Exploring the application of blockchain to humanitarian supply chains: insights from Humanitarian Supply Blockchain pilot project

Purpose – Some studies and reports have recently suggested using blockchain technology to improve transparency and trust in humanitarian supply chains (HSCs). However, evidence-based studies to display the utility and applicability of blockchains in HSCs are missing in the literature. This paper aims to investigate the key drivers and barriers of blockchain application to HSCs and explore whether evidence could support that the application of blockchain improves transparency and trust in HSCs. Design/methodology/approach – This paper puts forward a two-stage approach to explore the blockchain application in HSCs: an initial exploration of humanitarian practitioners and academicians interested in blockchain through focus group discussions; semi-structured interviews with practitioners involved at the UK Department for International Development’s Humanitarian Supply Blockchain pilot project. Findings – First, we found that main drivers include accountability, visibility, traceability, trust, collaboration, time efficiency, reducing administrative work and cross-sector partnership. Main barriers, however, are composed of engagement issues, lack of technical skills and training, lack of resources, privacy concerns, regulatory problems, pilot scalability issues and governance challenges. Second, evidence from our case study revealed the blockchain application could have added value to improve visibility and traceability, thus contributing to improve transparency. Concerning trust, evidence supports that blockchain could enhance both commitment and swift trust in the pilot study. Practical implications – Our study contributes to a more understanding of added values and challenges of blockchain application to HSCs and creates a perspective for humanitarian decision-makers. Originality/value – This study provides the first evidence from the actual application of blockchain technology in HSCs. The study discovered that it is still less evident in many humanitarian organizations, including medium- and small-sized nongovernmental organizations, that they engage in a direct deployment of in-house or customized blockchain-based HSC. Instead, these actors are more likely to indirectly use blockchain in HSCs through a private commercial partner.acceptedVersio

Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters: A case study of the 2015 Nepal earthquake

International audienceIn the humanitarian response, multiple decision-makers (DMs) need to collaborate in various problems, such as locating temporary relief distribution centres (RDCs). Several studies have argued that maximising demand coverage, reducing logistics costs and minimising response time are among the critical objectives when locating RDCs after a sudden-onset disaster. However, these objectives are often conflicting and the trade-offs can considerably complicate the situation for finding a consensus.To address the challenge and support the DMs, we suggest investigating the stability of non-dominated alternatives derived from a multi-objective model based on Monte Carlo Simulations. Our approach supports determining what trade-offs actually matter to facilitate discussions in the presence of multiple stakeholders. To validate our proposal, we extend a location-allocation model and apply our approach to an actual data-set from the 2015 Nepal earthquake response. Our analyses show that with the relative importance of covering demands, the trade-offs between logistics costs and response time affects the numbers and locations of RDCs considerably. We show through a small experiment that the outputs of our approach can effectively support group decision-making to develop relief plans in disasters response

Defining and measuring the network flexibility of humanitarian supply chains: insights from the 2015 Nepal earthquake

The efficient and effective response to disasters critically depends on humanitarian supply chains (HSCs). HSCs need to be flexible to adapt to uncertainties in needs, infrastructure conditions, and behavior of other organizations. The concept of ‘network flexibility’ is, however, not clearly defined. The lack of an unanimous definition has led to a lack of consistent understanding and comparisons. This paper makes a threefold contribution: first, it defines the concept of network flexibility for HSC in the context of sudden onset disasters. Second, it proposes a framework to measure network flexibility in HSCs. Third, we apply our framework to the 2015 Nepal earthquake case and provide evidence-based insights regarding how humanitarian organizations can improve network flexibility in HSCs. Our analyses for Nepal case show that delivery, IT support, and fleet criteria have the most influence on flexibility. Also, the application of our framework on the downstream network of nine humanitarian organizations shows low levels of network flexibility in all but one. This finding explains why several disruptions happened in relief distributions during the Nepal response.Published VersionNivå

Managing in-country transportation risks in humanitarian supply chains by logistics service providers: Insights from the 2015 Nepal earthquake

Humanitarian supply chains (HSCs) play a central role in effective and efficient disaster relief operations. Transportation has a critical share in HSCs and managing its risks helps to avoid further disruptions in relief operations. However, there is no common approach to or culture of risk management that its applicability has been studied through recent cases. This paper incorporates an empirical research design and makes a threefold contribution: first, it identifies in-country transportation risks during Nepal response. Second, we evaluate afore identified risks through an expert driven risk assessment grid. Third, we use our field data to study how some humanitarian organizations in Nepal response used logistics service providers for managing moderate- and high-level transportation risks. In this paper, we use both qualitative and quantitative methods. Our qualitative analysis reveals that some of the most important in-country transportation risks were delivery delays; market fluctuations; insufficient capacity; loss of cargo; cargo decay; unreliable information; and ethical concerns. Our quantitative work shows that while participants categorized the first three risks as high-level, the rest were ranked as moderate-level. More investigation in our field data indicates that using logistics service providers (LSPs) helped humanitarians significantly to manage afore in-country transportation risks during Nepal response. It also improved overall HSC performance with respect to flexibility, effectiveness, efficiency, and responsiveness. While this finding empirically confirms the “tools” role of LSPs for managing in-country transportation risks in response, it implies another role for LSPs; “contributors” to performance improvements.acceptedVersionnivå

How Can Authorities Support Distributed Improvisation During Major Crises? A Study of Decision Bottlenecks Arising During Local COVID-19 Vaccine Roll-Out

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CONTRA Project report #1: Requirements identification and system mapping

The research project “COVID-19 Network Technology-based Responsive Action” (CONTRA), funded by the Research Council of Norway, commenced in June 2020. The CONTRA project develops a decision support system (DSS) based on mathematical modeling and stochastic optimization, and machine learning tools for designing a robust COVID-19 vaccine distribution network. The project follows two main objectives within two phases. In response to the on-going COVID-19 outbreak, rapid analyses will provide actionable advice to public health authorities in Norway regarding vaccine distribution and delivery to responders. This phase involved a systematic study of vaccine distribution system actors in Norway and their decision-making needs. Based on such insights, the project will develop a DSS based on mathematical models to support designing the vaccine distribution network. The DSS should contribute to the effectiveness, efficiency, equity, and sustainability of the COVID-19 vaccine distribution. The proposed solution will also support vaccine distribution in future pandemics. The report describes the results of the first work package (WP) in the CONTRA project. The WP1 aims to identify the key actors in the vaccine distribution network in Norway, map their relation to each other, and distinguish critical decisions in the system. Moreover, the report presents an overview of related research on vaccine distribution networks, related decision support systems, and the progress in the literature about the COVID-19 pandemic. Through preliminary interviews, document review, and a workshop with multiple representatives from Norwegian public health authorities, the current vaccine distribution system is analyzed, and its actors have been mapped. This system map is the basis for further discussion both within the project team and with stakeholders. It should be noted that this map will change throughout the project due to the additional insights from other validation opportunities and the fact that the COVID-19 context is dynamic and is changing permanently. However, the system map has served as a basis for the problem definition in the CONTRA project. Based on our findings from the stakeholder workshop and system mapping, we have decided to focus on defining and studying the central vaccine allocation problem (CVAP), which is faced by Public Health Institute (FHI). As such, the CONTRA will investigate the problem of determining the amount of each vaccine to be shipped to every municipality. CVAP is challenged by the scarce amount of vaccines, the current immunization level, population, and priority groups in each municipality. In our project, CVAP will be formulated as a multi-objective resource allocation problem. Specifically, we will define and formulate objectives related to the following performance dimensions: efficacy (e.g., total coverage, coverage per priority group, etc.), efficiency and sustainability (e.g., logistics costs, waste), and fairness (e.g., distribution of efficacy among municipalities). The next step in the project will be to validate the problem definition and develop the mathematical model (second work package). Moreover, two individual reports for the actors map and system map will be published in the upcoming months by project partners.submittedVersionacceptedVersionpublishedVersionpublishedVersio

Toward a decision support system for COVID-19 vaccine allocation inside countries

The distribution of COVID-19 vaccines has proved to be a challenging task for public health authorities in many countries. Among several decisions involved in the task, allocating limited available vaccines to administration points is indeed critical. However, the operation management literature lacks evidence-based mathematical models that could support effective, efficient, sustainable and equitable vaccine allocation decision. This paper develops the fundamentals of a decision support system for COVID-19 vaccine allocation inside countries. The proposed DSS intends to support public health authorities in real-time by illustrating possible vaccine alternatives. The system could also inform and support other actors in the COVID19 distribution for planning and collaboration. Two illustrative cases for the COVID-19 vaccine allocation have been investigated to highlight potential benefits of our methodology

A Mathematical Model for Equitable In-country COVID-19 Vaccine Allocation

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Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters

International audienceLocating distribution centers is critical for humanitarians in the immediate aftermath of a sudden-onset disaster. A major challenge lies in balancing the complexity and uncertainty of the problem with time and resource constraints. To address this problem, we propose a location–allocation model that divides the topography of affected areas into multiple layers; considers constrained number and capacity of facilities and fleets; and allows decision-makers to explore trade-offs between response time and logistics costs. To illustrate our theoretical work, we apply the model to a real dataset from the 2015 Nepal earthquake response. For this case, our method results in a considerable reduction of logistics costs