دانلود مقاله ISI انگلیسی شماره 4642
عنوان فارسی مقاله

استفاده از تحلیل پوششی داده ها (DEA) برای نظارت بر عملکرد مبتنی بر بهره وری سازمان بازده محور : طراحی و پیاده سازی یک سیستم پشتیبانی تصمیم گیری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
4642 2013 12 صفحه PDF سفارش دهید 8206 کلمه
خرید مقاله
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عنوان انگلیسی
Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Omega, Volume 41, Issue 1, January 2013, Pages 131–142

کلمات کلیدی
بازده - محیط های پویا - بهره وری - تحلیل پوششی داده - سیستم پشتیبانی تصمیم گیری - داده کاوی - خوشه - درخت تصمیم گیری
پیش نمایش مقاله
پیش نمایش مقاله  استفاده از تحلیل پوششی داده ها (DEA) برای نظارت بر عملکرد مبتنی بر بهره وری سازمان بازده محور : طراحی و پیاده سازی یک سیستم پشتیبانی تصمیم گیری

چکیده انگلیسی

The competitive nature of the business environment requires the productivity-driven organization to be aware of its relative level of effectiveness and efficiency vis-à-vis its competitors. This suggests the need, first, for an effective mechanism that allows for discovering appropriate productivity models for improving overall organizational performance, and, second for a feedback-type mechanism that allows for evaluating multiple productivity models in order to select the most suitable one. In this paper our focus is on organizations that consider the states of their internal (e.g., possibly exemplified by resource-based view) and external (e.g., possibly exemplified by positioning) organizational environment in the formulation of their strategies. We propose and test a DEA-centric Decision Support System (DSS) that aims to assess and manage the relative performance of such organizations.

مقدمه انگلیسی

Modern organizational entities typically operate in dynamic, competitive environments. Within this context, the critical issues of organizational survival and advancement often lead to calls for improvements in the levels of effectiveness and efficiency [64]. However, due to the relativity of the concepts of efficiency and effectiveness, productivity-driven organizations must take into consideration the performance of their competitors. For the dynamic nature of the business environment will cause the levels of performance of competing organizations to change over time, and if the efficiency of the competitors has improved, then a productivity-driven organization must respond with its own improvements in efficiency. Although some improvements in productivity do not require any drastic structural transformations but simply call for a gradual type of improvements in the level of performance (e.g., TQM, BPI, etc.), significant changes in the levels of effectiveness and efficiency often require structural reorganizations (e.g., ERP, BPR, etc.) that could result in periods of unstable behavior, which, if not managed, could escalate and become chaotic [52]. Resultantly, in a dynamic business environment any static model that is used to describe the relationship between inputs and outputs will have limited usefulness and feasibility in periods of instability. This suggest the need, first, for an effective mechanism that allows for discovering appropriate productivity models for improving overall organizational performance [24] and, second for a feedback-type mechanism that allows for evaluating multiple productivity models in order to select the most suitable one. The overall goal of this investigation is to propose and test a Decision Support System (DSS) that aims to assess and manage the relative performance of organizations. We focus on organizations that consider the states of their internal (e.g., possibly exemplified by resource-based view) and external (e.g., possibly exemplified by positioning) organizational environment in the formulation of their strategies, such that the achievement of an organizational goal is dependent on the level of performance that is commonly measured in terms of the levels of the efficiency of utilization of inputs, effectiveness of the production of outputs, and efficiency of conversion of inputs into outputs. This suggests that an important component technique of our DSS is Data Envelopment Analysis (DEA), which is widely used by researchers and practitioners for the purposes of measuring productivity and relative performance [74], [7], [17], [15], [73], [26] and [63]. However, other techniques are also required for providing answers to several questions that are relevant to the organization's search for the productivity model that is most suitable with respect to survival and advancement. In this investigation we focus on the following questions related to system requirements: We present our investigation as follows. Part One outlines the functionality and composition of the proposed system. Part Two offers an overview of the structural elements of the proposed DSS. Part Three outlines the design of DSS. Part Four offers an illustrative example of the DSS in action. A brief conclusion follows.

نتیجه گیری انگلیسی

In this paper we presented a DEA-centric DSS that provides facilities for assessing and managing the relative performance of productivity driven organizations that operate in unstable environments. The design of our DSS was guided by a set of system requirements (see Table 1) that are highly relevant to a productivity driven organization's efforts to identify and evaluate multiple productivity models in order to select the most suitable one for the given organization. These requirements suggested a coupling of the capabilities of DEA with capabilities of multiple data mining techniques as well as established theoretical frameworks (i.e., neo-classical growth accounting). The resulting DSS is applicable to different organizational levels, including the country level and the firm level. In this paper we demonstrated the feasibility and usability of this DSS on country-level organizational entities. It should be noted that while other studies have combined data mining (DM) techniques with DEA, to the best of our knowledge this is the first study that has provided an integrated DEA-DM decision support model that can address the multiple productivity-related issues listed in Table 1. It should also be noted that while we utilized a specific set of data mining techniques that other techniques could also be utilized. For example, regressions splines could be used instead of regression. Similarly our DSS model allows for the utilization of other theoretical frameworks for addressing the issues such as complementarity. The results of this research suggests that additional exploration of integrated DEA-centric models involving multiple DM techniques and theoretical frameworks for addressing multiple productivity-related issues could be a fruitful area of design science research.

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