Title: Integrated grey entropy and COPRAS methods for machine selection decision problem

Authors: Elif Çirkin; Aşkın Özdağoğlu; Imran Patel; Mukund Nilakantan Janardhanan

Addresses: Department of Business Administration, Faculty of Business, Dokuz Eylül University, Tınaztepe Campus, 35390 Buca/IZMIR, Turkey ' Department of Business Administration, Faculty of Business, Dokuz Eylül University, Tınaztepe Campus, 35390 Buca/IZMIR, Turkey ' School of Engineering, University of Leicester, University Road, Leicester, LE1 7RH, UK ' School of Engineering, University of Leicester, University Road, Leicester, LE1 7RH, UK

Abstract: Decision-making processes play a significant role in the success or failure of any organisation. In today's globally competitive environment, organisations embark upon decision-making procedures and techniques facilitating them to survive and compete in such a harsh environment. Moreover, prompt, proper, and dynamic decisions enable effective and efficient management of firms' operations. Machine selection decision problem has been proven as a critical factor for both manufacturing and maintenance processes. A variety of decision-making methods and techniques have been introduced and developed in the last few years in order to cope with various decision issues. Within the scope of this study, an integrated grey entropy - complex proportional assessment (COPRAS) approach has been employed for selecting the appropriate machine alternative in a milling machine manufacturing industry. The performance values and potential supplier alternatives have been collected via focus group study conducted with the purchasing managers of the company. The results provide applicable information for purchasing department within the company as well as other firms facing similar decision problems.

Keywords: multi-criteria decision-making; MCDM; milling; manufacturing; grey entropy; machine selection.

DOI: 10.1504/IJSOM.2023.130177

International Journal of Services and Operations Management, 2023 Vol.44 No.4, pp.571 - 586

Received: 16 Jul 2020
Accepted: 10 Jan 2021
Published online: 06 Apr 2023 *

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