Multi-Criteria Performance Analysis Based on Physics of Decision - Application to COVID-19 and Future Pandemics

The purpose of this study is to present a novel perspective on decision support based on the conventional SEIR pandemic model paradigm considering the risks and opportunities as physical forces deviating the expected performance trajectory of a system. The impact of a pandemic is measured by the deviation of the social system's performance trajectory within the geometrical framework of its Key Performance Indicators (KPIs). According to the overall premise of utilizing Ordinary Differential Equations to simulate epidemics, the deviations are connected to several alternative interventions. The model is essentially built on two sets of parameters: (i) social system parameters and (ii) pandemic parameters. The ultimate objective is to propose a multi-criteria performance framework to control pandemics that includes a combination of timely measures. On the one hand, the current study optimizes prospective strategies to manage the potential future pandemic, while on the other hand, it explores the COVID-19 epidemic in the state of Georgia (USA).

Managing supply chains during the Covid-19 crisis: synthesis of academic and practitioner visions and recommendations for the future

Purpose

The Covid-19 pandemic has created an environment of high uncertainty and caused major disruptions in supply chains. The new normal that has emerged during the pandemic is leading to a need to identify new solutions to improve supply chain crisis management in the future. Practitioners require adapted recommendations for solutions to implement. These recommendations are laid out in this paper.

Design/methodology/approach

A combination of a systematic literature review (SLR), qualitative semi-structured interviews and a questionnaire survey of supply chain practitioners is applied. The interviews provide insights into supply chain practitioners' views of their approaches and, together with the solutions proposed in the literature, provide future recommendations for action for supply chain managers.

Findings

During the pandemic, companies experienced disruptions in supply, production and demand, as well as interruptions in transportation and distribution. The majority of the solutions proposed in the literature, coincide with the opinions of practitioners. These include collaborative risk management, real-time monitoring and information sharing, supply network management, scenario planning and “what-if” simulations.

Research limitations/implications

Although the number of interviews conducted and questionnaires completed is limited, they still serve to supplement the SLR with important practical insights and recommendations.

Originality/value

This paper presents a review of recent academic literature focusing on the impact of Covid-19 on supply chains and the existing solutions to mitigate that impact and manage future crises. It has been expanded to include industry perspectives and experiences. The findings of this study present recommended practices and strategies for better managing supply chains during a crisis.

Towards a better management of complex emergencies through crisis management meta-modelling

Managing complex emergency situations is a challenging task, mainly due to the heterogeneity of the partners involved and the critical nature of such events. Whatever approach is adopted to support this objective, one unavoidable issue is knowledge management. In the context of our research project, gathering, formalising and exploiting all the knowledge and information about a given crisis situation is a critical requirement. This paper presents some research results concerning this specific topic: from a theoretical point of view, the generic dimensions of crisis characterisation are defined, while from a technical point of view, we describe a software solution able to collect that knowledge (based on meta-models and ontologies). This is used to confront the characteristics of the situation (context) with characteristics of the resources (relief system) in order to design a suitable response. Finally, an illustrative example concerning a crash between a tanker truck and a train is described.