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The purpose of this paper is to highlight the importance of the humanitarian supply chain (HSC) as the backbone of the humanitarian operations. It further proposes feasible ways to overcome some of the main supply chain challenges identified by practitioners to achieve cost efficient and effective operations.

The challenges that the HSC faces and proposed changes to overcome them are gathered from interviews with nearly 40 practitioners.

Five critical issues that affect the future of HSCs are identified along with recommendations to address them.

It supports the fulfillment of the agenda for humanity’s five core responsibilities: global leadership to prevent and end conflict, uphold the norms that safeguard humanity, leave no one behind, change people’s lives – from delivering aid to ending need, and invest in humanity.

The original report was presented at the first World Humanitarian Summit in Istanbul in May 2016. The reader can find it via the following link www.logcluster.org/sites/default/files/whs_humanitarian_supply_chain_paper_final_24_may.pdf

Following a large-scale disaster, medical assistance is a critical component of the emergency response. The paper aims to discuss this issue.

Academic and practitioner literature is used to develop a framework studying the effectiveness of the humanitarian medical supply chain (HMSC). The framework is validated by using the findings of interviews conducted with experts and the case study of a serious humanitarian medical crisis (Ebola outbreak in 2014).

The factors affecting the effectiveness of the HMSC are identified.

To get an expert opinion on the major logistical challenges of the medical assistance in emergencies only 11 interviews with practitioners were conducted.

While the existing academic literature discusses the distribution of various supplies needed by the affected population, limited research focuses specifically on studying the HMSC aspect of the response. This paper closes this gap by describing the HMSC in the case of disaster response, and identifying the factors affecting its effectiveness, especially focusing on the factors that are unique to the medical aspect of the humanitarian supply chain.

Hospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.

The authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.

The ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.

This work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.