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The purpose of this paper is to identify how coordination has evolved in humanitarian logistics (HL), what were the triggers for change and how have they been facilitated.

This paper applies a systematic literature review of academic journals.

This is the first paper to discuss the concepts of network orchestration and choreography in a humanitarian context. The research revealed that network coordination has moved on in the commercial sector to include orchestration and now, choreography concepts which have not been tested in HL literature. This reveals a lag exists between HL research and practice.

This paper represents an exploratory study and provides the basis for further research on the concepts of orchestration and choreography in HL. The paper sets a research agenda for academics.

This paper is the first to discuss the concepts of network orchestration and choreography in a humanitarian context.

The areas of orchestration and choreography have received limited consideration within the humanitarian aid logistics literature to date. This paper is designed to redress this shortfall. As a result, it is hoped that it will act as a catalyst for further research and to widen and deepen the resultant debate with a view to improving the outcome for those affected by current and future disasters.

The rising threat of tropical cyclones in Zimbabwe is of great importance in establishing the general sources of humanitarian supply chain risks and assessing their negative impact on relief operations. There is a scarcity of studies that collate such evidence toward enhanced humanitarian supply chains in Southern Africa. With this in mind, this study explored scattered evidence on supply chain risks in the delivery of humanitarian aid to victims of Cyclone Idai in Zimbabwe.

This reflective study evaluates supply chain risks associated with Cyclone Idai humanitarian relief operations through qualitative in-depth interviews with relevant actors in the field. The data were triangulated with secondary information from associated publications, blogs and newspapers to reflect the truth about the phenomena under investigation.

The results show that Cyclone Idai disaster response operations were adversely affected by social, economic and political/governmental risk factors. In the same breath, poor or inadequate infrastructure and environmental factors were also contributing factors toward the futility of humanitarian relief operations.

This study is significant as it endeavors to contribute toward humanitarian supply chain management, specifically in assisting humanitarian organizations with suggested strategies that would work toward making humanitarian relief supply chains more resilient. However, more research needs to be done toward optimized implementation strategies for the suggested framework.

It is to the best knowledge of these researchers that this is a unique study carried out to examine humanitarian supply chain risk factors in Cyclone Idai relief operations in Zimbabwe.

The purpose of this paper is to examine the impact of cloud computing (CC) use on collaboration and its ultimate impact on the agility of humanitarian supply chains. Further, this paper aims to analyze the moderating role of inter-organizational trust in the relationship between CC use and collaboration.

This paper provides an empirical assessment of CC use based on an interview analysis of 19 individuals from humanitarian organizations. A survey questionnaire is later used with 107 participants from US relief organizations. Partial least squares test is used to examine the relationships depicted in the conceptual model.

The results provide an account of how CC is used in a humanitarian context. Further, the results indicate that CC use has a positive and significant impact on collaboration between humanitarian organizations and their suppliers. Collaboration is found to be significantly positively associated with agility in humanitarian organizations.

No study, to the best of the authors’ knowledge, has empirically assessed the impact of CC use on humanitarian supply chain collaboration. This will be the first study to empirically analyze the relationships between CC use, inter-organizational trust, collaboration and agility in a humanitarian context.

This study provides a theoretically and empirically validated model depicting the relationships between CC use, collaboration, agility and inter-organizational trust in humanitarian supply chains. Humanitarian organizations can use these findings to optimize agility.

This study contributes to supply chain management research, particularly humanitarian supply chain management knowledge, by empirically examining the usefulness of CC use on collaboration and agility in the supply chain.

The purpose of this paper is threefold: first, to investigate the role of big data and predictive analytics (BDPA) and social capital on the performance of humanitarian supply chains (HSCs); second, to explore the different performance measurement frameworks and develop a conceptual model for an HSC context that can be used by humanitarian organizations; and third, to provide insights for future research direction.

After a detailed review of relevant literature, grounded in resource-based view and social capital theory, the paper proposes a conceptual model that depicts the influence of BDPA and social capital on the performance of an HSC.

The study deliberates that BDPA as a capability improves the effectiveness of humanitarian missions to achieve its goals. It uncovers the fact that social capital binds people, organization or a country to form a network and has a critical role in the form of monetary or non-monetary support in disaster management. Further, it argues that social capital combined with BDPA capability can result in a better HSC performance.

The proposed model integrating BDPA and social capital for HSC performance is conceptual and it needs to be empirically validated.

Organizations and practitioners may use this framework by mobilizing social capital, BDPA to enhance their abilities to help victims of calamities.

Findings from study can help improve coordination among different stakeholders in HSC, effectiveness of humanitarian operations, which means lives saved and faster reconstruction process after disaster. Second, by implementing performance measurements framework recommended by study, donors and other stakeholders will get much desired transparency at each stage of HSCs.

The findings contribute to the missing link of social capital and BDPA to the existing performance of HSC literature, finally leading to a better HSC performance.

Humanitarian services usually perform in the face of uncertainty in which mobilization of resources in an efficient and effective manner is a big challenge. Sharing timely and correct information among logistics partners and workers is a key to drive rapid response logistics effectively. The purpose of this paper is to understand how coordinated effort effects resources management (RM).

This study uses quantitative research methodology and collected data from 82 humanitarian workers dealing with logistical activities from a densely populated city of Pakistan. Data were then statistically analyzed through partial least squares–structural equation modeling.

The results suggest that the success of humanitarian supply network depends upon the level of trust among the partners, which accelerates commitment through strong coordination. Information sharing reduces behavioral uncertainty and enhances swift trust (ST). ST then helps to improve coordination and commitment from all stakeholders in order to manage resources to lead effective relief operations.

The study guides the practitioners and relief operations’ policy makers to lay emphasis on distributing right and timely information flow among the partners, which can lead to effective, efficient and swift humanitarian relief operations.

This study on RM during humanitarian logistics is well timed in the context of developing country with high uncertain events, improper infrastructure and very limited resources.

The number of natural and man-made disasters is remarkable and threatened human lives at the time of occurrence and also after that. Therefore, an efficient response following a disaster can eliminate or mitigate the adverse effects. This paper aims to help address those challenges related to humanitarian logistics by considering disaster network design under uncertainty and the management of emergency relief volunteers simultaneously.

In this paper, a robust fuzzy stochastic programming model is proposed for designing a relief commodity supply chain network in a disaster by considering emergency relief volunteers. To demonstrate the practicality of the proposed model, a case study is presented for the 22 districts of Tehran and solved by an exact method.

The results indicate that there are many parameters affecting the design of a relief commodity supply chain network in a disaster, and also many parameters should be controlled so that, the catastrophe is largely prevented and the lives of many people can be saved by sending the relief commodity on time.

This model helps decision-makers and authorities to explore optimal location and allocation decisions without using complex optimization algorithms.

To the best of the authors’ knowledge, employee workforce management models have not received adequate attention despite their role in relief and recovery efforts. Hence, the proposed model focuses on the problem of managing employees and designing a disaster logistics network simultaneously. The robust fuzzy stochastic programming method is applied for the first time for controlling the uncertainties in the design of humanitarian relief supply chains.

The purpose of this paper is to understand how big data and predictive analytics (BDPA), as an organizational capability, can improve both visibility and coordination in humanitarian supply chains.

The authors conceptualize a research model grounded in contingent resource-based view where the authors propose that BDPA capabilities affect visibility and coordination under the moderating effect of swift trust. Using ordinary least squares regression, the authors test the hypotheses using survey data collected from informants at 205 international non-government organizations.

The results indicate that BDPA has a significant influence on visibility and coordination. Further, the results suggest that swift trust does not have an amplifying effect on the relationships between BDPA and visibility and coordination. However, the mediation test suggests that swift trust acts as a mediating construct. Hence, the authors argue that swift trust is not the condition for improving coordination among the actors in humanitarian supply chains.

The major limitation of the study is that the authors have used cross-sectional survey data to test the research hypotheses. Following Guide and Ketokivi (2015), the authors present arguments on how to address the limitations of cross-sectional data or use of longitudinal data that can address common method bias or endogeneity-related problems.

Managers can use this framework to understand: first, how organizational resources can be used to create BDPA, and second, how BDPA can help build swift trust and be used to improve visibility and coordination in the humanitarian supply chain.

This is the first research that has empirically tested the anecdotal and conceptual evidence. The findings make notable contributions to existing humanitarian supply chain literature and may be useful to managers who are contemplating the use of BDPA to improve disaster-relief-related activities.

The recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.

This paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.

Among various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.

This study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.

This paper aims to provide a discussion on the interface and interactions between data, analytical techniques and impactful research in humanitarian health supply chains. New techniques for data capturing, processing and analytics, such as big data, blockchain technology and artificial intelligence, are increasingly put forward as potential “game changers” in the humanitarian field. Yet while they have potential to improve data analytics in the future, larger data sets and quantification per se are no “silver bullet” for complex and wicked problems in humanitarian health settings. Humanitarian health supply chains provide health care and medical aid to the most vulnerable in development and disaster relief settings alike. Unlike commercial supply chains, they often lack resources and long-term collaborations to enable learning from the past and to improve further.

Based on a combination of the authors’ research experience, interactions with practitioners throughout projects and academic literature, the authors consider the interface between data and analytical techniques and highlight some of the challenges inherent to humanitarian health settings. The authors apply a systems approach to represent the multiple factors and interactions between data, analytical techniques and collaboration in impactful research.

Based on this representation, the authors discuss relevant debates and suggest directions for future research to increase the impact of data analytics and collaborations in fostering sustainable solutions.

This study distinguishes itself and contributes by bringing the interface and interactions between data, analytical techniques and impactful research together in a systems approach, emphasizing the interconnectedness.

When a large-scale outbreak such as the COVID-19 pandemic happens, organizations that are responsible for delivering relief may face a lack of both provisions and human resources. Governments are the primary source for the humanitarian supplies required during such a crisis; however, coordination with humanitarian NGOs in handling such pandemics is a vital form of public-private partnership (PPP). Aid organizations have to consider not only the total degree of demand satisfaction in such cases but also the obligation that relief goods such as medicine and foods should be distributed as equitably as possible within the affected areas (AAs).

Given the challenges of acquiring real data associated with procuring relief items during the COVID-19 outbreak, a comprehensive simulation-based plan is used to generate 243 small, medium and large-sized problems with uncertain demand, and these problems are solved to optimality using GAMS. Finally, post-optimality analyses are conducted, and some useful managerial insights are presented.

The results imply that given a reasonable measure of deprivation costs, it can be important for managers to focus less on the logistical costs of delivering resources and more on the value associated with quickly and effectively reducing the overall suffering of the affected individuals. It is also important for managers to recognize that even though deprivation costs and transportation costs are both increasing as the time horizon increases, the actual growth rate of the deprivation costs decreases over time.

In this paper, a novel mathematical model is presented to minimize the total costs of delivering humanitarian aid for pandemic relief. With a focus on sustainability of operations, the model incorporates total transportation and delivery costs, the cost of utilizing the transportation fleet (transportation mode cost), and equity and deprivation costs. Taking social costs such as deprivation and equity costs into account, in addition to other important classic cost terms, enables managers to organize the best possible response when such outbreaks happen.