Abstract
This study aims to determine the technical efficiency levels of the enterprises active in the “Manufacture of Basic Iron and Steel and of Ferro-Alloys” sector in Turkey. The inputs and outputs are deterministic in classical Data Envelopment Analysis, so the changes in exchange rate, inflation rate, etc. aren’t considered, and the precautions for future inconsistencies are not foreseen. This leads to critics of deterministic Data Envelopment Analysis models. In this paper, the additive model developed depending on the Banker, Charnes and Cooper (BCC) model was extended by chance constrained programming formulations in order to overcome the insufficiencies in deterministic Data Envelopment Analysis, and the technical analysis of “Manufacture of Basic Iron and Steel and of Ferro-Alloys” sector was performed.
Highlights
Data Envelopment Analysis (DEA) is a technique used in the evaluation of the efficiencies of decision making units (DMUs) that use multiple inputs for producing multiple outputs
When Chance Constrained DEA (CCDEA) and deterministic DEA results are compared it is observed that number of inefficient DMUs in input variables increase except manufacture cost, whereas number of inefficient DMUs in output variables decrease
Compared with the deterministic process, number of inefficient DMUs decrease in all output variables when α=0,05 inefficient DMUs differ in input variables
Summary
Data Envelopment Analysis (DEA) is a technique used in the evaluation of the efficiencies of decision making units (DMUs) that use multiple inputs for producing multiple outputs. Up to now since it has appeared, DEA has found widespread applications in various areas and still goes on its advancement with an interaction to many techniques. Using the micro-data of the production units for the year 2004, the technical efficiency of the Manufacture of Basic Iron and Steel and of Ferro-Alloys” sector was analyzed according to the deterministic DEA BCC Additive model and Chance Constrained DEA (CCDEA) Additive model. The contribution of the study is two dimensional; under the light of previous scientific studies conducted, using micro-data on the basis of production rather than panel data and applying the deterministic DEA and CCDEA method, the measurement of technical efficiency was studied and the results were compared. Determination of input and output variables are presented, and in the sixth part, a real data application and the results of this application entirely are discussed and interpreted
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