Gongbing Bi

Supply chain DEA: production possibility set and performance evaluation model

Performance evaluation is of great importance for effective supply chain management. The foundation of efficiency evaluation is to faithfully identify the corresponding production possibility set. Although a lot of researches have been done on supply chain DEA models, the exact definition for supply chain production possibility set is still in absence. This paper defines two types of supply chain production possibility sets, which are proved to be equivalent to each other. Based upon the production possibility set, a supply chain CRS DEA model is advanced to appraise the overall technical efficiency of supply chains. The major advantage of the model lies on the fact that it can help to find out the most efficient production abilities in supply chains, by replacing or improving inefficient subsystems (supply chain members). The proposed model also directly identifies the benchmarking units for inefficient supply chains to improve their performance. A real case validates the reasonableness and acceptability of this approach.

Input/output indicator selection for DEA efficiency evaluation: An empirical study of Chinese commercial banks

One of the interesting research subjects in DEA is to choose appropriate input and output indicators. In the process, one may encounter many problems, such as the selection tools, correlation analysis, and the classification of input versus output status. In this paper, we propose a new method for choosing DEA variables. Unlike previous research, it is based on the conception of cash value added (CVA), and can make a selection according to the statistic results. This new method has some advantages: first, it is more objective, avoiding the influence of subjective factors on the subsequent calculation; second and most important is that it provides managers and researchers with measurement variables and exact classifications of these factors; third, all variables under discussion come from financial statements which are easily available. This variable selection method has been applied to 14 Chinese commercial banks, and both regression and statistic test results are satisfactory.

Energy and Environmental Efficiency of China’s Transportation Sector: A Multidirectional Analysis Approach

With the rapid economic development, the transportation sector becomes one of the high-energy-consumption and high-CO2-emissions sectors in China. In order to ensure efficient use of energy and to reduce CO2 pollution, it is important to gain the best performance standards in China’s transportation sector. Data envelopment analysis (DEA) has been accepted as a popular tool of efficiency measurement. However, previous studies based on DEA are mainly restricted to the radial expansions of outputs or radial contractions of inputs. In this paper, we present a nonradial DEA model with multidirectional efficiency analysis (MEA) involving undesirable outputs for the measurement of regional energy and environmental efficiency of China’s transportation sector during the period 2006–2010. We not only evaluate the energy and environmental efficiency level and trend of China’s transportation sector but also investigate the efficiency patterns of 30 regions and three major areas of China. Additionally, we identify the energy saving potential and CO2 emissions reduction potential for each province and area in China in this study.

A New Malmquist Productivity Index Based On Semi-Discretionary Variables With An Application To Commercial Banks Of China

The Malmquist productivity index studies productivity change, that is, the technical progress or regress together with the efficiency changes over time. In a nonparametric framework, the index can be estimated by data envelopment analysis (DEA). In this paper, first all inputs are divided into three groups, namely, discretionary variables, nondiscretionary variables, and semi-discretionary variables, and then a mixed integer linear model is proposed to deal with semi-discretionary variables. The proposed models consider not only the properties of semi-discretionary inputs, but also the relationship between them and other inputs. By introducing such a relationship and the preferences of decision-makers (DMs), the models aid DMs in generating efficiency scores and finding proper benchmarking points. Finally, the Malmquist productivity index combining the proposed model is computed and illustrated by an empirical application to the evaluation of 17 branches of Bank of China in Anhui Province. The results show a slight decrease in productivity during the year 2007/2008, and the productivity change positively during 2008/2009 due largely to efficiency increase.

Efficiency Evaluation of Tourism Industry With Data Envelopment Analysis (DEA): A Case Study in China

Focusing on the efficiency evaluation of the Chinese tourism industry, this article aims to diagnose inefficiency and provide insight for improvement. A two-stage network data envelopment analysis model is formulated to compare the efficiencies of 31 provinces, municipalities, and autonomous regions in China. Assuming a two-stage process for the Chinese tourism industry, we evaluate the overall performance as well as the performance for each stage. The results indicate an obvious performance (or efficiency) imbalance in the tourism industry among different regions and a lot of room for improvement in some regions. Managerial implications of our findings may be of some benefit to local governments and related enterprises.

A clustering method for evaluating the environmental performance based on slacks-based measure☆

Abstract The conventional clustering algorithms are mostly distance-based, which can lead to distorted results in the evaluation of production unit’s performance. As a non-parametric method, data envelopment analysis (DEA) has become a popular approach to measuring the production process performance. However, few researchers paid attention to the relationship between clustering approach and DEA. In this paper, we use a non-radial DEA framework (slacks-based measure, SBM) to classify the environmental performance of Chinese industry, forming a benchmark-based clustering approach. Additionally, we employ the context-dependent DEA method to get the sub-clusters for detailed managerial meaning. An application in real world is given to explain the usage and effectiveness of the proposed SBM-based clustering method, and the result is compared with the conventional distance-defined k-means clustering approach.

Environmental subsidy and the choice of green technology in the presence of green consumers

In this paper, we present a study on a government using subsidy policy to motivate firms’ adoption of green emissions-reducing technology when consumers are environmentally discerning. We consider two profit-maximizing firms selling two products in a price and pollution sensitive market. The products differ only in their manufacturing costs, selling prices and the amount of pollutant emissions per unit of product. The objective of each firm is to determine the selling prices of the products, taking into account the impact of green technology on costs and customer demands. Two cases are considered: (1) the government has limited budget and can choose only one firm at most to provide subsidy; (2) the government has sufficient budget and can choose both firms to provide subsidy. We discuss which firm should be selected in each case and in which situation the firm has incentive to invest in the green technology. We also show that the green technology level, environmental improvement coefficient and unit cost increase coefficient play important roles in the government subsidy strategy.

Estimating Relative Efficiency of DMU: Pareto Principle and Monte Carlo Oriented DEA Approach

Abstract The traditional data envelopment analysis (DEA) models treat a decision making unit (DMU) as a “black box”, which is often criticized for not considering the inner production mechanism of a production system. The network DEA models developed to overcome this deficiency by considering the internal structure of a DMU have recently gained popularity. The inner data, however, are not generally available for real application purposes. This paper, on one hand, addresses the problem with the traditional DEA for not considering the inner structure and, on the other, with the network models for missing inner data in parallel production settings. Procedures built on managerial information of production processes, as characterized by the Pareto principle, are presented that consider the inner production mechanism as well as the data availability in a reliable way. Firstly, the production activities of a DMU are classified into a core business unit (CBU) and a non-core business unit (NCBU). Secondly, the internal information related to inputs/outputs is assumed to be available for the DMU under evaluation; whereas for the other DMUs, this data is generated by using the Pareto principle. In addition, the Monte Carlo method, also known as the parametric bootstrap, is applied to estimate the efficiency of the DMU. A numerical example illustrates the proposed method.

A decision model for supplier selection in the presence of dual-role factors

Best suppliers help manufacturers to gain and sustain a competitive advantage by improving product quality, reducing cost, shortening lead-time and so on. The crucial role played by suppliers in a supply chain renders the selection of a supplier or suppliers a significant work that attracts the attention of both researchers and practitioners. In the literature, data envelopment analysis (DEA) as a nonparametric technique is often applied to conduct a performance analysis. Little attention, however, has been paid to dual-role factors in the supplier selection process. This paper proposes a novel two-step approach to select suppliers in the presence of dual-role factors. As a combination of DEA and two-person zero-sum game theory, our work considers the strategic behaviour of the external environment and is able to handle the supplier selection in a competitive environment. Moreover, the proposed model circumvents the requirement to specify the status of a dual-role factor ahead of an evaluation and is easy to solve. Finally, the applicability of the procedure is illustrated by an application to a data set of 18 suppliers.

Radial and non-radial DEA models undesirable outputs: an application to OECD countries

Data envelopment analysis (DEA) is an effective tool to measure the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs. This study classifies the outputs into desirable and undesirable outputs, and then presents two DEA models, radial and non-radial models, to measure the environment efficiency for DMUs with undesirable outputs. Finally, two proposed DEA models are applied to evaluate the performance of 26 countries within OECD, in which two inputs (population and total energy consumption), two desirable outputs (GDP and total power generation) and one undesirable output ( 2 CO emission) are considered, and some managerial implications are