A breakthrough in macro-scale circularity and eco-efficiency assessment: A case study of OECD countries.

Countries' circularity performance and CO2 emissions should be addressed as a part of the UN net-zero Sustainable Development Goals (SDGs) 2030. Macro-scale circularity assessment is regarded as a helpful tool for tracking and adjusting nations' progress toward the sustainable Circular Economy (CE) and SDGs. However, practical frameworks are required to address the shortage of real-world circularity assessments at the macro level. The establishment of CE benchmarks is also essential to enhance circularity in less sustainable nations. Further, monitoring the extent to which nations' circularity activities are sustainable and in line with the SDGs is an area that lacks sufficient practical research. The current research aims to develop a macro-level framework and benchmarks for national sustainable circularity assessments. Methodologically, we develop a dynamic network data envelopment analysis (DN-DEA) framework for multi-period circularity and eco-efficiency assessment of OECD countries. To do so, we incorporate dual-role and bidirectional carryovers in our macro-scale framework. From a managerial perspective, we conduct a novel comparative analysis of the circularity and eco-efficiency of the nations to monitor macro-scale sustainable CE trends. Research results reveal a significant performance disparity in circularity, eco-efficiency, and benchmarking patterns. Accordingly, circularly efficient nations cannot necessarily be considered eco-friendly and sustainable. Although Germany (as a superior circular nation) can be regarded as a circularity benchmark, it cannot serve as an eco-efficiency benchmark for less eco-efficient nations. Hence, the new method allows decision-makers not only to identify the nations' circularity outcome but also to distinguish sustainable nations from less sustainable ones. This, on the one hand, provides policymakers with a multi-faceted sustainability analysis, beyond the previous unidimensional analysis. On the other, it proposes improvement benchmarks for planning and regulating nations' future circularity in line with real sustainability goals. The capabilities of our innovative approach are demonstrated in the case study.

How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis.

Covid-19 virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) threatens the health of human beings worldwide, imposing a concern for the world and prompting governments to control the contagion. Although vaccination is a proper tool to control the transmission, the efficient allocation of limited health-care resources to massive patients can improve the effectiveness of medical services. Relying on the Artificial Neural Network (ANN), the aim of this research is to enhance the future efficiency of Covid-19 treatment centers by forecasting their efficiency and providing benchmarks. To do this, we use the congestion approach of data envelopment analysis (DEA) based on the theory of economies of scale principles. In the traditional input-oriented DEA, inefficient decision-making units (DMUs) can become efficient merely by reducing the inputs. However, this may not always be true in real-world applications such as improving the efficiency of COVID-19 treatment centers (DMUs). Meaning that the treatment centers with less congested inputs (e.g., ventilators, test equipment, pulmonologists, and nurses, etc.) normally have higher mortality rates. For this reason, in this study, we take the congested inputs approach into account to provide proper benchmarks for the inefficient treatment centers. According to the congestion approach of DEA, an optimum increase in congested inputs can lead to a greater than a proportional increment in outputs. In other words, if more respiratory equipment, pulmonologists, patient rooms, nurses and beds, etc. are allocated to Covid-19 treatment centers, not only the number of deaths (undesirable outputs) are decreased, but also the number of recoveries (desirable outputs) are increased. Such an optimal rise in the congested inputs is determined in pairwise comparisons derived from the model. Accordingly, in this study, first, considering the congestion approach of DEA and historical data of five periods, we identify the initial efficiency of Iranian Covid-19 treatment centers. Then, by running ANN, we forecast the future inputs and outputs, the overall efficiency, and rank of the treatment centers. By doing this, the prospective efficient and inefficient DMUs are identified, and appropriate benchmarks are determined.

Using network data envelopment analysis to assess the sustainability and resilience of healthcare supply chains in response to the COVID-19 pandemic.

The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes.

Assessing the sustainability of supply chains by dynamic network data envelopment analysis: a SCOR-based framework.

The objective of this paper is to assess the sustainability of supply chains by proposing a dynamic network data envelopment analysis (DNDEA) model in the presence of interval data, due to the fact that in many real-world applications, the condition of convexity in the production technology might be violated. To prevent this issue, a DNDEA model based on the free disposal hull (FDH) approach is developed. For the first time, this paper develops a DNDEA version of the free disposal hull (FDH) model in the context of the SCOR framework. It is also shown that this model always presents a finite efficiency score for assessing the sustainability of supply chains. Moreover, using this model, real benchmarks can be calculated to improve the sustainability of unsustainable supply chains. A case study in print industry is given. The results validate our proposed model.