanitarian logistics

literature. This cycle, Figure 3, contains four stages: preparedness, response, reconstruction, and

mitigation (Tomasini & Van Wassenhove, 2009). The first two stages, preparedness and response,

mitigate the effect of crises (Besiou et al., 2014). These strategies can be categorised depending on the

moment of implementation, i.e. proactive, concurrent and reactive (Abubakar et al., 2017).

Proactive strategies, on the one hand, refer to competencies needed in the pre-disruption phase.

Concurrent strategies, on the other hand, relate to quick reactive thinking and first-response abilities to

cope with disturbances in the during-disruptions phase (Hollnagel, Pariès, Woods, & Wreathall, 2010;

Sheffi & Rice Jr, 2005). Finally, reactive strategies refer to what is required in the post-disruption phase

so as to recover (Abubakar et al., 2017). The aforementioned categories can be related to the capabilities

of the system, as illustrated in Figure 2.

The complex nature of a distribution network implies that these strategies can influence one

another, which can be both beneficial, creating synergies, as well as harmful, provoking trade-offs to

be made. This can be illustrated with the complex interplay between transportation and storage.

Increasing storage, for example, implies a decrease in supply frequency, however it can cause

transportation bottlenecks to worsen (Trkman & McCormack, 2009). Implementing a particular

resilience strategy can produce another threat, either at the same or at a different point in the supply

network. In order to maximise the effectiveness of the earlier described strategies, continuous

adaptation and a holistic approach are essential.

B

C

D MITIGATION

Part of the humanitarian logistics stream

RECONSTRUCTIONConsists of an immediate response and restore phase

Part of the humanitarian logistics stream.

RESPONSE

Part of the humanitarian logistics stream

PREPAREDNESS

A DISASTER MANAGEMENT

CYCLE

Figure 3 - The humanitarian logistics stream cycle


2.2.4.3. Mitigation strategies in a vaccine supply chain

UNICEF's vision states: “A world where no child dies from a preventable cause, and all children

reach their full potential in health and well-being”. A key action to achieve this is increasing the

resilience of both delivery systems and communities in absorbing and recovering from external shocks,

including from public health emergencies and outbreaks (UNICEF, 2009). In what follows, a more

thorough analysis of strategies is provided categorised by moment of occurrence.

Proactive strategies

Ideally, the design of a resilient network starts at the very beginning in the contingency planning

process, when the focus should be on identifying ways to strengthen the resilience of the health care

facility in terms of its functioning and physical structure (ICRC, 2015). At warehouse level this, among

other, concerns the presence of a skilled and adequate workforce that is able to perform frequent

maintenance works (Vleugels & Van Roey, 2017). Extensive training and follow-up of the health

workers decreases the likelihood of man-made mistakes. As to equipment, the procurement of off-grid

fridges, which can be generator or solar driven, eliminates the impact of an unreliable power grid.

Furthermore, a well-established inventory system ensures that stocks have the longest possible

remaining shelf life allowing to bridge a maximum of time in case of an emergency (ICRC, 2015).

Next, focus should be on strengthening the coordination and cooperation with other health-care

providers. In this context, data visibility is key. The implementation of Electronic Vaccine Intelligence

Networks (eVIN), for example, enables immunization managers to see when to buy more vaccines, how

much to order, and even which fridge needs maintenance. The data visibility by eVIN reduces vaccine

stock-outs (Levine, 2017). However, basic telecommunication networks need to be in place.

Finally, advances in the development of vaccines can significantly reduce the impact of possible

challenges. Smart vaccines, for example, could be a game-changer in resource-poor countries. Their

production is rapid and low-cost, and they remain stable, safe and effective without refrigeration which

has a major beneficial impact on the complexity of the distribution network (Levine, 2017).

Concurrent strategies

Concurrent strategies are all about managing and adjusting resources, as well as maintaining

control in the event of a SC disruption. Here, the trade-off is often between speed and inventory, or,

respectively, between a “fast” or a “slow” cold chain. A fast cold chain’s supplies cover less than a

month, whereas a slow cold chain’s supplies cover over a month or more (WHO, 2008). A fast cold

chain may mean higher distribution costs, but the costs are compensated for in part by placing smaller

quantities of vaccines in circulation. A slow cold chain reduces the costs of vaccines distribution but

increases the quantity of supplies in circulation. This trade off clearly denotes the choice between

flexibility and redundancy (Sheffi & Rice Jr, 2005). In cases where health facilities are inaccessible

during several months of the rainy season, WHO recommends a slow cold chain. These facilities would

then receive enough vaccines and supplies to cover the entire period of inaccessibility (WHO, 2008).


Redundancy is often used as a way to enable the facilities to remain up and running during a threat.

Buffer stock, back-up generators, back-up mobile freezers that can be plugged in cars and backup spare

parts for the generators and fridges, for example, serve as buffer just in case. To guarantee the

availability of fuel to operate the generators, contracts can be negotiated with local fuel stations such

that the fuel tank is filled up on a frequent basis (Vleugels & Van Roey, 2017). In the extreme case in

which all the back-up measures are not sufficient, the vaccines supply schedule can be adapted, and all

vaccines are immediately stored in the facilities further downstream (Vleugels & Van Roey, 2017).

At network level, a key improvement area resides in the bundling of forces. Through the integration

of immunization services with other health care services or collaboration between different players in

the field, the supply base expands. This allows for capacity, infrastructure and knowledge sharing as

well as providing a more cost effective and sustainable solution (WHO, 2008). Likewise, collaboration

with the private sector, e.g. Project Last Mile, allows for further strengthening. Ultimately, the whole

sector could even be outsourced or privatized. Outsourcing, despite challenges during the transition

phase, has proven successful in the majority of cases (GAVI Alliance, 2016b).

Lastly, emergency scenarios that are applicable in case of a disruptive event are key. This includes

alternative routes, alternative modes of transportation as well as finding the appropriate mix of service

delivery strategies. Innovation, too, can play a major role, e.g. drone delivery can potentially serve as a

new way to reach geographically isolated areas.

Reactive strategies

After a disruptive event occurred, it is essential to reassess the system and determine areas of

improvement. The aforementioned contingency planning needs to be regularly reviewed to

accommodate for changes in the external situation. Also, mechanisms to reflect lessons learnt should

be established as part of these regular reviews, to improve further operations (ICRC, 2015).

The main conclusion that can be drawn from this literature review is that the sources of potential

challenges are ubiquitous and that to cope with them a trade-off needs to be made between redundancy

and flexibility. Sheffi states that, in general, there is significantly more leverage in making supply chains

flexible than there is in adding redundancy (Sheffi & Rice Jr, 2005). Yet, the inaccessibility of certain

areas due to the rainy season asks for a certain level of redundancy, which is reflected in the

recommendation for a slow cold chain as made by the WHO. However, their definition of slow is broad.

Hence, the remainder of this master dissertation further evaluates the design parameters of such a slow

SC.


3. Methodology 3.1. Methodology

Over the course of the last years numerous papers have discussed the design of supply chains under

uncertainty and disruptions. Whereas mathematical models are often adopted, this dissertation will

make use of the simulation technique. This allows to assess the impact of mitigation strategies

throughout the whole network without neglecting the interrelatedness of strategies and challenges. First,

this section discusses the main methods that are used in the state-of-the-art literature. Hereafter, the

choice for the simulation technique is clarified, then a brief description of the required data is given.

3.1.1. Common methods in literature

The two main streams in Operations Research literature regarding resilience are the papers

conducting research through mathematical modelling versus those applying the simulation technique.

The first stream approaches the SC network design problem as an optimization problem and often

involves mixed-integer programming or stochastic programming. Traditionally, the mathematical

formulation of the SC design has been based on the facility location problem (Geoffrion & Graves,

1974). Related to SC resilience, the problem involves selecting locations, establishing their storage

capacity, and determining a distribution strategy that anticipates potential disruptions at distribution

centres (Garcia-Herreros, Wassick, & Grossmann, 2014). The main challenge when considering SCs of

significant size is given by the number of scenarios. In addition to the NP-hardness nature of the SC

network design problem under uncertainty, mathematical models may not capture all of the complex

interactions and uncertainty of the entire system (Brown & Lee, 2011; Lemmens et al., 2014) and is

therefore not desirable in the context of this master dissertation.

A second stream of literature approaches the design problem from a simulation point of view.

Simulation is a powerful tool to validate obtained policies in uncertain decision-making environments

(Govindan et al., 2017). It allows decision-makers to simulate operational, structural, policy,

procurement, technological, and managerial changes to the SC and to visualize the impact on cost and

various operational measures (Path & WHO, 2011). Simulation models are virtual representations of

the entire system and therefore serve as a ‘virtual real world’ to test and assess new configurations or

technologies (Brown & Lee, 2011). In the context of vaccine distribution, various research has been

conducted using the simulation method. The existing research considers the introduction of new

vaccines and technology (Lee, Assi, et al., 2012; Norman et al., 2013), altering characteristics of

vaccines and other technologies (Lee et al., 2011; Lee, Cakouros, et al., 2012), changing configuration

and operations of the SC (Assi et al., 2013), investing or allocating resources (Haidari et al., 2013) and

optimizing vaccine delivery (Brown et al., 2014).

Simulation makes it relatively easy to look at different scenarios for the vaccine SC, including

those radical ideas that would not be possible to test in real life, and create SC blueprints. Since, it is


essential to consider the overall picture, simulation seems a more suitable method for the context of this

master dissertation. Nevertheless, one needs to remember that models are simplified representations of

reality and cannot capture every factor that could affect the delivery of vaccines (Lee et al., 2016).

3.1.2. The simulation method as applied in this dissertation

Setting up a successful and trustworthy simulation study involves numerous steps. In the first phase

a conceptual model, e.g. under the form of a flow-chart, is created. The objective of this step is to

capture the system logic and data necessary for the simulation modelling activity (Persson & Olhager,

2002). Secondly, the ‘as-is’ SC is evaluated, i.e. a critical assessment of the current locations is

conducted with corresponding capacities, routes, transportation modes and their respective capacities,

etc. and also an exhaustive analysis of the operating context. In this step it is essential to obtain high

quality data from partners involved in the distribution of vaccines in the chosen region as well as to

obtain a deeper understanding of key issues, insights and contextual concerns. This can be obtained

through a so-called STEEP (Social, Technological, Economic, Environmental, and Political) analysis.

The outcome is a model of the current situation and an evaluation of what the future may look like.

A computer simulation model is constructed using the Anylogic simulation software, then, verified

and finally validated. Verification means testing the model against the conceptual model and making

sure all elements behave as they should. In the validation step, the model is tested against itself (Persson

& Olhager, 2002). The simulation encompasses five years.

In first instance, a base case analysis will be performed that allows to determine bottlenecks.

Hereafter, scenarios are constructed to overcome potential challenges to the system, these are based on

the mitigation strategies as discussed in the Section 2.2.4.3. Ultimately, the results will be used to

formulate recommendations for policy-makers.

3.1.3. Required data

First of all, a suitable country needed to be chosen. It was essential for our study that the country’s

vaccine SC was affected by a recurring threat. Therefore, it was chosen to evaluate Madagascar’s

current situation and the influence of the rainy season (see Section 0). Data were collected from a wide

variety of sources, i.e. online sources such as the WHO website, GAVI online resources, TechNet 21,

etc. as well as personal sources, mainly via Andry Fidele Ravalitera, from UNICEF Madagascar.

The data needed are three dimensional. Firstly, supply side data are needed. This includes the

geographical location of health facilities and warehouses, their respective capacities, the transportation

modes with corresponding capacities and the operating methods (delivery strategies, push or pull

strategies, etc). This information can be gathered through organisations such as UNICEF, Logistics

Cluster, etc. Secondly, demand side data are essential. This corresponds to the population dispersion

and the number of people in need related to each health facility as well as the ease of reaching the


population. Thirdly, information is needed to gain a deeper understanding on the context, its issues and

possible solutions. Thorough web research, interviews with people familiar with the geographic area as

well the distribution of vaccines in challenging contexts give an idea of the contextual barriers.

Andry Fidele Ravalitera, from UNICEF, provided an EVM3 analysis conducted in 2014, a Health

System Strengthening Cash Support proposal form conducted in 2014, the Health Sector Development

plan for the period of 2015 to 2019 proposed in 2015, and an application for support from the cold chain

optimisation platform from September 2016. In addition to this, raw data was obtained providing

insights in the trimestral needs per district per vaccine for the routine immunization delivery, as well as

the needs for the immunization campaigns conducted in August and September of 2015. Lastly, an excel

file combining 114 other files from 2017 was obtained containing population data, health facility

location and capacity data and resources per district.

The aforementioned files provided insights in the CCE inventory, transportation resources, target

population and target basic health centers from 2017. The data with regards to transportation axes and

routine trimestral needs are from 2015. Information on the needs at central level as well as the vaccines

included in the program and the central level capacity were only available from 2014. Even though

more data than expected was made available, numerous assumptions were required. The different years

of creation of the source documents caused deviations that were to be corrected for. Additionally,

elements such as vehicle capacity, fridge capacity, transportation waste percentages, failure rates, vial

size, transportation schedule and personnel per site were unavailable, hence, assumptions were made.

Finally, it is important to note that due to the challenging context of a developing country and the non-

commercial character of the supply chain, the data needs to be used with care. The practices as they

appear on paper often deviate from what actually happens in real life.

3 An analysis that helps to uncover important shortcomings in the performance of many countries’ immunization supply chains.


4. Case study: Madagascar This chapter provides an answer to an adjusted version of the research question being: “What

strategies can be considered to adapt the immunization distribution network of Madagascar so as to

limit the impact of the five months of isolation due to the rainy season?”. After an introduction to

Madagascar (Section 4.1) and an introduction to the current vaccine SC in Madagascar (Section 4.2),

the challenges to the system are categorized according to the taxonomy of Tukamuhabwa

(Tukamuhabwa Rwakira, 2015) as discussed in Section 2.2.3.1. A discrete-event simulation model

(Section 4.3) allows to assess the current situation (Section 4.5), based on certain performance

indicators (Section 4.4), and compare it to different scenarios to enhance the situation (Section 4.6).

Finally, a critical assessment of the results is provided (Section 4.7) and discussed (Section 4.8).

4.1. Madagascar in a nutshell

Madagascar is the fifth largest island in the world and located in the Indian Ocean (Worldbank,

2017). What could have been a flourishing paradisiac island ranks among the ten poorest countries in

the world. The extreme poverty - the average Malagasy is 42% poorer today than in 1960, the year of

independence - creates challenges for the health care system in terms of affordability and accessibility.

88% of the people live in rural areas which causes 50% of them to live beyond five kilometers from a

health facility, with some villages located 100 kilometers from the nearest health facility (Van Den Ent

et al., 2017). This refrains Madagascar from providing equitable access to immunization.

In 2017, the total population amounted to 25.612.861 and an annual birth cohort of 865.588 infants

(Gavi Alliance, 2017). Madagascar is a young country that is expanding quickly, at a growth rate of

2,7%, which will put additional stress on the already limited capacity to deliver basic services across

the country and on its natural resources. Clearly, this will result in an increase in vaccines needed.

After a coup in 2009, the country became ineligible to receive international aid during a period of

unprecedented international investment in global health (Bonds et al., 2017). Additionally, political

crises have had a significant influence on the economy in Madagascar. It brought along uncertainty for

the investors with many private firms reducing their operations or halting activities altogether

(Worldbank, 2017). Since the democratic elections in January 2014, Madagascar's newly recognized

government has become eligible for official aid from foreign governments, creating a singular

opportunity for transforming the national health system (Bonds et al., 2017).

The unfavorable investment climate has caused Madagascar to lag behind in terms of technology

and infrastructure. When comparing the statistics of Madagascar with the average statistics for the SS

countries a significant gap is noticeable. The rate of access to electricity was merely 17% in 2014

whereas the SS average is at 37%. In Madagascar on average 42% of the people have a mobile cellular

subscription, albeit a 74% for SS Africa. Furthermore, only 5% of the population uses internet, whereas

20% of the SS population does (Worldbank, 2017). The current state of the Malagasy infrastructure,

too, scores below average. In the latest global competitiveness index report from the World Economic


Forum, Madagascar ranks 133rd out of 139 concerning infrastructures. The state of the existing road

network is the 5th worst in the world as well as the electricity and telephony infrastructure being the

4th worst in the list. This puts a burden on communication and data sharing, key elements of a properly

functioning SC (World Economic Forum, 2017) and limits the implementation of new data solutions

such as mHealth or Logistimo, as these rely on mobile connectivity.

Madagascar is one of the most exposed countries to climate-related disasters. Recurring cyclones,

floods, droughts and locust invasions have affected the lives of more than half the population (USAID,

2017) and coping with them is constrained due to the weakness of the public infrastructure and services.

These cause roads to be blocked, bridges to collapse, areas to be unreachable, power to be unstable, etc.

Climate change threatens the wellbeing of both the country’s biodiversity and its people. It is predicted

that the number and severity of cyclones will increase. Finally, sea level will rise around Madagascar

endangering the numerous communities and ecosystem at its endless coastline (USAID, 2017). The

impact of the aforementioned events can be enormous. The most recent cyclone, Enawo in 2017,

affected over 430.000 people with an estimated economic loss equivalent to four % of the gross

domestic product. The three consecutive years of severe drought caused massive crop failure in 2016,

putting some 850.000 people, more than half of the population of the south of the island, in the

emergency or crisis phases of food insecurity (USAID, 2017). Appendix IV provides an overview of

the areas that are affected by recurring environmental challenges (FEWS NET, 2013).

The status of Madagascar’s immunization program reflects the challenging environment of the

country (Van Den Ent et al., 2017). The political crisis and international boycott led to a decrease of the

available budget by 65 %. In 2016, an average immunization coverage of 74 % was found. An overview

of the immunization coverage over time can be observed in Appendix V. At the same time, the article

by Van Den Ent et al. states that immunization inequities are apparent. The aforementioned article (Van

Den Ent et al., 2017) sets forth that in Madagascar “A child living in a family from the wealthiest Figure 4 - Immunization supply chain in Madagascar


quintile is 1.5 times more likely to be vaccinated than a child living in a family from the poorest quintile.

Children living in the Itasy region, a more affluent region, are 3.4 times more likely to be vaccinated

than children living in the Menabe region, a poorer region characterized by remote areas with difficult

access to care. A child with an educated mother is 1.7 times more likely to be vaccinated than a child

whose mother is not formally educated”.

4.2. Malagasy vaccine supply chain

Figure 4 depicts the structure of the entire Madagascar vaccine supply chain. It consists of four

levels, whose functional units include one central depot in Antananarivo, 22 regional depots, 112 district

depots and 3011 basic health centers (BHC). As can be observed in Figure 4, the central depot receives

vaccines from the manufacturers via UNICEF on a trimestral basis, which is slow according to WHO

norms. From the central warehouse the vaccines are transported to the regional level. The shipments

are conducted four times a year and are fulfilled either by cold trucks, normal trucks or planes.

Subsequently, the regional level resupplies the districts by means of motorcycles or pick-ups. A

different path is followed for the supply of 38 districts that are either close to an existing transportation

axis or that suffer from geographical isolation. These districts are supplied immediately from the central

level. Vaccine administration occurs daily at BHC level, and the number of BHCs per district ranges

from 6 to 90. An overview of the transportation routes is displayed at the right-hand side of Figure 4.

Lastly, vaccines are provided to the BHCs every month, either by the BHCs picking them up, or

by the districts delivering to them, or on the basis of an agreement between districts and BHCs. When

the BHCs are not equipped to store a month’s supply, they can pick up vaccines on a more regular basis.

Figure 5 - overview of CCE

The ratio indicates the number of times the demanded order size can be stored in respectively all the fridges, 20% of the fridges, or when only using the solar fridges. The red tinted areas indicate that the CCE is insufficient to store all vaccines demanded in a trimester, the dark blue areas imply a significant excess of CCE in the districts.


One week before transportation occurs a phone call is made to the involved warehouses, presumably to

obtain insights on current stock levels and desired order size.

Each vaccine storage location needs to be equipped with CCE in order to provide the right

circumstances to store the vaccines. Walk-in refrigerators and freezers are utilized at the central level.

The cold chain storage at central level is recorded in the official documents and amounts to 38.571,00

liters. At central level, 45% of all CCE is obsolete, at BHCs level this increases to 53%. Approximately

18% of the BHCs possess no or unrepairable CCE which puts a burden on the quality of the vaccines.

In order to enhance its CCE situation, Madagascar applied for CCE support from GAVI.

A summary of storage and transportation devices in Madagascar and their assumed net capacity

can be found in Appendix VI. Figure 5 provides an overview of the distribution of CCE in the country

relative to the CCE needs. It can be seen that, even in the case where 80% of the fridges is obsolete or

when only solar refrigerators are operational, a significant amount of excess CCE is present in numerous

districts. Nevertheless, multiple areas are identified in which the CCE is not sufficient to safely store

the vaccines needed. This indicates that little to no equipment is repaired or disposed of, and that the

data are not corrected for these events. Additionally, the uneven distribution of the equipment

throughout the country restrains the system from providing equitable access to vaccines.

There are important bottlenecks in the supply of immunization services. These include the shortage

of qualified immunization personnel, geographic inaccessibility, outages in the supply of vaccines and

the absence or non-functionality of the cold chain for the storage of vaccines, as well as the shortage of

replacement parts or the scarcity of kerosene. The operational status of the fuel powered equipment

varies from 40% to 80%. To overcome the problem of replacement parts and energy source, i.e. poor

quality of kerosene and unreliable energy grid, solar refrigerators are being introduced gradually.

Central warehouse

Regional warehouse*

District warehouse

District warehouse

District warehouse

Regional warehouse

Supply scheme does not change throughout the year. The regional and district transportation routes are not

impacted by the rain season.

Case A – all year round supply

Supply scheme is interrupted

throughout the rainy season from the

central warehouse onwards.

Case D – interrupted rain season supply

*5 regional warehouses inaccessible: Farafangana DRSP, Fenerive Est DRSP,Maintirano DRSP, Sambava DRSP,Moramanga

Stream of vaccines

Cancelled stream during rainy season

District warehouse

Direct resupply from central warehouse

cancelled throughout rainy season.

Case B - interrupted rain season supply

Regional warehouse

District warehouse

Supply scheme is interrupted

throughout the rainy season from the

regional warehouse onwards.

Case C – interrupted rain season supply

Figure 6 - Resupply cases during rainy season


A major burden for the system is the periodical inaccessibility due to the rainy season. The

geographical dispersion of the affected areas can be assessed in Appendix VII. From November to April

685 BHCs spread over 30 districts are impossible to reach, affecting approximately 6 million people.

Based on the above, four scenarios to resupply the sites were constructed (see Figure 6). Case A, in

which the routes remain unchanged is the most common scenario. Case B occurs when the direct

resupply from the central warehouse to the district level can no longer take place. This impacts 7

districts. Case C, where the regional warehouse can still be supplied, however, no further distribution

to district level is possible, affecting 11 districts. Finally, case D where even distribution to the regional

level is hindered, hits four regional warehouses and 13 districts. This study aims to identify distribution

network design changes that reduce the impact of these five months of isolation.

The challenges that have been identified throughout this section and the previous section (Section

4.1) are categorized according to the taxonomy of Tukamuhabwa (Tukamuhabwa Rwakira, 2015) as

discussed in section 2.2.3.1. An overview can be found in Table 1.

Table 1 - Categorization of identified challenges to the immunization SC in Madagascar

Threat category Examples from the immunization supply chain in Madagascar

Supply challenges Shortages on the global vaccine market causing insufficient vaccines to be distributed

to the country

Demand challenges Rurality and geographical spread of the population, rapidly growing population, poor

forecasting due to lack of data

Process challenges Obsolete CCE, shortages of replacement parts for both refrigerators and vehicles,

scarcity of kerosene

Control challenges Shortage of qualified personnel, lack of data sharing and information systems, no

adjustment for actual demand

Exogenous

challenges

Geopolitical challenges: political instability causing decreased international aid,

unfavorable investment climate leading to decreased investments in infrastructure and

industry and challenges caused by climate change and Madagascar’s geographical

situation

Economic challenges: decrease in private sector activity

4.3. Model description

A discrete-event simulation model representing the vaccine SC in Madagascar was constructed

using AnyLogic. This multimethod simulation software, developed by the AnyLogic Company, was

chosen because of its high flexibility and unlimited expansion possibilities thanks to its Java context.

The intuitive interface, clear visualization and click-and-play implementation of GIS, geographical

information systems, allowed to reduce the needed development time and technical troubles.

Nonetheless, Java knowledge was an indispensable asset throughout this research.


The model simulates the operational policies, storage facilities, transportation procedures and

resources in the Madagascar vaccine supply chain. It represents the flow of vaccines from arrival at the

central storage facility in Antananarivo, Madagascar, through each subsequent level of the supply chain,

to the district level where the basic health centers pick up their vaccines. Official documents state that

22 regional warehouses are present in the country. The model, however, only considers regional

warehouses as such when they resupply district sites. Data regarding the routine immunization planning

include 16 regional depots that match the definition. The warehouse that is assigned as regional

warehouse in the six other regions, actually functions as a district warehouse and is hence considered

as a district warehouse. An overview of the regional warehouses can be found in Appendix VIII. For

example, Miarinarivo is indicated as being a regional warehouse, however, it serves its purpose as a last

level warehouse. It does not resupply any other district; hence it is seen as a warehouse at district level.

The demanded vaccines and the target population have been adjusted to 2017 to account for an

annual 2,7% population growth rate. From the target population per district, the newborns are derived

based on an annual birth cohort of 3,38%. These are assumed to be equally spread over the BHCs and

are used as a proxy for the demand at BHC level. Throughout the simulation the supply from UNICEF,

the demand at district level and the demand from BHCs grow with the population growth rate. The used

vaccine schedule was discussed in the EVM analysis of 2014 and can be found in 0. In total 21 vaccines,

with a weighted average of 5,33 cm3 per vaccine, rounded to 5 cm3, are needed to immunize a child.

This study uses a representative vaccine instead of modelling every vaccine separately to limit the

complexity of the model. This implies that they are distributed in perfect correspondence to the ratio

required to immunize one child. Additionally, this entails that no drill down is possible on the

availability of the different vaccines in the system and that no supply shortage on the global market of

one specific vaccine can be simulated. Furthermore, when the vial sizes of the included vaccines change,

or new vaccines are introduced, the parameters of the representative vaccine should be adapted.

The representative vaccines are shipped in boxes of 100 vaccines each.

Since a box is the smallest entity in the simulation, sites can be oversupplied

by maximum 99 vaccines. Additionally, BHCs that need less than a box a

month are thus supplied one box a month. Hence, at BHC level, the

oversupply can amount to, in some cases, nearly 100%. This means that

BHCs arriving soon after a warehouse refill may end up with an oversupply,

taking away the vaccines of the BHCs that arrive later in time. An overview

of the estimated demands per BHC can be observed in Figure 7. The boxplot

indicates that for 75% of the districts the BHC demand exceeds 387

vaccines per month. That means that, in 75% of the districts, the maximum

oversupply that can occur per BHC is approximately 20%. The model does

not include corrective measures at a following resupply moment.

Figure 7 - Demand per BHC


Every storage location, fridge, and vehicle in the Madagascar vaccine SC is represented in the

model. Three types of loss are accounted for in the simulation:

• Shipping loss: vaccines wasted during transportation due to e.g. breakage on bumpy roads,

2% of all vaccines transported

• Inventory loss: loss due to insufficient CCE capacity, depends on the CCE available

• CCE breakdown loss: loss due to a CCE breakdown and insufficient back up capacity

The model includes three reasons why no full resupply occurs:

• Insufficient inventory: the site from which the resupply departs has insufficient inventory

to meet the demand

• Insufficient vehicle capacity: the vehicle that is designated to transport the vaccines does

not provide enough storage space to meet the demand

• Missing vehicle: no vehicle available to perform the required transportation

The model includes four types of transportation:

• Planes: 200 km/h, moves in straight lines

• Truck: 50km/h in dry season, 30km/h in rainy season, moves along the road network

• Pick up: 50km/h in dry season, 20km/h in rainy season, moves along the road network

• Moto: 50km/h in dry season, 20km/h in rainy season, moves along the road network

The recent implementation of a logistics management and information system (LMIS)4 as well as

the fact that a phone call is conducted a week before each shipment allow us to assume that districts are

resupplied up to a certain level. That means that if e.g. Ambalavao, that has a quota of 52.170 vaccines

per trimester, has an inventory of 42.000 vaccines at the moment that they receive the phone call, they

will receive at most 10.170 vaccines through that shipment.

Furthermore, it is assumed that when insufficient vaccines are available to meet the full shipment

needs, the available vaccines are spread relative to the individual needs of the districts served during

that shipment. The transportation route that serves Ambalavao and Ihosy DRSP is considered as

example. In line with what is agreed during the phone call, Ambalavao should receive 10.170 vaccines

and Ihosy DRSP 30.510 vaccines. This results in a total of 40.680 vaccines that are to be shipped. Let’s

assume that due to unforeseen circumstances, e.g. a canceled resupply from UNICEF, only 10.000

vaccines are left in the central warehouse in Antananarivo. This is clearly insufficient to meet the needs

of both sites; thus, the available vaccines will be assigned proportionally. That is Ambalavao will

receive 25% or, 2.500 vaccines and Ihosy DRSP the remaining 75%.

4 In 2017 the MOH introduced a Logistics Management Information System (LMIS), Malagasy Channel, an automated order tracker that enables users to send reports and requisition orders to the central warehouse. In the meantime, all regions and districts have received training and by the end of July 2017 a significant increase in data reporting has been witnessed.


When an order cannot be fulfilled, the maximum number of vaccines are transported, and the order

is not revisited until the next trimester. Furthermore, it is assumed that vehicles only leave sites during

the operational hours (i.e. Monday to Friday, 8am to 6pm). As soon as a vehicle has left the site, it

completes its shift. The same operational hours apply for the pick-up of vaccines by BHCs.

No safety stock has been implemented in the simulation. On the one hand, because no such

information was available in the obtained documents. On the other hand, because a gap analysis pointed

out that on average 8% surplus is already calculated in the order sizes, see Appendix X.

4.4. Supply chain performance indicators

First and foremost, the fill rate at each level are computed during each simulation run. For central

and regional level, the fill rate is computed both before and after selecting the means of transportation.

An overview of the calculated fill rates can be found in Figure 8. The respective fill rates are computed

according to Equations (1) to (5):

!"##%&'(&')(*'%&##(+(#,(!-%('%&*./-%'&'"-* = '-'&#-!+&11"*(.&+&"#&,#("*"*+(*'-%2

'-'&#-!+&11"*(.3(4&*3(3(1)

!"##%&'(&')(*'%&##(+(#&!'(%'%&*./-%'&'"-* = '-'&#-!+&11"*(..ℎ"//(3'-'&#-!+&11"*(.3(4&*3(3

(2)

!"##%&'(&':(;"-*#(+(#,(!-%('%&*./-%'&'"-* = '-'&#-!+&11"*(.&+&"#&,#("*"*+(*'-%2

'-'&#-!+&11"*(.3(4&*3(3(3)

!"##%&'(&':(;"-*#(+(#&!'(%'%&*./-%'&'"-* = '-'&#-!+&11"*(..ℎ"//(3'-'&#-!+&11"*(.3(4&*3(3

(4)

!"##%&'(&'>".'%"1'#(+(# = '-'&#-!+&11"*(..?//#"(3'-@A).'-'&#-!+&11"*(.3(4&*3(3,2@A).

(5)

The district level fill rate allows to estimate the number of immunized children, see Equation (6):

C) = !"##%&'(&'3".'%"1'#(+(# ∗ '&%;('/-/?#&'"-* ∗ ,"%'ℎ1-ℎ-%'(6)

To obtain more insights in the results, data with regards to missed demand and waste are kept and

analyzed. This allows to calculate average missed demand distributions and average waste distributions.

These are calculated according to Equations (7) until (11)

4"..(33(4&*3"*.?!!"1"(*'"*+(*'-%2 = '-'&#4"..(3"*.?!!"1"(*'"*+(*'-%2

'-'&#-!+&11"*(.3(4&*3(3(7)

4"..(33(4&*34".."*;+(ℎ"1#( = '-'&#4"..(34".."*;+(ℎ"1#('-'&#-!+&11"*(.3(4&*3(3

(8)

4"..(33(4&*3"*.?!!"1"(*'+(ℎ"1#(1&/&1"'2 = '-'&#4"..(3"*.?!!"1"(*'+(ℎ"1#(1&/&1"'2

'-'&#-!+&11"*(.3(4&*3(3(9)


I&.'("*.?!!"1"(*'.'-%&;(1&/&1"'2 = '-'&#I&.'("*.?!!"1"(*'1&/&1"'2'-'&#-!+&11"*(.3(4&*3(3

(10)

I&.'())K,%(&L3-I* = '-'&#I&.'())K,%(&L3-I*'-'&#%(1("+(3+&11"*(.

(11)

It is obvious that there is a direct relation between the fill rate level before transportation and the

missed demand due to insufficient inventory (Equation (12)). Additionally, the difference between fill

rate before transportation and the fill rate after transportation is directly linked with sum of missed

demand due to insufficient vehicle capacity and missed demand due to missing vehicle (Equation (13)):

1 − !"##%&'(,(!-%('%&*./-%'&'"-* = 4"..(33(4&*3"*.?!!"1"(*'"*+(*'-%2(12)

!"##%&'(,(!-%('%&*./-%'&'"-* − !"##%&'(&!'(%'%&*./-%'&'"-*

= 4"..(33(4&*3"*.?!!"1"(*'+(ℎ"1#(1&/&1"'2

+ 4"..(33(4&*34".."*;+(ℎ"1#((13)

Storage and vehicle utilization are calculated per node. This allows to assess the efficiency and

bottlenecks of the model. They are calculated according to Equations (14) and (15):

.'-%&;(?'"#"O&'"-*-+(%'"4( = '-'&#*?4,(%-!+&11"*(."*"*+(*'-%2

P/(%&'"-*&#))K1&/&1"'2(14)

+(ℎ"1#(?'"#"O&'"-*/(%-%3(% = '-'&#*?4,(%-!+&11"*(..ℎ"//(3

)&/&1"'2-!1ℎ-.(*'%&*./-%'&'"-*4('ℎ-3(15)

Finally, some cost elements are calculated during the full duration of the simulation (Equations

(16) and (17)). The transportation cost uses the traveled distance, the fuel price5, consumption rate per

vehicle and an extra 15% maintenance cost as a percentage of the fuel cost (WHO, 2014). The cost of

all wasted vaccines is calculated based on a weighted average cost per vaccine based on the cost price

as provided by UNICEF (UNICEF, 2016). This resulted in a cost per vaccine of € 2,37. Since no data

was available on personnel, no HR costs have been taken into account. The other cost elements such as

personnel, depreciations, etc. are assumed to remain constant, hence no further calculations are required.

'%&*./-%'&'"-*1-.'

= Q 3".'&*1('%&+(#(3RSTUVWS ∗ 1-*.?4/'"-*RSTUVWS ∗ !?(#/%"1(RSTUVWSX

∗ (1 + 4&"*'(*&*1(%)(16)

I&.'&;(1-.' = Q #-!I&.'(3+&11"*(. ∗ I(";ℎ'(3&+(%&;(-!+&11"*(1-.'XU[SX

(17)

5 Diesel price of € 0,85, as retrieved from https://www.globalpetrolprices.com/diesel_prices/#hl80, consulted on April 9, 2018


Not included in model

Used as a proxy for immunization

coverage

Missed demand due to limited vehicle capacity or missing vehicle at central

level

Missed demand due to limited vehicle capacity or missing vehicle at regional

level

Service level beforetransportation

Depends on inventory at central levelMissed demand due to insufficient

inventory

Central level storageService level before

transportationDepends on inventory at regional level

Missed demand due to insufficient inventory

Regional level storageImmunization coverage

BHC levelService level BHC

Depends on inventory at district level

District level storage

Central level metrics Regional level metrics District level metrics

Service level aftertransportation

Depends on vehicles capacity/availability

Level 1 transportationService level after

transportationDepends on vehicle capacity/availability

Level 2 transportation

Gap due to outgoing transportation constraints

Gap due to outgoing transportation constraints

BHC level

Vaccines lost during transport (2%)

Vaccines lost during transport (2%)

Vaccines lost due to insufficient capacity or

CCE breakdown


CCE breakdown


CCE breakdown

Figure 8 - Overview of fill rates


4.5. Base case analysis

The current situation in Madagascar is simulated in the base case. It is assumed that vaccines are

distributed in boxes containing 100 vaccines each, and that UNICEF supplies 3.844.625 vaccines each

trimester, corrected on an annual basis with a population growth rate of 2,7%. All types of fridges are

included (solar, electrical, and fuel refrigerators). Due to the poor operational status of the CCE as

discussed in the EVM Analysis of 2014, 80% of the fridges are assumed to be obsolete. Each fridge has

a capacity of 156 liters. Level one transportation (i.e. planes and trucks) can each carry 54 cold boxes

(one cold box equals 20,7 liters) and level two transportation, i.e. pickups and moto’s, can carry 12 cold

boxes and one cold box respectively.

In the following paragraphs the fill rate at district level is discussed first. Next, the central and

regional level are further assessed by comparing their fill rate before transportation and the fill rate

after. Thirdly, more insights are given in the reasons why demand is missed, followed by an assessment

of the main causes of waste throughout the system and an overview of the storage utilization at the

warehouses. Finally, a more thorough stock out assessment is provided which clearly illustrates the

impact of the rainy season on the availability of vaccines.

Firstly, the simulation of the base case suggests a fill rate of 64% at district level (Figure 9). This

means that they can immunize 530.000 children (immunized children is further referred to as IC) on an

annual basis. There is, however, a significant deviation between the fill rate of the districts that are

affected by the rainy season and those that are not, which is

further referred to as the equity gap. For those affected by the

rainy season the fill rate is as low as 49%, whereas for the other

districts it amounts to 71% (that is, an equity gap of 22%)

(Figure 9).

Since the model does not include the storage at BHC level

neither the immunization of people itself, no real immunization

coverage can be obtained. As a consequence, the fill rate at

district level is used as a proxy for the immunization coverage.

This choice implies that the numerous steps that are required to

finally reach the person in need (e.g. storage at BHC level,

transportation to BHC level, reaching the population and

creating awareness, the availability of personnel, …) are

ignored. Hence, one expects this proxy to be an overestimation

of the actual immunization coverage. The obtained fill rate is

lower than the immunization coverage reported in official WHO

data, that is a national immunization coverage of 65% in 2015

and 74% in 2016 (WHO, 2017a). However, it is important to

Figure 9 – Performance at district level Left: fill rate - Right: number of IC


note that these data are questionable since they are challenged by other sources (WHO, 2017a). A model

rerun where the share of obsolete fridges only amounts to 50% resulted in a fill rate at district level of

72% which is closer to what can be found in the official sources (see Appendix XI). Nevertheless, it

was opted for to continue with the worst-case scenario due to the specific country information. This

choice is backed up by the fact that the model only includes routine immunization and no immunization

campaigns, hence, a part of the administered vaccines is not captured.

In Section 4.1, it was described that “Children living in the Itasy region, a more affluent region, are

3.4 times more likely to be vaccinated than children living in the Menabe region” (Van Den Ent et al.,

2017). The simulation confirms this gap. On average 10% of all vaccines demanded by the BHCs in the

Itasy region is missed, while a depressing 54% for the Menabe region (Figure 16).

Secondly, at central as well as at regional level, two fill rates are computed (Figure 10). The first

fill rate, “fill rate before”, indicates the demand that can be met from inventory. A deviation from 100%

indicates insufficient inventory at the resupply site. The second fill rate, “fill rate after”, indicates how

many vaccines are actually shipped from the resupply site. The difference between the two denotes the

missed demand due to transportation problems, i.e. missing vehicles or insufficient vehicle capacity

(see Figure 8). At central level, this gap is 25%, whereas, at regional level it is resp. 5 and 14%.

The second part of Figure 10 discusses the reasons why demand is not met. This differs relative to

the level in the SC and has already been hinted at when considering the difference between the two

calculated fill rates. At central level, the simulation suggests that the main reason why demand is not

fulfilled is insufficient vehicle capacity. At regional level, however, it is mainly due to insufficient

inventory, resp. 30% and 28%, and in lesser extent due to insufficient vehicle capacity, resp. 5 and 11%.

It is remarkable that missing vehicles do not stress the system whatsoever. Only at rain-impacted

regional level an impact of 3% is observed. A possible explanation can be that in the obtained data an

abundance of vehicles was available, and no vehicle breakdown was included in the model.

Additionally, at regional level, when no pick up is available, but a motorcycle is used instead, this

missed demand is considered as insufficient vehicle capacity.

The observable issue with vehicle capacity is also reflected in vehicle utilization numbers (Figure

11). The utilization of plane storage capacity is on average 96%, with a median of 100%6. A similar

result is seen for truck transportation with an average of 81% and a median of 88%. The motorcycles’

capacities, too, are well used with an average capacity utilization rate of 82% and a median of 95%.

Pick up transportation is less congested with an average capacity utilization of 59 and a median of 57%.

The estimated total transportation costs in the system augment to approximately € 0.43 Million every

year which is in line with what was calculated for a GAVI eligible sample country (Lee et al., 2015).

6 This model only includes one route that is conducted by planes, i.e. from the central warehouse to Sambava DRSP. The vaccines demanded by Sambava DRSP exceed the plane capacity, which explains the high utilization rate numbers.


The histograms in Figure 12 provide a rough sense of the distribution of the average fill rates. At

district level, 79% of the sites that are not affected by the rainy season score above 50% fill rate, whereas

for the districts that are affected by the rainy season this is merely 48%. It is interesting to see that at

regional level; no such deviation is present. The distribution of fill rates is relatively similar between

the two zones, with the bulk of districts, respectively 83% and 75%, with a fill rate of 50% or above.

Next, vaccines are also wasted throughout their journey. This loss is mainly due to the breakdown

of cold chain equipment, and to a lesser extent due to insufficient cold chain capacity (Figure 13). It is

apparent that the impact of failing CCE is significantly higher when insufficient cold chain capacity is

available in the system. Hence, it is important to consider the two metrics as a whole. At central level,

2% of all vaccines are lost due to storage related issues. At regional level this amounts to a total of 15%

for the non-affected sites and 23% for the rainy-season affected sites, and at district level respectively

10% and 9%. In total 172 thousand boxes out of 811 thousand boxes are wasted throughout the

simulation, i.e. 21%, which is significantly lower than the 50% loss that is reported in some countries

(Sabot, Yadav, & Zaffran, 2010). This can partially be due to the fact that no vehicle breakdowns were

modelled. Additionally, the crucial and often challenging parts related to the last mile delivery were

excluded from the model. Furthermore, the communication between the sites prior to delivery, reduces

waste due to insufficient capacity, since no orders exceeding the available CCE capacity are accepted.

Another discussion element in the analysis of the base case is the storage utilization, see Figure 14.

At central level, an average of 67% can be observed. This indicates that the warehouse has excess

capacity that allows them to cope with the foreseen population growth. The utilization rate drops further

downstream the supply chain. The histograms in Figure 14 provide insights in the variation between

the unique data points. At regional level, the bulk of the sites, 56%, have an average storage utilization

between 25% and 50%. At district level, storage utilization is even lower, 88 of the 112 districts, or

78%, have a utilization rate lower than 25%. This is significantly lower than the 67% utilization rate

recommended by the WHO (WHO, 2017b). This indicates that either they are oversized, or the vaccines

do not reach the facilities.

Lastly, the impact of the rainy season on the system as a whole is further assessed. This has already

been partially discussed by comparing the rainy-season affected sites with those that are not affected.

The simulation assumes that the districts that are inaccessible during the rainy season are not resupplied

whatsoever in this period of time. The base simulation does not include any adaptive measures to

overcome the stress this causes to the system since no indications for such measures were found in the

data. However, demand from BHCs is not canceled in this period of time. This assumption was made

since children are still born in this period, hence immunization should still take place at BHC level.

A stock out assessment (Figure 17, Figure 18 and Table 2) provides an overview of the moments

at which stock outs occur. The assessment contains the last 1000 days of the simulation of which 356

rainy days and 644 dry days. All 16 regional warehouses and 97 of the 112 districts suffer occasional


stock outs. At regional level, stock outs are perceived during 25% of the days during the dry season and

throughout 24% of the days during the rainy season. In the dry season, the regional warehouses in rainy-

season affected areas experience considerably fewer days with stock outs. This can be explained by the

fact that they are well equipped to store their requested vaccines. In the rainy season, the regional

warehouses that are not affected experience less stock outs than during the dry season, since there are

more vaccines available for their resupply. At district level, sites suffer stock outs on average 30% of

the time during the dry season and 39% during the rainy season. There is, however, a big difference

between the sites that are affected by the rainy season and those that are not. Whereas, the latter ones

run out of stock only in 26% of the days throughout the rainy season, this is 71% for the affected sites.

Moreover, throughout the dry season the affected sites have an increased share in days with stock out.

The results clearly indicate that the rainy season puts stress on the system. At district level, a

significant gap in fill rate can be observed between the rainy-season affected sites and those that are

not. This gap is caused by the fact that there is a significant increase in stock out days because vaccines

are blocked either at central or at regional level and cannot reach their destination. Additional challenges

the system has to cope with are CCE breakdown and limited vehicle capacity. To improve the current,

slow, cold chain the trade-off between adding redundancy and adding flexibility, as stated in Section

2.2.4.3, is evaluated by means of a scenario analysis. The following sections consist of discussing and

assessing scenarios which potentially could alleviate the burden of the rainy season on the distribution

of vaccines in Madagascar.

Figure 10 - Fill rate at central and regional level

5%

3%


Figure 11 - Vehicle utilization

Figure 12 - Histograms average fill rate


Figure 13 - Sources of waste in the system

Figure 14 - Storage utilization


Figure 15 - Stock out assessment geographical overview

Figure 16 - Stock out assessment geographical overview - Itasy and Menabe region


Table 2 - stock out assessment

AVERAGE % OF STOCK OUT DAYS DURING DRY SEASON

AVERAGE % OF STOCK OUT DAYS DURING RAINY SEASON

Central 0% 0%

Not affected 0% 0%

Region 25% 24%

Not affected 31% 24%

Affected 7% 24%

District 30% 39%

Not affected 27% 26%

Affected 38% 71%

Figure 17 - Stock out assessment – Regional Left: stock out pattern – Right: number of days with stock out


Figure 18 - Stock out assessment - Districts


4.6. Studied scenarios

The constructed simulation model is used to evaluate different scenarios for dealing with the burden

of the rainy season. The strategies chosen, and their respective effects are analyzed over a limited time

horizon of five years in order to remain as close as possible to reality. This limits the impact of wild

assumptions and predictions about the future. A short time horizon allows to assume (1) no major

infrastructure changes will occur (e.g. electricity grid, road infrastructure, mobile interconnectedness);

(2) no major population displacements; (3) no major changes in vaccine delivery technologies; (4) no

changes in the immunization package. Additionally, these constraints limit the changes that are possible,

justifying scenarios that remain close to the situation as is today. The scenarios use existing locations

and transport routes and do not have the luxury to choose any location to build a new warehouse or

establish a new transport route.

As discussed in Section 2.2.4.3, a tradeoff exists between a flexible and a rigid SC when designing

a distribution network. It was stated that in cases where health facilities are inaccessible during several

months of the rainy season, a rigid, or a “slow cold chain” (i.e. a SC relying on cold generating

equipment and storage rather than speed and transportation capacity) is preferable. These facilities

would then receive enough vaccines and supplies to cover the entire period of inaccessibility (WHO,

2008). Sheffi stresses that flexibility is more important than redundancy in order to obtain a resilient

supply chain. This simulation is used to test possible definitions of a rigid SC and aims to determine

which rigid regime is flexible enough to cope with the inaccessibility.

The base case is compared to scenarios that differ in three possible ways as can be observed in

Figure 19. Firstly, redundancy is added by means of a buffer in rainy-season affected areas that builds

up throughout the year in order to bridge the supply/demand gap during the rainy season. The impact

of this buffer is tested by increasing the requested number of vaccines in those areas by resp. 50 % and

100 %. Since this is purely an administrative change and holding costs are assumed to be equal at each

level of the supply chain, no extra costs are expected. Secondly, flexibility is introduced by resupplying

the warehouses at resp. a bimonthly and monthly pace rather than at a trimestral pace. It is clear that

100 % Buffer:

100 % Buffer:

Bimonthly0 % Buffer: 50 % Buffer:

Monthly0 % Buffer:. 50 % Buffer:

Increased buffer

1 - Increased buffer

2 -I

ncre

ased

freq

uenc

y

Base case

C D E

F G

A B

No extra cost

Renewal costs

3 - Improved CCE in rainy season affected areas

H

Increased transportation costs

100 % Buffer:

100 % Buffer:

Base RSCCE

Bimonthly0 % Buffer: 50 % Buffer:

Monthly0 % Buffer:. 50 % Buffer:

Increased buffer

I J K

L M N

O P Q

No extra cost



Figure 19 - Design of tested scenarios


this will increase the cost of transportation significantly since respectively 1,5 times and three times

more trips are planned. The model made abstraction of any personnel costs and fuel is assumed to be

available at all time. Thirdly, another redundancy measure is evaluated, i.e. the influence of an improved

CCE base in rainy-season affected areas. In this scenario it is assumed that only 50% of their CCE is

obsolete. To achieve this, 30% of the current CCE needs to be repaired or replaced, which is 193 fridges.

Due to the lack of information with regards to the presence of maintenance teams for repair, it is

assumed that new CCE needs to be bought. This results in an extra investment of approximately 1

Million euros7. According to the WHO guidelines this investment is depreciated linearly over the CCE’s

useful life years, i.e. 5 years (WHO, 2004). This results in an annual cost of 200.000 euros.

The complex interplay of vaccine storage and supply frequency is further assessed by combining

the different scenarios with each other. In total 17 scenarios are tested against the base case. Their

performance is compared on three levels. First and foremost, the scenario should improve the situation

in all areas of the country in terms of immunization coverage, i.e. fill rate at district level. This means

that no improvement in the rainy-season affected areas can be realized at the expense of the other areas.

Secondly, the chosen scenario should ensure a more equitable distribution. Thirdly, the additional waste

and transportation costs are calculated and compared. Finally, the scenarios that are considered as

‘interesting’ based on the aforementioned waterfall, are further evaluated by means of a stock out

analysis. Figure 20 summarizes the key metrics for the preferred scenarios.

4.7. Results

This section follows the structure as set out in Section 4.6. Firstly, the fill rates at district level and

equity gaps are evaluated. Next, a cost analysis of the preferred scenarios is carried out. Hereafter, a

stock out assessment is conducted of the best performing scenarios.

The base case suggested a fill rate at district level of 64%, with an equity gap of 22% between the

rainy-season affected areas (with a fill rate of 49%) and the rest (with a fill rate of 71%). This provides

the means to immunize 530.000 children on an annual basis (see Figure 21). The total waste at central

level is 3%, at regional level 19% and at district level 10% (see Figure 22). At central level, the dominant

reason for missed demand is insufficient vehicle capacity, i.e. 25%, and at regional level, it is primarily

due to insufficient inventory, i.e. 29% (see Figure 23). The total estimated annual cost of the base case

augments to 449.000 euros (see Figure 24).

When keeping the current CCE, the simulation suggests that at a trimestral resupply pace, the two

alternative buffer scenarios, i.e. A and B, improve the rainy-season affected areas at the expense of the

7 In total there are 644 fridges in rainy-season affected areas, of which only 20% or 128 are operational. In order to improve to a 50% operational level, 193 fridges need to be bought or renewed. It is assumed that these are of the type TCW 3000 SDD with a unit price of 5167,00 € (Unicef, 2017). This results in a total cost of € 997 231,00.


other areas (Figure 21). Furthermore, the buffer implies that vaccines are kept at lower levels of the

cold chain. This jeopardizes the vaccine supply chain’s ability to cope with events such as refrigerator break

down, resulting in an increase in vaccines wasted in rainy-season affected areas by approximately 10% in

both scenarios (Figure 22, from 24% to 33% in both cases). Additionally, transportation bottlenecks

due to insufficient vehicle capacity are more pronounced, increasing from 25% in the base case to resp.

27% and 33% in the 50% buffer scenario and the 100% buffer scenario (Figure 23). Due to the poor

performance of the scenarios A and B, they are eliminated for further investigation.

If the decision-makers in Madagascar would decide to switch to a bimonthly resupply system,

considerable improvements can be observed. The scenario in which a 100% buffer is incorporated, i.e.

E, has the biggest improvement with regards to the district fill rate. It increases the overall fill rate by

8% and the equity gap decreases to 14%. In this scenario, 607.400 children can be immunized, that is

77.400 extra (Figure 21). The increased frequency reduces the number of vaccines transported per trip,

which alleviates the transportation bottlenecks caused by insufficient vehicle capacity. For the 100%

buffer case, for example, a decrease in demand missed due to insufficient vehicle capacity from 33% to

10% can be observed (Figure 23). Additionally, vaccines move faster through the chain, which means

they spend less time in storage, resulting in decreasing waste (Figure 22). Since scenarios without a

buffer or with a 50% buffer, i.e. C and D, do not perform considerably better, they are eliminated for

further investigation.

The Malagasy authorities could also opt to resupply each month. Here the simulation suggests that

the scenario without a buffer, i.e. F, is noticeably better than both the base case and the scenarios with

buffer, i.e. G and H. Hence, scenario G and H are eliminated for further investigation. Scenario F

enables 156.900 additional children to be immunized resulting from a 13,2% increase in district fill rate.

However, the equity gap also increases significantly and equals 32%, i.e. 10% more than in the base

case (Figure 21). For that reason, scenario F, too, is eliminated for further investigation.

The second half of Figure 21 shows the results of the scenarios in which the CCE in the rainy-

season affected areas is renewed, i.e. only 50% is obsolete (in the figures referred to as renewed CCE).

As discussed in Section 4.6, this comes at an extra investment of approximately 1 Million euros. Similar

to when no investments were made, the trimestral scenarios do not manage to simultaneously improve

both the affected and non-affected areas. So, scenarios I, J and K, are excluded too. In the bimonthly

case important improvements of the fill rates can be appraised. The case in which a buffer of 100%, i.e.

N, is applied beats the others, i.e. scenario L and M, with an increase in fill rate of 11.7%, a decrease in

equity gap to merely 7% and reaching an extra 121.500 children (Figure 21). The waste rates at all three

levels are brought down (Figure 22). Strikingly, at regional level the transportation bottlenecks are

slightly more pronounced than in the same case without additional CCE, i.e. from 15% to 22%. This

can be explained by looking at the ordering policy. Sites can never order more than their CCE allows.

Hence when increasing CCE, order sizes increase, which can lead to transportation bottlenecks.


Finally, when choosing for a monthly resupply pace, the case with 100% buffer, i.e. Q, outperforms

the other two, i.e. scenarios O and P. The overall fill rate increases to 80% with an equity gap of 17%

and 171.400 additional children can be immunized. No significant waste is present and missed demand

due to insufficient vehicle capacity is reduced to 0% at central level and to 7% at regional level.

Overall the simulation suggests that increasing frequency results in better vaccine availability.

Figure 20 restates the scenarios that perform best. It is important to match these with the additional

costs they provoke (see Figure 24). The cost per IC is calculated, as well as, a cost per immunized child

adjusted for the decrease in equity gap, i.e. that is giving more weight to children vaccinated in rainy-

season affected areas, see Table 3. It indicates that no investment in CCE is seen as efficient when

looking at the total increase in reached children only8. However, the focus of this master dissertation is

to overcome the burden of the rainy season. Hence it is important to adjust for a decrease in equity gap.

Since 1,32 euros and 1,60 euros are smaller than 1,92 euros, a bimonthly scenario with 100% buffer is

preferred over a monthly, i.e. E and N (Figure 20). These are further assessed in a stock out analysis to

see the effect on the vaccine availability throughout the year.

The output of the stock out analysis, see Table 4, confirms that the introduction of the buffer and a

bimonthly resupply system improves the availability of vaccines at district level. Whereas at central

level, a considerable increase in stock out days can be appraised. An overall decrease is suggested at

district level from 30% to resp. 27% and 24% in days with stock outs during the dry season and from

39% to resp. 35% and 32% during the rainy season. Furthermore, a significant enhancement can be

observed for the rainy season affected districts. These were the troublemakers in the base case with on

average 38% of the days during the dry season and 71% of the days during the rainy season on which

no vaccines were available at the site. The alternative scenarios have improved this to 29% during the

dry season and 59% during the rainy season, or even 22% and 53% when also investing in CCE.

8 The cost per IC is larger for the improved CCE scenarios than for the non-improved scenarios.

Figure 20 - Comparison table preferred scenarios


4.8. Discussion

The findings of this research confirm Sheffi’s theory that to obtain a resilient supply chain

redundancy measures and, even more important, flexibility should be implemented. Additionally, they

are also in line with the WHO recommendations to implement a slow, rather than a fast, cold chain. The

simulation suggests that opting for a bimonthly resupply pace combined with a buffer build-up in the

rainy season impacted areas reduces the impact of the rainy season on the availability of vaccines.

In the first place, Sheffi (Sheffi & Rice Jr, 2005) stressed the importance of introducing redundancy

to create a resilient supply chain network. Such as, keeping some reserve stock to be used in case of a

disruption. Our research shows indeed that the implementation of the buffer decreases the equity gap

significantly. However, the increased vehicle capacity requirements and storage requirements further

downstream associated with increase in order sizes limit the potential of this approach. They result in

more missed demand due to limited vehicle capacity and increased waste at intermediary levels in the

supply chain. As a consequence, the overall improvement of this redundancy is rather small.

Secondly, redundancy can also be achieved by expanding storage capacity. The simulation suggests

that relieving storage constraints does not guarantee an improved fill rate at district level. The results

indicate that enhanced CCE in the rainy-season affected areas lessens the waste caused by storage issues

significantly while increasing the missed demand due to insufficient vehicle capacity.

Haidari et al., too, reported on this important tradeoff between storage capacity and transportation.

They state that relieving storage constraints exacerbates existing transportation bottlenecks (Haidari et

al., 2013).

Thirdly, Sheffi stated that there is significantly more leverage in making supply chains flexible

than there is in adding redundancy (Sheffi & Rice Jr, 2005). Haidari et al confirms this by declaring

that increasing transportation frequency, and thus increasing flexibility, alleviates capacity constraints

both in transportation and storage. This was also observed in this research. Flexibility implies that fewer

vaccines need to be transported at once and fewer vaccines need to be stored at the locations. This

induces decreased waste in storage at regional level and decreased missed demand due to insufficient

vehicle capacity. The fewer vaccines that need to be transported at once, open up room in the vehicles

to implement the buffer policy. This way also the equity gap is reduced.

Fourthly, resilience comes with additional effort and costs. The costs related to each scenario turn

out to be higher per immunized child than the base case. Although the monthly resupply pace is very

attractive, the higher costs associated with it cannot be justified. Even more so because the costs

estimated in our model are an underestimation of the total cost, given that some flexibility aspects of

the monthly resupply are beyond the scope of our model. That is, each resupply trip carries a risk of

vaccine breakage, mishandling, or temperature exposure. Additionally, more frequent trips result in an

increase in total distance traveled and time spent on the road for the drivers and ultimately transportation

costs. Furthermore, the managerial implications of getting the right vehicle in the right condition at right


time at right place with a certified driver as well as the availability of fuel and spare parts are key. A

bimonthly pace, on the other hand, is only 7% more expensive9, it reaches 15% more children, and by

implementing the buffer policy the equity gap is reduced significantly. This alone enables the system

to be more adaptable towards potential future increase in volume that will flow through the system.

As described in Section 2.2.2, a vaccine SC is not profit driven and the objective is to serve all

areas, despite limitations in infrastructure and resources. In our opinion, the bimonthly case with a 100%

buffer and an investment in CCE is the preferred scenario. This scenario allows for 23% more children

to be immunized, a reduction in equity gap to only 7% and a reduction in days with stock out in both

seasons. Admittedly, the total cost is 71% higher than the base case. However, one has to realize that

this is only 36 eurocents per immunized child. The macro-economic benefits of immunizing more

children are wide-ranging and are difficult to overestimate.

The WHO stated that in cases where health facilities are inaccessible during several months of the

rainy season, a more rigid cold chain is preferable over a fast supply chain (WHO, 2008). Sheffi stresses

the importance of flexibility and to a lesser extent redundancy. This simulation considered three

possible definitions of a rigid SC and aimed to determine which rigid regime is flexible enough to cope

with the inaccessibility. It was found that a resupply period of only two months increases the fill rate at

district level by 12% while decreasing the equity gap by 15% for 36 eurocents more per child. It can

thus be seen that the current SC in Madagascar is too rigid, and some degree of flexible system yields

significant improvements.

4.9. Contribution and limitations of this research

The insights of this research contribute to both literature and practice. Firstly, the findings confirm

Haidari et al. who studied the Niger vaccine SC by the means of a HERMES simulation model. They

stated that relieving storage constraints exacerbates existing transportation bottlenecks and that

increasing transportation frequency alleviates capacity constraints. This is also what is suggested by the

results of this research. Secondly, the results are in line with Sheffi who states that there is significantly

more leverage in making supply chains flexible than there is in adding redundancy and it adds to the

WHO advice that “slow” cold chains are preferred in rainy-season affected areas. This research

investigates several options of “slow” supply chains and as such provides more detailed insights into

the degree of flexibility and redundancy that is required in the design of the SC for vaccines in

Madagascar. Thirdly, this research provides insights for the practitioners (managers in NGOs and policy

makers) helping them to improve the vaccine SC and as such reach more children in need of

vaccinations. Fourthly, the dataset constructed from several different sources, as discussed in Section

9 The adjusted cost per IC is used to compare the scenarios. Hence, 1,32 euros is 6,6% more than 1,24 euros.


3.1.3, allows to simulate the vaccine SC in Madagascar, which had not been done before. It is also

available now for further research.

Models, by definition, are simplifications of real life and cannot capture every possible factor,

relationship, or outcome. Constructing the model involved substantial data collection from a wide

variety of sources. Some data (e.g. CCE and available transportation modes per site, vehicle capacities,

transportation costs, travel speed, etc.) may be less reliable because reporting does not always match

reality. Moreover, the simulation model did not account for random events such as vehicle breakdown,

unavailable personnel and warehouse demolition. The model did not consider additional resources

needed with new vaccine introduction such as personnel, vaccine accessories (e.g. safety equipment),

and potential changes in the network. Furthermore, the estimate of population demand for vaccines was

based on birth-rate data. The actual demand at each BHC was unknown. Finally, due to limited

knowledge of GIS implementation in Anylogic it can be that the followed routes deviate from the

existing routes. When a route cannot be found, a straight path is followed from origin to destination.

Even though the findings are in line with what Haidari et al concluded, future investigations are

necessary to validate the conclusions that can be drawn from this research. Firstly, while this study

examined the effects of augmenting only in-country transport, increasing the frequency of shipments to

the country might also have positive implications for vaccine availability. Secondly, field research can

allow to refine the parameters used in the simulation model, decreasing the number of assumptions

required. Moreover, locally obtained information would allow enhanced costs estimations. Thirdly,

numerous extensions are possible to the simulation model, ranging from further GIS implementation

and adapting the model for dynamic disruptions and rerouting, to including personnel requirements and

vehicle breakdowns. Additionally, it might prove interesting to include the last mile steps in the

simulation such that the whole journey from manufacturer to the people in need is captured. Finally,

more scenarios can be tested including alternative means of transportation and the impact of innovations

such as drones on the distribution of vaccines in Madagascar.


Table 3 – Cost per IC

BASE 3M BUFFER 100 2M

(E)

BUFFER 100 2M CCE (N)

BUFFER 100 1M CCE (Q)

Total IC 530.000

607.400 +15%10

651.500 +23%

701.300 +32%

Not affected 431.400 479.400 497.600 551.300

Affected 98.500 128.000 153.900 150.100

Total cost 449.000 €

569.000 €

+27%

767.000 €

+71%

961.000 €

+114%

Cost per IC 0,85 € 0,94 € 1,18 € 1,37 €

Adj. cost per IC11 1,24 €

1,32 € +7%12

1,60 € +29%

1,92 € +55%

Table 4 - Stock out assessment of chosen scenarios

BASE 3M BASE 3M

BUFFER 100 2M

(E)

BUFFER 100 2M

(E)

BUFFER 100 2M

CCE (N)

BUFFER 100 2M

CCE (N)

Avg Dry % Avg Rainy % Avg Dry % Avg Rainy % Avg Dry % Avg Rainy %

Central 0% 0% 24% 11% 13% 6%

Not affected 0% 0% 24% 11% 13% 6%

Region 25% 24% 27% 15% 26% 17%

Not affected 31% 24% 30% 16% 33% 22%

Affected 7% 24% 18% 13% 3% 0%

District 30% 39% 27% 35% 24% 32%

Not affected 27% 26% 27% 25% 24% 23%

Affected 38% 71% 29% 59% 22% 53%

Grand Total 29% 37% 27% 32% 24% 30%

10 % increase in total immunized children relative to the base scenario

11 Cost per IC is calculated giving each immunized child the same weight, i.e. 50%. For example, for the bimonthly 100% buffer case, €0,94 = €()*.,,,

-∗(,,(∗01*.0,,2,,(∗3-4.,,,). The adjusted cost per immunized child is calculated by giving the additional immunized children in the rainy-season affected areas a weight of 75% and those in non-affected areas a weight of 25%. The same example is used to illustrate the calculations, €1,32 = €()*.,,,

-∗(,,-(∗01*.0,,2,,1(∗3-4.,,,).

12 % increase in adjusted cost per IC relative to the base scenario


22%

+ € 120k

+ € 317k

+ € 512k

F

A B

EDC

HG

KJI

NML

QPO

A B

EDC

HGF

KJI

NML

QPO

14%

BASE BASE

Figure 21 - Overview of population reached and fill rate at district level for the different scenario’s


Figure 22 – Overview of waste per scenario

FFF

19%

10%

2%

13%

8%

4%

20%

14%

4%

14%

10%

4%

19%

15%

6%

14%

9%

3%

10%

9%

7%

9%

8%

7%

11%

10%

8%

8%

7%

6%

9%

7%

8%

8%

6%

10%

A B

EDC

HG

KJI

NML

QPO

A B

EDC

HG

KJI

NML

QPO

A B

EDC

HG

KJI

NML

QPO

BASE BASE BASE


Figure 23 – Overview of fill rates and missed demand per scenario

25%

F

A B

EDC

HG

KJI

NML

QPO

A B

EDC

HGF

KJI

NML

QPO

A B

EDC

HGF

KJI

NML

QPO

A B

EDC

HGF

KJI

NML

QPO

10%

BASE

BASE

BASE

BASE


A B

EDC

HGF

KJI

NML

QPO

BASE

Figure 24 – Overview of transportation, wastage and improved CCE costs per scenario


5. Conclusions and insights “If vaccines are the solution, then why aren’t children receiving them?” a thought provoking

question to which this master dissertation tried to formulate an answer and - more importantly - a

solution in the case of Madagascar. Vaccines can get stuck somewhere along their journey for numerous

reasons, one of which the periodical inaccessibility due to the rainy season. This leads to stock outs at

lower levels of the supply chain. Due to the relatively slow pace of the current supply chain in

Madagascar substantial numbers of vaccines per resupply trip are required which clearly congests the

system. The limited vehicle capacity as well as the low number of operational CCE create bottlenecks

and prevent vaccines from flowing smoothly from manufacturer to the people in need.

This thesis had the aim to explore the challenges to immunization supply chains and strategies to

relieve the congested system. After exploring the state of the art literature, two seemingly opposite

recommendations were found. The WHO, on the one hand, advocates for a more rigid cold chain, that

is, a cold chain in which supplies cover more than a month. Sheffi, on the other hand, stresses the

importance of flexibility. Hence, the question was raised how slow the cold chain could be to remain

flexible enough.

The research points out that a shift towards a bimonthly resupply pace, together with the

implementation of a buffer policy, which anticipates missed resupply moments due to isolation,

alleviates the stress on the system. The combination of the two policies improves the immunization

coverage substantially and reduces the equity gap with regards to vaccine availability between the rainy-

season affected areas and the rest. More important improvements are found when also the CCE is

partially renewed. This main finding is in line with the conclusions Haidari et al made for the Niger

vaccine supply chain. This solution, however, comes at a cost estimated at 36 eurocents per immunized

child. Yet, the macro-economic benefits of immunizing approximately 120.000 extra children are far-

reaching. Even though, this is more expensive than the base case, decision-makers in Madagascar

should further investigate the feasibility of increasing the flexibility of their current immunization

distribution network.

This research contributes to both literature as well as practice. It provides more insights in the

impact of recurring disruptive events on an immunization supply chain, along with strategies to improve

the situation. Moreover, it can help policy-makers in Madagascar to reassess and improve the current

immunization SC to reach more children and reduce the equity gap between the different districts. As

such, this research contributes to the philosophy of Seth Berkley, CEO of GAVI, the best vaccine

imaginable is only valuable to the extent we get it to everyone who needs it.


Acknowledgement

After an intensive period of learning, not only on an academic level but also on a personal level,

the moment has come to finish up my dissertation and to write this note of gratitude. First of all, I would

like to thank my supervisor, Catherine Decouttere, for the guidance, encouragement, advice and

knowledge sharing. I have been extremely lucky to have a supervisor who was so engaged and who

responded to my questions so promptly. I would also like to thank my promotor, Professor dr. Vandaele,

for the excellent cooperation and for the opportunities I was given to conduct my research.

Secondly, I would like to thank Andry Fidele Ravalitera, my contact at UNICEF Madagascar, for

providing me with the essential data to successfully conduct this research. I want to thank Jo Tierens

and Gino Vleugels from The Janssen Pharmaceutical Companies of Johnson & Johnson for providing

me with their insights regarding immunization supply chains. Karel Van Roey from Goal, responsible

for Janssen’s Ebola vaccine trial in Sierra Leone for discussing the differences between a public and a

private immunization supply chain, as well as, providing me with insights on how the situation is in the

field. In addition, I would like to thank Jef Molenaers, sales manager for pharma transportation at H.

Essers, for explaining the ins and outs of cold chain transportation.

Thirdly, a special appreciation goes to my parents. My mom, Ann Vereecke, who gave me

constructive feedback, crash courses in SC management, plentiful insights in correct academic writing

and the patience of dealing with me throughout this process. My dad, Patrick Dewilde, who was always

willing to remove and insert comma’s, correct my typos and entertain me with typical dad jokes.

Finally, I would like to thank my fellow students, my friends and my family for their patience, wise

counsel, feedback, sympathetic ear and the much-needed occasional escapes from my research.


Appendix I. Structure of literature review

Appendix II. A cold chain as defined by UNICEF


Appendix III. Five capabilities of a resilient system

DESCRIPTION THEMES IN SUPPLY CHAIN RESILIENCE

Ability to anticipate Proactive capabilities necessary to

identify and monitor potential events,

changing environments, and performance

before the ability of the supply chain to

function is affected

Proactively plan, anticipate risk, prepare,

resist, avoid and be alert

Ability to adapt Concurrent capabilities required to

manage and adjust critical supply chain

resources continually during disruptions

and/or normal business activities

Cope with unexpected disturbance or

change, absorb/withstand/reduce impact,

tolerate, adapt

Ability to respond Concurrent capabilities needed to react to

supply chain events on time and

efficiently, to lessen the impact of

disruptions or change the effects to ensure

a desirable outcome

Maintain control, retain structure and

function, react, change rapidly and

respond

Ability to recover Reactive capabilities essential in the

aftershock of a supply chain event, so as to

restore or return to normal operations

Survive, maintain continuity, bounce

back, return to original/normal state, move

to new/desirable state, recover, restore

quickly, in timely fashion, and cost-

effectively and resume operations

Ability to learn Reactive capabilities required after a

supply chain event to understand what has

happened and improve future performance

based on the experience

Sustain, growth, thrive, evolve, future

adjustments and profitability


Appendix IV. Overview of areas affected by recurring environmental challenges

Appendix V. Overview of immunization coverage over time


Appendix VI. CCE equipment per supply chain level

Supply chain

level

Stationary storage Equipment

Quantity Avg net volume

Transportation Equipment

Quantity Avg net volume

Central Cold room (5)

Refrigerator (12)

Total of 38.571,00

litres

Refrigerated trucks (2)

Depi trucks (3)

Rental planes or trucks

1.118 liters

1.118 liters

1.118 liters

Regional Solar fridges (134)

Electrical fridges (72)

Fuel fridges (235)

Vaccine carrier (3.983)

156 liters

156 liters

156 liters

[0,8; 3,6] liters

Pick-ups (42)

Moto (70)

Boat (2)

248 liters

21 liters

/

District Solar fridges (134)

Electrical fridges (72)

Fuel fridges (235)

Vaccine carrier (3.983)

156 liters

156 liters

156 liters

[0,8; 3,6] liters

Pick-ups (337)

Moto (448)

Boat (14)

248 liters

21 liters

/

Appendix VII. Accessibility during the rainy season

April

Jan May June

Feb Mar Aug Sep Oct July Nov Dec April

Jan Feb Mar


Appendix VIII. Overview of regional warehouses

Appendix IX. Malagasy immunization schedule Vaccine Quantity Volume Vaccine Quantity Volume

HPV13 1 12 cm3 BCG14 1 1 cm3

IPV15 1 2 cm3 PCV-1016 3 12 cm3

VAR17 1 3 cm3 DTC-HepB-Hib18 3 3 cm3

ROTA19 2 16 cm3 VPO20 4 1 cm3

VAT21 5 3 cm3 TOTAL 21 5 cm3

13 Human Papillomavirus vaccine

14 Tuberculosis vaccine

15 Inactivated Poliovirus vaccine

16 Pneumococcal Conjugate vaccine

17 Measles vaccine

18 Diphtheria and tetanus toxoids and whole-cell pertussis, Hepatitis B, Haemophilus influenza type b vaccine

19 Rotavirus vaccine

20 Oral Polio vaccine

21 Tetanus vaccine


Appendix X. Order size analysis


Appendix XI. Results of simulation run with 50% obsolete fridges

Performance at district level Left: fill rate – Right: number of IC

Fill rate at central and regional level

Sources of waste in the system


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