This paper provides a framework to analyze the maturity of humanitarian logistics systems to face crisis situations related to recurrent events, and thus to identify the main areas of action and the community needs in terms of crisis logistics planning. First, the main notions of humanitarian logistics systems planning, and the theoretical contribution of maturity models are presented. Second, a maturity model for humanitarian logistics systems is proposed and the main categories of elements defining maturity extracted from literature. Then, the methodology to define the main elements of the maturity model via evidence is presented. This methodology combines a literature overview, a documentary analysis, and the development of three case studies, two located in Colombia and one in Peru. The main elements that characterize capability maturity model in humanitarian logistics systems facing recurrent crises are identified, from which the administration of donations, design of a distribution network, and the choice of suppliers are highlighted. The practical implications of the framework are proposed to allow its use to anticipate humanitarian logistics system for future crises. The framework allowed a first analysis guide and will be further extended.

Purpose: This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.Design/methodology/approach: A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of riska prioricategorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.Findings: Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.Research limitations/implications: The results need a qualitative or case study validation at the community/district level.Practical implications: We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.Originality/value: Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.

In this article, we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determine the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means high economic activity, which leads to more deaths due to COVID-19. There is a lack of supply in the set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated with COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

In this article we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is: which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determines the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means a high economic activity that leads to more deaths of COVID-19. There is a lack of supply of a set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated to COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

Las aerolíneas alrededor del mundo se han visto afectadas por los desafíos de rentabilidad, participación de mercado y el comportamiento de la demanda, lo cual ha contribuido al desarrollo de la gestión de ingresos o Revenue Management. El modelo de gestión busca maximizar el nivel de ingreso en las empresas con la gestión de la demanda. La aplicación de este modelo en empresas de diversos rubros ha generado un incremento en sus ingresos anuales en orden del 4 a 5% favoreciendo oportunidades de negocio a través del desarrollo de nuevos mercados basados en la rentabilidad empresarial. La industria aeronáutica fue una de las pioneras en aplicar este modelo de gestión a nivel mundial. En esta oportunidad, el caso aplica el modelo en un mercado dinámico y con nuevas restricciones: mercado regional, vuelos cortos, tarifas desreguladas e impacto de las aerolíneas de bajo costo.