A Developed Data Envelopment Analysis Model for Efficient Sustainable Supply Chain Network Design

In recent years, various organizations have focused on considering the sustainability concept in the supply chain (SC) design. Managers try to increase the sustainability of SCs to achieve a competitive advantage in today’s growing market. Designing a sustainable supply chain (SSC) by integrating economic, social, and environmental dimensions affects the SC’s overall performance. To achieve the SSC, decision makers (DMs) are required to evaluate different strategies and then apply the most effective one to design SC networks. This study proposes an assessment approach based on the network data envelopment analysis (DEA) to choose an efficient strategy for each stage of an SSC network. This approach seeks to provide a sustainable design with DMs to avoid imposing additional costs on SCs that result from noncompliance with environmental and social issues. To this end, we consider sustainability-concept-related inputs and outputs in the network DEA model to choose the most efficient strategy for SSC design. The strategy selection process can become an important issue, especially when SCs active in a competitive environment. Accordingly, a crucial feature of the presented model is considering the issue of competition to choose the efficient strategy. Furthermore, undesirable outputs and feedbacks and independent inputs and outputs for intermediate stages in the network system are considered to create a structure compatible with the real world. The output of the proposed approach enables DMs to select the appropriate strategy for each stage of the SSC network to maximize the aggregate efficiency of the network.

Tourist attractions in efficiency analysis

This paper discusses the use of tourist attractions in tourism efficiency analysis. Tourist attractions can be employed either as an input of the production technology or as an environmental factor in a two-stage Data Envelopment Analysis model. An empirical illustration to the case of Chinese provinces underlines that using tourist attractions in different ways can yield different rankings of the units in terms of efficiency. Recommendations for future research are then proposed.

Efficiency of Indian Thermal Power Plants and Suspended Particulate Matter: Findings Across Technological Assumptions and DEA Models

Suspended particulate matter (SPM) emissions from coal-based thermal power plants (CTPPs) cause respiratory illness. However, this has not been given its due importance in the efficiency assessment of CTPPs. This study contributes to the literature by incorporating suspended particulate matter in the benchmarking exercise for Indian CTPPs. In such a study, the theoretical assumptions regarding pollution generating technology or the choice of evaluation tool may impact the ranking of CTPPs. To draw robust inferences, we present a comparative study of two alternative microeconomic approaches (joint and by-production technologies) and two types of data envelopment analysis tools (graph–hyperbolic and directional distance function) applied on two representative samples of Indian CTPPs. Results indicate that Indian CTPPs are moderately inefficient. Choice of technological assumption or data envelopment analysis model does not impact the ranking of CTPPs. Ownership and plant load factor play vital roles in determining inefficiency, and impacts of these factors remain stable across models. JEL Classifications: C61, D22, Q40, Q50

Investigating into the dual role of loan loss reserves in banking production process

This paper considers the use of loan loss reserves (LLRs) in the banking production process and treats it as one variable with a dual role. We establish a three-stage network Data Envelopment Analysis model to address this issue. Using a sample of 43 Chinese commercial banks over the period 2011–2019, the results show that the banks with the ratio between LLRs and total loans less than 1% have higher level of efficiency compared to the ones holding the ratio greater than 1%. The results show that when excluding LLRs in the production process, the efficiency scores are significantly inflated. We find that small and medium sized banks are more efficient than their big counterparts, however, the results show that big banks hold more than enough amounts of LLRs than the one required by the regulatory authority. When LLRs are excluded from the production process, it shows that big banks perform better than small and medium sized banks. Our findings show that less liquid banks perform better than the ones with higher levels of liquidity no matter in which way LLRs are treated. Finally, we find that lower capitalized banks, compared to the ones with high levels of capitalization, are less efficient. however, it shows that higher capitalized banks consistently keep more than 1% LLRs out of total loans.

Using data envelopment analysis to perform benchmarking in intensive care units

Background Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis. Methods We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit. Results We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79–1.21] and SRU was 1.15 [IQR: 0.95–1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18–1.88] vs. 1.7 [IQR: 1.36–2.00]) and nursing workload (168 hours [IQR: 168–291] vs 396 hours [IQR: 336–672]) but higher nurses per bed ratio (2.02 [1.16–2.48] vs. 1.71 [1.43–2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the “most efficient” quadrant. Conclusion Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.

The Development of an Efficiency-Based Global Green Manufacturing Innovation Index: An Input-Oriented DEA Approach

Innovation-based economic growth is considered to be a vital strategic aim for all economies, but environmentally friendly concepts and sustainable development (SD) must also be considered. The literature on the Global Innovation Index (GII) shows various investigations relevant to innovation, yet the lack of comprehensive consideration within the GII of environmental concerns represents a critical challenge. This paper aims to provide a holistic-perspective evaluation model for the top 15 manufacturing countries worldwide in order to resolve this. The efficiency-based Global Green Manufacturing Innovation Index (GGMII) was developed by formulating an input-oriented data envelopment analysis model. Criteria such as the value added to the gross domestic product (GDP), corresponding CO2 emissions, and unemployment rates were examined in order to represent the economic, environmental, and social dimensions of SD, respectively. Other scientific and technological dimensions were also considered. The data corresponding to all ten of the criteria were collected from World Bank Open Data. The results show a mismatch between the original GII and the proposed GGMII for the top eight manufacturing countries (the United States, the United Kingdom, Germany, Korea, France, China, Japan, and Canada), while the remaining countries (Italy, Spain, Russia, India, Mexico, Brazil, and Indonesia) occupied the same rank in both indices, but showed a sizable diminution in their original GII scores. The proposed GGMII might be utilized as a benchmarking instrument for all countries worldwide in the future.

Data-Driven Evaluation and Optimization of Agricultural Sustainable Development Capability: A Case Study of Northern Anhui

Human survival depends on the sustainable development of agriculture. This study constructs a data-driven evaluation and optimization method of agricultural sustainable development capacity, aiming to better cope with challenges such as environmental pollution and excessive consumption of resources and energy, as well as improve agricultural economic level. Further, an evaluation index system was constructed based on comprehensive consideration of energy and resources utilization, environmental pollution, and agricultural economy. After simplifying and integrating the data, a data envelopment analysis model was constructed to quantitatively evaluate the capability for agricultural sustainable development and its changing trend. Moreover, its influencing factors were analyzed from the perspective of input, which provides accurate countermeasures for improving agricultural sustainable development ability, resource utilization efficiency, and process optimization. This study shows the realization process of the aforementioned method for the agricultural development of six cities in northern Anhui from 2010 to 2019. Our results suggest that the sustainable development ability of northern Anhui is weak, but overall, has a good development trend. Based on our results, some countermeasures were proposed to control production scale reasonably, reduce environmental load, and improve resource efficiency, which provides a reference for policymakers to guide and standardize the development of regional agriculture.

Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach

The Korean government has been continuously conducting diverse national R&D programs to discover new growth engines. The Republic of Korea is one of the countries with the largest investment in national R&D, but its efficiency was relatively low. In response, this study established a framework to identify the characteristics and direction of outstanding R&D programs. In this study, the performance of the R&D programs was identified in the sub-program unit. The efficiency of the national R&D program was analyzed using the data envelopment analysis model through the outputs of the national R&D programs such as papers and patents. However, patent and paper output would take time to be realized. Therefore, this study also calculated the diversity index of R&D programs to identify their potential expected performance. This study applied the suggested framework in the electric vehicle fields, which is one of the core growth engines of South Korea. A list of outstanding programs was identified from the National Institute of Science and Technology Information (NTIS) data. Additionally, this study also discovered the main technology areas and their current issues of outstanding and -new R&D programs. These results could contribute to suggesting the policy direction to conduct high-performance national R&D programs.

Efficiency Evaluation of China's Regional Innovation Process Based on a Network Data Envelopment Analysis Model with a Mixed Structure: Enlightenment from the Regional Tobacco Industry

Accurate measurement of regional efficiency is a prerequisite for effective management. Prior studies have expanded on the overall "black box" evaluation with two stages of research and development (R&D) and commercialization, opening up the internal structure of the regional innovation process, but ignoring the independent innovation activities of universities, research institutes, and firms in the R&D stage. We construct a mixed structure with two stages, three actors, and four subsystems, and conduct an empirical analysis of China's provincial samples from 2017 to 2019 by using the network data envelopment analysis (DEA) model. Results show that the efficiency of the R&D stage at the provincial level is generally higher than that of the commercialization stage. However, the three subsystems of the R&D stage perform poorly. Spearman’s rank correlation coefficient suggests that there is a significantly positive correlation between total regional efficiency and commercialization. In addition, we use the k-means method to divide 27 provinces into three clusters, setting a more appropriate improvement benchmark for inefficient provinces. Based on enlightenment of regional tobacco industry, we put forward some proposals for specific stage and specific subsystem.