基于DEA方法的制造企业物流系统效率评价实例分析

针对制造企业的特点,设计了物流系统评价指标体系,运用DEA方法对制造企业物流系统效率评价问题进行研究。通过实例分析,证实DEA方法对制造企业物流系统效率评价有较好的应用价值。     

基于DEA方法的制造企业物流系统率评价实例分析

针对制造企业的特点,设计了物流系统评价指标体系,运用DEA方法对制造企业物流系统效率评价问题进行研究.通过实例分析,证实DEA方法对制造企业物流系统效率评价有较好的应用价值.     

基于DEA的西部地区农民专业合作经济组织发展模式的绩效评价

西部地区农民专业合作经济组织的发展比较快速,但是存在一定的问题。文章通过研究西部地区农民专业合作组织的发展现状寻找出存在的问题,有针对性地分析了西部地区农民专业合作经济组织存在的主要模式,并以呼和浩特为例,借助数据包络分析法(DEA)对农民专业合作经济组织的主要模式进行了效率评价,寻找有利于地区发展的创新性模式。     

基于DEA的创新型大企业创新效率的评价研究

文章以DEA方法的C^2R模型为基础进行改进,建立C^2GS^2扩展有效模型,运用该模型和WINQSB软件,对创新型大企业内部的各创新单元之间以及合作创新的伙伴之间进行创新效率评价,根据评价结果是否为DEA有效和扩展有效值来判断创新单元的效率是否达到最优,进而采取相应的措施优化创新资源的配置,最大限度的发挥各种创新资源的效率,最终提高企业的创新能力和竞争力。     

基于DEA的产业园区生产系统非合作博弈环境效率分析

文章针对我国当前产业园区发展中的环境效率评价问题,将园区生产系统划分为经济生产系统和污染处理系统,构建了两系统的环境效率评价基础模型。在此基础上,将非合作博弈的观点引入到两系统中,利用DEA的基本方法,将评价模型进行有效改进并计算经济生产系统、污染处理系统及产业园区综合系统的环境效率,提出了模型使用中的注意问题。最后通过产业园区的实例说明了该方法用于环境效率评价的合理性与科学性。     

基于网络DEA的我国铁路运输企业效率评价分析

效率是决定企业发展的关键因素,本文探究网络DEA模型的组成,通过网络DEA方法分析和评价我国铁路运输企业的工作效率,对铁路运输企业效率的提升提出了相关建议,并为相关领域提供了理论指导。     

基于DEA方法的云南省高技术企业创新效率的探讨

首先基于创新效率的现有研究成果,构建高技术企业创新效率评价指标体系与模型。然后结合所获取的数据,运用DEA方法,对云南省2009-2014年高技术企业的创新效率进行综合评价与分析。结果表明:云南省高技术企业创新效率中等偏上,在六年的效率评价中,有四年是有效的,云南省的高技术企业有两年的投入冗余,而研发工作人员和研发经费投入比例不合理是导致云南省高技术企业非DEA有效和创新效率偏低的根本原因。最后提出相应政策建议。     

基于改进的DEA模型在效率评价中的应用

DEA模型是目前最常用的效率评价方法,但是传统DEA模型输入和输出指标权重分配不合理,导致无法对有效的决策单元加以区分。在DEA模型中引入灰色关联约束锥,对权重进行约束。通过将该模型应用于高校评价,验证了该模型可以在信息不充分时解决指标权重分配和决策单元排序问题,使评价结果更为合理。     

基于三角模糊AHP和超效率DEA的物流系统评价

传统的DEA、AHP或者二者的单纯结合在评价物流绩效时还存在着一些不足。文章提出了基于三角模糊AHP和超效率DEA的物流系统评价法,考虑经验的模糊性的同时结合了超效率DEA,更全面、更实际地评价了物流系统。     

基于超DEA的皖江九市产业用地效率评价

本文以皖江九市作为研究区,在采用传统DEA评价方法 C2R模型评价2001至2010年10年间皖江九市总体产业用地投入产出效率的基础上,采用超效率DEA评价法对2010年皖江九市各市产业用地投入产出效率进行评价排序。结果表明:2001年至2010年10年间,皖江九市总体上仅2008及2010年两年产业用地投入产出为DEA有效,其余年份均存在产业用地投入冗余、产出不足现象;2010年,产业用地投入产出达到DEA有效的城市有六个,运用超DEA模型排序产业用地投入产出效率,结果从高到低依次为合肥、马鞍山、滁州、芜湖、巢湖及池州。     

中国高校人力资源管理创新绩效评价研究

利用DEA方法对我国2005-2009年的高校人力资源管理创新绩效进行了评价和分析,并运用Malmquist生产率指数对其绩效变动进行了测算。结果显示,我国高校人力资源管理创新绩效水平较为低下,纯技术效率低下是其主要原因;全要素生产率呈现较为显著的增长趋势,技术效率水平提高是引致TFP增长的主要原因,而技术进步对TFP有负面效应;我国高校人力资源管理创新绩效水平存在较为显著的省际和区域差异。     

Assessing academic department efficiency at a public university

The assessment of public universities has gained importance because of the demands from such state government bodies as the executive and the legislature. Public universities are increasingly being asked to account for how efficiently they have used diminishing state financial resources. Administrators thus have the responsibility of ensuring that the university's financial, human, and physical resources are allocated to academic departments in a manner that enhances the institution's efficiency. In this paper, data envelopment analysis (DEA) is proposed for evaluating the efficiency of academic departments at a public university. DEA provides a single measure of efficiency for each academic unit. It also identifies the causes behind the inefficiencies exhibited by poor performing units, as well as the changes that these units need to make in order to improve their efficiencies. Its usefulness as a planning tool is also discussed. Copyright © 2002 John Wiley & Sons, Ltd.     

Implications of variant efficiency measures for policy evaluations in UK higher education

Policy goals in UK higher education encourage the publicly funded universities to become more-specialised and larger in size without compromising output quality. Efficiency gains are expected to flow from this increased specialisation in accordance with universities’ comparative research and teaching strengths. Mergers to reap further gains from economies of scale are also being actively encouraged. Given this scenario, the paper investigates whether best-practice efficiency measurement based on data envelopment analysis (DEA) provides empirical support for the current policy goals. It also assesses whether such support is dependent on the specific type of efficiency measure used in the DEA modelling. This assessment finds that a selection of (nine) commonly used, variant efficiency measures generally support the current policy goals. The paper also uses the principal-agent framework to explore the issues involved in using computed DEA-based efficiency scores for policy evaluations and possible funding guidance in UK higher education. This highlights empirically how policy-makers and universities can have very different preferences about which efficiency measure is to be used for policy evaluations and possible funding guidance.     

基于超效率DEA的城市效率演变特征

以我国15个副省级城市为研究样本,首先构建了基于效率的副省级城市的投入产出评价指标体系,采用超效率的数据包络分析模型,克服了一般文献中由于通常使用数据包络分析的DI,BC2和DEC2R模型,而陷入对有效的决策单元无法做进一步分析评价的困惑,比较客观地评价了1995-2005我国15个副省级城市效率的演变特征:其一,超效率DEA值变化呈倒U字型.且超效率值有趋同性的态势;其二,超效率DEA值大于1的副省级城市数量逐年增多;其三,超效率DEA值整体变化比较稳定,但少数某些城市的超效率值变化剧烈.     

Using social media for efficient brand marketing: An evaluation of Chinese Universities using Bilibili

As the higher education market becomes increasingly competitive, Chinese universities are competing to develop brand marketing programmes on social media platforms, with significant differences in performance. This paper divides universities’ social media marketing processes into two stages: the production-operation stage and the marketing communication stage. It constructs a two-stage data envelopment analysis (DEA) model to evaluate the brand marketing efficiency of 296 Chinese universities on the Bilibili video-sharing platform to provide strategic proposals for social media marketing. The results show that 10 universities perform efficiently in the production-operation stage, while 11 universities perform efficiently in the marketing communication stage. The gap in marketing efficiency is relatively large across Chinese universities. Most universities perform unsatisfactorily because of their redundant input or insufficient output. To improve the brand marketing efficiency on Bilibili, universities should pay more effort into quality rather than quantity of input, produce high-quality videos continuously and build and maintain fan loyalty.     

Some statistical and DEA evaluations of relative efficiencies of public and private institutions of higher learning

This paper uses the Charnes, Cooper and Rhodes (CCR) ratio form of Data Envelopment Analysis (DEA) (1) to examine how DEA can be utilized in analyzing different aspects of production behavior of institutions of higher learning (IHLs) as an alternative to more traditional approaches such as econometric-regression models, and (2) to compare “specifically” relative efficiencies of public and private doctoral-granting universities in the U.S. and to analyze technical and scale efficiencies of those universities. The separation of doctoral-granting universities into universities with and without medical colleges represents a departure from preceding studies. This division proved very important in uncovering substantial differences in behavior between the two groups even when using the “statistical averaging” approaches that were customary in previous studies. For both groups, public universities proved more efficient than private universities when managerial and program inefficiencies are present in the data. When managerial inefficiencies are disentangled from the data and medical schools are not present, private universities have more efficient programs. However, their managers are less efficient users of program opportunities than are managers in public universities. Another portion of the current study dealt with returns-to-scale possibilities. These differed markedly (even on average) between IHLs with and without medical schools. Moreover, analyses by DEA showed marked ranges of variation for returns-to-scale possibilities for individual IHLs within each group. These possibilities have generally been concealed by the statistical averaging utilized in previous econometric studies. Further, their identification is beyond the ability of the usual types of one-at-a-time ratio and trend analyses.     

基于DEA模型的城市科技活动效率评价

将DEA理论应用于广东21城市科技活动的分析中，建立评价城市科技活动效率的数据包络分析模型，谊模型评价了各城市的总体效率和技术效率的状况，进而对非DEA有效的城市提出达到DEA有效的可行措施，并分析了城市科技活动效率和资源配置之间的关系。     

Research performance evaluation of Chinese university: A non-homogeneous network DEA approach

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.     

Measuring productivity of research in economics: A cross-country study using DEA

We measure productivity in leading edge economic research by using data envelopment analysis (DEA) for a sample of 21 countries belonging to the Organization for Economic Cooperation and Development (OECD). Publications in ten top journals of economics from 1980 to 1998 are taken as the research output. Inputs are measured by R&D expenditure, the number of universities with economics departments and (as an uncontrollable variable) population. Under constant returns-to-scale, the US emerges as the only efficient country. Under variable returns-to-scale, the efficiency frontier is defined by the US, Ireland and New Zealand. With the exception of the US, all countries in our sample display increasing returns-to-scale, and thus have the potential to raise their efficiency by scaling up their research activities.     

Measuring efficiency under fixed proportion technologies

Data Envelopment Analysis (DEA) applications frequently involve nonsubstitutable inputs and nonsubstitutable outputs (that is, fixed proportion technologies). However, DEA theory requires substitutability. In this paper, we illustrate the consequences of nonsubstitutability on DEA efficiency estimates, and we develop new efficiency indicators that are similar to those of conventional DEA models except that they require nonsubstitutability. Then, using simulated and real-world datasets that encompass fixed proportion technologies, we compare DEA efficiency estimates with those of the new indicators. The examples demonstrate that DEA efficiency estimates are biased when inputs and outputs are nonsubstitutable. The degree of bias varies considerably among Decision Making Units, resulting in substantial differences in efficiency rankings between DEA and the new measures. And, over 90% of the units that DEA identifies as efficient are, in truth, not efficient. We conclude that when inputs and outputs are not substituted for either technological or other reasons, conventional DEA models should be replaced with models that account for nonsubstitutability.