To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.
Inadequate supply of energy has become one of the major problems in societies due to consumers' increasing demand. Economic growth is a key reason for the increase in the energy consumption. Although di erent policies can be employed for resolving this problem, optimizing the e ciency of energy suppliers can be addressed as a key policy in this regard. This paper presents an adjusted Network Data Envelopment Analysis (NDEA) model for evaluating performance of energy supply chain in Iran from production to distribution stages. Some suggestions have been proposed to optimize the performance of the energy supply chain. The NDEA model is adjusted by using Assurance Region (AR) to achieve more realistic and scienti c results. Borders of the assurance region obtained from Data Envelopment Analytic Hierarchy Process (DEAHP) method are entered into the NDEA model. The results obtained from this model are compared with those of conventional NDEA and technical e ciency in pairs. Finally, the Spearman and Kendall's-Tau correlation tests are used for validating the results.
The performance of an energy generation system depends on three sectors of generation, transmission, and distribution. Especially when the power generation system has a decentralized structure in which different companies have different types of power plants, transmission, and distribution architectures. In this study, a network data envelopment analysis (NDEA) model is extended to assess the energy generation system's performance considering all three sectors of the regional electricity companies. There is a gap in the published DEA literature on energy network efficiency in studying the function of individual processes of these subsystems. After identifying the companies' structure, inputs, and outputs for all sectors, the extended NDEA model is implemented to estimate the efficiency of the whole energy generation network including production, transfer, and circulation sectors of the firms. The proposed model's network-based features allow computing the efficiency of individual subsystems and the whole system simultaneously. Iran’s energy generation system, including all three sectors during 2015–2019, is used to verify the model. The results indicate that while the annual average efficiency score of electricity companies has been increasing by 2018, it was decreased during 2018–2019. The model can be easily applied to energy generation systems in other countries.
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