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Project1:DEA models for Parallel System: Application to Manufacturing Industry of China from the Perspective of Auditing

        审计计划阶段审计对象的遴选直接决定着审计效果和国家宏观政策的调整。为了科学的遴选审计对象,解决有限的国家审计资源与日益扩大的审计需求之间的矛盾,本文采用平行网络CRS模型,并对其的应用范围进行拓展,选取2011年制造业行业数据,将每个行业内部结构按照企业类型进行分类,运用平行网络CRS模型建立了国家审计对象遴选方法。研究得出,按照系统总效率遴选审计对象,选择的审计对象并不能真实反映各企业类型的实际效率,根据本文提出的遴选策略,依据总效率遴选的审计对象与通过四类企业类型选择的审计对象一致的仅有6个行业,国家审计资源无法实现合理有效配置。整体来看,私营企业平均效率>系统>集体企业>外商及港澳台投资企业>国有及国有控股企业,进而提出了按照不同企业类型行业效率与系统行业效率的差距大小遴选审计对象的策略,差距越大说明此企业类型对系统效率影响越大,应作为重点审计对象,据此遴选审计对象,不仅实现了制造业审计的全覆盖,而且也提高了审计效率。

      Conventional data envelopment analysis (DEA) considers a system or decision-making unit (DMU) as a “black box” in calculating its efciency and does not take the operation of individual components into account. However, in the real world, there are systems which are composed of independent component units. In recent studies on parallel DEA, inputs/outputs of the system are the sum of those of all its component units, which is not always true. This paper proposes a new parallel DEA model where each input/output of the system is not the sum of those of all its components under an assumption of variable returns to scale (VRS). The proposed approach is then applied to the manufacturing industry of China. Previous applications on manufacturing industry only discuss efficiency improvement for inefficient DMUs and barely analyze the efficient DMUs. However, from the perspective of auditing, the audit department should not only focus on inefficient DMUs or DMUs of the lower efficiency, but also pay attention to efficient DMUs or DMUs of the higher efficiency. Based upon this consideration, we propose a method to help the audit department select audit objects scientifically

 Project2Behind the Black-box DEA-a New Viewpoint of the Two-stage Structure

        当我们还不知道一个组织的内部结构和内部数据时,黑箱模型只能告诉我们,无效单元要实现有效,最初投入的缩减量或(和)最终产出的扩张量应该是多少。但是,这并没有回答,该组织的内部环节应该进行怎样的调整配合?我们则尝试回答,对一个串行的两阶段组织来说,黑箱模型所给出的最初投入缩减(或最终产出扩张)形式的效率调整,需要其内部中间品怎样配合。简言之,本项目尝试提供串行两阶段组织实现有效的全链条完整信息。 当前主流的两阶段DEA模型虽然重视上下游环节在中间品上的关联,但因其人为施加的权重约束,导致这些模型所得整体效率普遍小于黑箱模型的结果。这种不一致,导致研究者在考察具有内部结构的组织绩效时面临着信息困境:预期到两类模型结果的不一致,研究者在尚未完全知晓组织内部信息时,就难以开展对组织绩效的初步考察。本项目在保持整体效率与黑箱模型结果一致的前提下,完全基于所得数据,尝试得到串行两阶段组织在其内部的协调方式,避免完全人为施加权重约束。从而,对组织绩效的研究在不断获取组织内部信息的同时能保持结论的连续性

      Before we know the internal structure and the internal data of an organization, the standard DEA model (“black-box” model) can only tell us to what extent the initial inputs (final outputs) of the organization should be reduced (increased). It cannot tell us, however, what adjustment on the intermediates should be made. In this project, we try to answer this question for serial two stage organizations, using their intermediates data. In other word, we try to provide the complete information about the efficiency improvement along the supply chain. In the popular “relative” two-stage DEA models, the authors put emphasis on the relationship between the two stages, by requiring that the weights of the intermediates are equal across different stages, but they generally lead to efficiency results less than those obtained by the black-box model. Knowing this inconsistency of efficiency scores between the two kinds of models, a researcher may be confronted with an “information dilemma”: he will inevitably deny his earlier efforts on efficiency evaluation after he gets the complete internal information of the organization.We try to derive the coordination mechanism within a serial two-stage organization based on available data, keeping the overall efficiency of our new model equal to that of the black-box one.

 Project3A Two-stage DEA Model with Partial Intermediates: Application to China Dairy Supply Chains

      This project is aimed at evaluating efficiency of China dairy supply chains with a given scale of upstream. After examining the data of 2011’s “China’s dairy yearbook”, we construct a two stage non-cooperative model with some outputs in the first stage flowing out of the system, some additional inputs flowing into the second stage, and especially some of the intermediates only partly entering the second stage. By this model, we not only get the efficiency scores of each dairy chain, but also provide the optimal portion of the intermediates which partly go into the second stage and partly flow out of the given chain.