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Measuring the Efficiency of Industries by Fuzzy Data Envelopment Analysis

المصدر: المجلة العلمية لقطاع كليات التجارة
الناشر: جامعة الأزهر - كلية التجارة
المؤلف الرئيسي: Zeidan, Asmaa S. A. (Author)
مؤلفين آخرين: Hafez, Enayat I. (Co-Author) , Ismail, Elham A. (Co-Author)
المجلد/العدد: ع18
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2017
الصفحات: 69 - 95
DOI: 10.21608/JSFC.2017.86899
ISSN: 2636-3674
رقم MD: 983639
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
التصنيع | الصناعات التحويلية | تحليل البيانات
كلمات المؤلف المفتاحية:
Data Envelopment Analysis | Fuzzy | Interval Data | Efficiency | Decision Making Units | Manufacturing Industries
رابط المحتوى:

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المستخلص: Manufacturing is a part of the income of any country, helping to grow the economy by generating productivity, stimulating research and development, and investing in the future. Therefore, this paper seeks to explain the productivity growth performance of Ethiopian's manufacturing sector using a dataset of 14 types of industries for the year of 2008; utilizing data envelopment analysis (DEA) techniques either traditional or fuzzy DEA models. Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Conventional DEA models assume that input and output values should be certain (crisp data). However, the observed values of the input and output data in real-world situations are sometimes vague or imprecise. In this paper, three approaches that transform the original data (crisp data) into interval data, in the form of upper and lower frontier data, are suggested. Then, by using these upper and lower frontier data; the interval DEA efficiency scores can be achieved. These approaches are applied on the real-life data and the results show that data envelopment analysis (DEA) techniques are suitable to evaluate and compare the performances of industries that enable the decision makers to analyze the situation better.
ISSN: 2636-3674

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