Selection of Input Variables in DEA using 2 – Level Fractional Factorial Design
C.V. Pradeepa1, V. Prakash2
1C.V. Pradeepa, Ph.D Research Scholar, Department of Statistics, Presidency College Autonomous, University of Madras, Chennai (Tamil Nadu), India.
2V. Prakash, Associate Professor Head, Department of Statistics, Presidency College Autonomous, University of Madras, Chennai (Tamil Nadu), India.
Manuscript received on 19 January 2020 | Revised Manuscript received on 02 February 2020 | Manuscript Published on 05 February 2020 | PP: 147-154 | Volume-8 Issue-4S5 December 2019 | Retrieval Number: D10361284S519/2019©BEIESP | DOI: 10.35940/ijrte.D1036.1284S519
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data Envelopment Analysis (DEA) can be a statistics oriented, non – parametric method to gauge relative efficiency supported pre – selected Inputs and Outputs. It is a implemented mathematics based technique for measuring the relative overall performance of organisational units in which the presence of Multiple Inputs and Outputs makes evaluation difficult. In a few cases, the performance model isn’t well defined, so it’s important to pick the proper Inputs and Outputs by way of other means. We used, Morita and Avkiran proposed technique after it has been developed an Input – Output Selection Method that uses Fractional Factorial design, which is an Statistical method to locate a best and optimal combination. In this study 2k – p Fractional Factorial design is applied to demonstrate the proposed method relates to the Manufacture of Pharmaceuticals, Medicinal Chemical and Botonical Products from the Manual of Annual Survey of Industries (ASI) 2016 – 2017.
Keywords: Data Envelopment Analysis (DEA), Decision Making Units (DMUs), CCR, BCC, Super Efficiency, Mahalanobis Distance.
Scope of the Article: Low-power design