Humanitarian Supply Chain Research Articles

A Decision Support Model for Barriers and Optimal Strategy Design in Sustainable Humanitarian Supply Chain Management

Sustainable humanitarian supply chain (SHSC) management enables effective and efficient responses to natural and human-made disasters. Existing literature falls short of offering decision support (DS) models to address the barriers and strategies to designing SHSC. To this end, this study develops a DS model that identifies and prioritizes the barriers to SHSC and determines optimal strategies for mitigating those barriers. This study adopted both qualitative and quantitative approaches. As part of the qualitative approach, a field study was applied using in-depth interviews to determine the barriers and corresponding strategies, while under the quantitative approach, a quality function deployment (QFD) integrated optimization technique was used to prioritize barriers and determine optimal strategies to mitigate the SHSC barriers. The study found that a lack of contingency planning, the prevalence of corruption and political interference, and a lack of social and environmental awareness are the most important barriers, while logistics outsourcing, supply chain (SC) performance management, and SC flexibility are the most essential strategies. We also found that our DS model is highly flexible and can be adapted under different scenarios, which makes the model applicable to different contexts. This study has a significant contribution to literature and practice. We developed a novel decision model that captured cost savings and leveraged both cost and time savings from interrelated strategies to determine the best optimal strategy while applying QFD-integrated optimization modeling. The paper’s findings will assist humanitarian SC managers in designing an effective, efficient, and sustainable humanitarian SC.

A scenario-based game theory integrating with a location-allocation-routing problem in a pre- and post-disaster humanitarian logistics network under uncertainty

PurposePre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.Design/methodology/approachIn this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.FindingsManagers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.Originality/valueThe model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Comparative study of robust optimization model of multimaterial emergency logistics in an uncertain environment

AbstractGiven the significance of objective functions in alleviating human suffering within the humanitarian relief supply chain, it is imperative to consider both logistics costs and the cost of deprivation as ideal targets. This study investigates a multimaterial single‐cycle stochastic mixed integer model, integrating uncertainties about post‐disaster demand, supply survival, and road networks. An innovative heuristic approach is used to solve the stochastic facility location problem, which minimizes unfulfilled requests and response times in lexicographic order. This research also compares the cost‐minimization model of the pre‐positioning problem to an alternative model that maximizes demand satisfaction under resource constraints. Empirical validation through numerous case studies and solutions confirms the effectiveness and feasibility of the alternative model. The results of our experiments consistently demonstrate that the best solution provided by the alternative model not only satisfies the minimum cost model but also surpasses the conventional problem, without compromising performance. Consequently, it is crucial to avoid the cost‐minimization approach at the expense of human suffering.

Reliable design of humanitarian supply chain under correlated disruptions: a two-stage distributionally robust approach

Reliable design of humanitarian supply chain under correlated disruptions: a two-stage distributionally robust approach

Designing a sustainable-resilient humanitarian supply chain for post-disaster relief process, an earthquake case study in Haiti

Purpose Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief. Design/methodology/approach A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations. Findings A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained. Originality/value Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Design and management of humanitarian supply chains for pandemics: lessons from COVID-19

The COVID-19 pandemic has been an unprecedented challenge for humanity, causing widespread disruptions to daily life and economies worldwide. However, amidst the challenges, it has also presented an opportunity to reflect on our competencies, capabilities, and objectives. One area that has gained significant attention during this time is the humanitarian supply chain management theories, which offered significant directions to the scholars engaged in operations and supply chain management, practitioners and policymakers. To further advance the theoretical boundaries of the humanitarian supply chain and expand the scope and boundaries of the discipline, a special issue has been organised. This issue includes 24 carefully selected articles that have gone through multiple rounds of review. The articles are all centred on the humanitarian crisis resulting from COVID-19 and offer a comprehensive understanding of the challenges faced by various stakeholders. Additionally, one relevant article from a previous issue has been included, which explores the potential use of drones in disaster relief operations. The articles included in this special issue have significant implications for theory, practice, and policy, providing valuable insights and direction for further research to expand the scope of the discipline. This particular issue is a crucial contribution to humanitarian supply chain management. It provides a deeper understanding of the complex challenges faced during a crisis and the best practices to overcome them.

The Role of Artificial Intelligence in Supply Chain Agility: A Perspective of Humanitarian Supply Chain

The present study aims to establish a link between digital and humanitarian supply chain management. This study has focused on using artificial intelligence-big data analytical capabilities and information alignment to develop and maintain supply chain collaboration to achieve supply chain agility in a dynamic environment like a disaster. The targeted population is humanitarian organizations in Pakistan. Simple random sampling method, data was collected from 242 respondents using an online questionnaire. The Partial Least Square – Structural Equation Modelling technique has been used for analysis. Resource Based Theory and Contingency Theory in this study have provided foundations to develop and test the relationships among information alignment, supply chain agility, artificial intelligence – big data analytical capabilities, and supply chain collaboration in disaster management. Findings showed the use of artificial intelligence – big data analytical capabilities are beneficial for information alignment and supply chain agility.

Leveraging Industry 4.0 Technologies for Sustainable Humanitarian Supply Chains: Evidence from the Extant Literature

Prevailing and exacerbating impacts of climate change call for robust and resilient humanitarian supply chains (HSCs). To that end, intelligent technologies that brought about the Industry 4.0 (I4.0) revolution, such as the Internet of Things, blockchain, and artificial intelligence, may tremendously impact the optimal design and effective management of HSCs. In this paper, we conduct a systematic literature network analysis and identify trends in I4.0 and HSCs. We posit the need to instill into current HSC efforts the quadruple bottom-line (cultural, economic, environmental, and social) pillars of sustainability and define a Sustainable Humanitarian Supply Chain (SHSC). Based on the extant literature and ongoing practice, we highlight how I4.0 technologies can aid SHSC stages from disaster risk assessment to preparedness to response to relief. The complex nature of SHSCs requires a holistic and multidisciplinary approach and collaboration by scholars, policymakers, and industry practitioners to pool solution resources. We offer future research venues in this fledgling but life-saving scientific discipline. SHSCs can be empowered with I4.0 technologies, a much needed direction in our climate-changed world.

A bi-objective mixed-integer non-linear programming model with Grasshopper Optimization Algorithm for military-based humanitarian supply chains

Military systems have many components, such as garrisons, border guards, and industries with a large population that may be affected by disasters. Garrisons, hospitals, clinics, and other facilities in military systems are needed to reduce human casualties. However, these facilities may not have enough resources to handle the disruption. There is no mathematical model to manage disasters with military systems including garrisons and border guards as relief forces interacting with non-military medical centers. This study develops a model for coordinating humanitarian supply chains and logistics within the military system. The affected military sites (AMSs) are military hospitals, clinics, industries, airports, military organizational settlements, garrisons, and border guards. A bi-objective mixed integer non-linear programming model is developed to minimize total costs during pre- and post-disaster planning, a weighted average of injury time in military vehicles, and total delay in relief items (RIs) distribution and rescue operations. For small and medium test problems, GAMS software is used, and for large test problems, the Grasshopper Optimization Algorithm (GOA) is proposed. We conducted some numerical examples to demonstrate the proposed model’s efficiency and feasibility and evaluate the metaheuristic. Sensitivity analyses are conducted to evaluate model behavior under various conditions.

The Role of Supply Chain Finance in Humanitarian Aid Relief : Literature Review

Humanitarian aid relief has been operational since the late 19th century, addressing development and disaster aid cases. The intricate relationship between humanitarian aid relief and the supply chain encompasses various stages, from securing funding to coordinating stakeholders. However, the volatile and uncertain nature of the field poses challenges such as corruption, prolonged funding gaps, and demands for accountability and visibility. This study highlights the critical role of funding and financing in the humanitarian supply chain, emphasizing the underdeveloped state of research on supply chain finance (SCF). The paper aims to investigate the integration of SCF into humanitarian aid relief, utilizing literature reviews from journals, papers, and reports.The findings reveal a correlation between SCF's essential elements—focused on liquidity/cash, operational activities, and engagement among actors. This research proposes a framework, how the humanitarian aid relief sequence and SCF theory should collaborate for effective humanitarian aid supply chain finance (HASCF) practices

Application of industry 4.0 technologies for mitigating humanitarian supply chains risks: the case of Covid-19

ABSTRACT The study aims at integrating and combining successful digital tools such as Blockchain technology, Internet of Things (IoT), and Cloud Computing (CC) to digitalise HSC. To show the enabling of these digital tools, we first conducted a real case study in collaboration with a major leader in real estate diagnostics and building technical data. Second, to mitigate the impact of various disturbances and risks of COVID-19 on supply chain (SC) performances, we modelled two logistics networks, i.e. before and after the COVID-19 pandemic. Finally, we proposed two efficient solutions. In the first solution, we proposed a risk mitigation matrix model based on the cause-effect diagram. In the second one, we developed an advanced digital model which is completely based on Industry 4.0 technologies. Results showed that the new digital model was more efficient since it enables to enhance HSC performances by mitigating risks and enabling tracking, monitoring, facilitating coordination, real-time information sharing, and flexibility during humanitarian operations.

Geospatial Tools and Remote Sensing Strategies for Timely Humanitarian Response: A Case Study on Drought Monitoring in Eswatini

This article explores the escalating impact of natural disasters, particularly droughts, in the Southern African Development Community (SADC), with a specific focus on Eswatini. Over the last century, approximately 63 million people in SADC countries have been affected by droughts, leading to challenges in agriculture, livestock losses, and severe food and water shortages. Despite being the smallest SADC nation, the Kingdom of Eswatini faces disproportionate consequences due to its susceptibility to climate variability, particularly drought. The inadequacy and unreliability of rainfall have resulted in a drastic reduction in food production, with maize, a staple crop, experiencing a 70% decline. This adverse trend, spanning three decades, has heightened the vulnerability of farmers to climatic shocks, hindering sustainable agricultural development and impeding poverty alleviation efforts. To address the growing threat of drought in the kingdom, a comprehensive approach is imperative, involving coordinated plans and the development of swift humanitarian relief strategies. This study utilized remote sensing technologies to monitor drought and assess its repercussions, evaluating the impact on agricultural production. Additionally, geospatial tools, including Open Route Service (ORS) and Near Neighbor Analysis algorithms, were employed to optimize humanitarian supply chain logistics. Results from the analysis, including Vegetation Health Index (VHI) fluctuations and drought severity mapping, reveal that 1990 was the year the kingdom was most severely hit by drought. This study also found that smallholder farmers practicing rainfed agriculture in vulnerable regions, such as the lower Middleveld and western Lowveld, suffered the severe socioeconomic consequences of agricultural drought, including income loss, food insecurity, and migration patterns. Through this integrated approach, decision makers can engage in targeted interventions, focusing on farming areas needing irrigation infrastructures or populated areas requiring a coordinated humanitarian response amidst climate variability.

Supporting disaster relief operations through RFID: enabling visibility and coordination

PurposeIn the aftermath of disasters, humanitarian actors need to coordinate their activities based on accurate information about the disaster site, its surrounding environment, the victims and survivors and the supply of and demand for relief supplies. In this study, the authors examine the characteristics of radio frequency identification (RFID) technology and those of disaster relief operations to achieve information visibility and actor coordination for effective and efficient humanitarian relief operations.Design/methodology/approachBuilding on the contingent resource-based view (CRBV), the authors present a model of task-technology fit (TTF) that explains how the use of RFID can improve visibility and coordination. Survey data were collected from humanitarian practitioners in India, and partial least squares (PLS) analysis was used to analyze the model.FindingsThe characteristics of both RFID technology and disaster relief operations significantly influence TTF, and TTF predicts RFID usage in disaster relief operations, providing visibility and coordination. TTF is also a mediator between the characteristics of RFID technology and disaster relief operations and between visibility and coordination.Social implicationsThe many recent humanitarian disasters have demonstrated the critical importance of effective and efficient humanitarian supply chain and logistics strategies and operations in assisting disaster-affected populations. The active and appropriate use of technology, including RFID, can help make disaster response more effective and efficient.Originality/valueHumanitarian actors value RFID technology because of its ability to improve the visibility and coordination of relief operations. This study brings a new perspective to the benefits of RFID technology and sheds light on its antecedents. The study thus expands the understanding of technology in humanitarian operations.

Logistics in Humanitarian Supply Chains

Logistics in Humanitarian Supply Chains

Stochastic linear programming model for the shelter’s location in small Colombian cities

This article addresses the problem of locating temporary shelters in small Colombian cities through the construction of a stochastic linear programming model that considers different scenarios where the affected population is a factor that generates uncertainty in the location of shelters and the respective flows in the humanitarian supply chain. The model is validated through the construction of a case study referring to the floods presented in a city in the center of the department of Valle del Cauca, which constantly causes the need to relocate the affected population, seeking to reduce attention times to the victims while considering restrictions associated with the budget available for emergency care. The generated proposal becomes a benchmark for the efficient management of shelters in similar disasters in small Colombian cities.

Analysis and assessment of humanitarian supply chain barriers for disaster and crisis management using hybrid approach

Analysis and assessment of humanitarian supply chain barriers for disaster and crisis management using hybrid approach

Designing a humanitarian supply chain network in dynamic conditions for transferring of relief items under demand uncertainty

Designing a humanitarian supply chain network in dynamic conditions for transferring of relief items under demand uncertainty

ERP Systems in Humanitarian and Private Sectors’ Supply Chains: Challenges and Success Factors

Purpose: This paper investigates and compares challenges and success factors within different supply chain ERPs used globally across humanitarian and private organizations in Africa, Asia, Canada, Australia, Europe, and the Americas. Eighteen challenges and 27 success factors were selected from literature published between 2015 and 2020 to determine whether they are equally relevant globally in the private and humanitarian sectors. Design/methodology/approach: The research utilized an anonymous online questionnaire advertised on different social media websites and completed by 50 humanitarian supply chain professionals and 53 private sector professionals worldwide. The collected data was analyzed using a descriptive statistic–crosstabulation analysis to show the differences or similarities in supply chain professionals’ opinions from humanitarian and private organizations. Additionally, the hypotheses were tested by using the Mann–Whitney Test. Findings: Findings revealed that all the examined success factors were supported except one, which was similar in both sectors. However, the challenges during the implementation of ERPs differ in these two sectors — with four success factors not supported in the humanitarian sector and nine not supported challenges in the private sector. Originality: This study’s significance is that, as per the researchers’ knowledge, such a comparative study was never done before, and it will allow both sectors’ professionals to understand all the elements mentioned above better and integrate them while implementing supply chain ERPs.

Operations research approaches for improving coordination, cooperation, and collaboration in humanitarian relief chains: A framework and literature review

Given the considerable number of actors in the humanitarian space, coordination is essential for successful disaster response. Furthermore, the sheer size of challenges and limited resources increasingly highlight the need for improved cooperation and collaboration in humanitarian supply chains. A significant number of studies in the literature explore the 3Cs (coordination, cooperation and collaboration), using conceptual, empirical and analytical methods. This paper aims to provide an overview and analysis of the Operations Research (OR) approaches that support decision making for improved 3Cs in humanitarian relief chains and to identify future research directions. To achieve this aim, we first present a holistic view of the discussions in the literature and derive a conceptual framework for 3C mechanisms in humanitarian operations. Based on our framework, we analyse studies that develop OR methods to address the design and management of 3C mechanisms in humanitarian relief chains. We also identify current gaps and future research directions.

Model and solution of sustainable bi-level emergency commodity allocation based on type-2 fuzzy theory

The allocation of emergency commodities is very important for efficient emergency response after a disaster. In this study, a sustainable multi-objective bi-level emergency commodity allocation model is established, providing a practical emergency commodity allocation strategy for humanitarian supply chains. First, type-2 fuzzy variables are used to describe uncertain transportation costs and handle them based on the conditional value-at-risk (CVaR) theory of type-2 fuzzy variables. Second, Karush-Kuhn–Tucker (KKT) conditions with complementary constraints are used, auxiliary variables are introduced, and global criteria are applied to achieve the equivalent transformation of the model. Finally, with the Wenchuan earthquake in China in 2008 as an example, we prove the necessity of introducing the CVaR for risk measurement and the practicability of using type-2 fuzzy variables to describe uncertain parameters by comparing this scenario with a risk-neutral situation and the proposed model with a model based on type-1 fuzzy theory. The novelties of this study include considering both the government and suppliers are decision-makers, exploiting CVaR to measure tail risk and using type-2 fuzzy theory to transform uncertain parameters. The contributions of this study include considering sustainability during the rescue process and proposing a CVaR formula based on type-2 theory.