Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33082
Appears in Collections:Management, Work and Organisation Journal Articles
Peer Review Status: Refereed
Title: A best-worst-method-based performance evaluation framework for manufacturing industry
Author(s): Khan, Sharfuddin
Kusi-Sarpong, Simonov
Naim, Iram
Ahmadi, Hadi Badri Ahmadi
Oyedijo, Adegboyega
Contact Email: a.oyedijo@stir.ac.uk
Keywords: Performance evaluation
best worst method
manufacturing
operations
management
social and stakeholders.
Issue Date: 30-Jul-2021
Date Deposited: 12-Aug-2021
Citation: Khan S, Kusi-Sarpong S, Naim I, Ahmadi HBA & Oyedijo A (2021) A best-worst-method-based performance evaluation framework for manufacturing industry. Kybernetes. https://doi.org/10.1108/K-03-2021-0202
Abstract: Purpose: The purpose of paper is to develop a performance evaluation framework for manufacturing industry to evaluate overall manufacturing performance. Design/methodology/approach: The Best Worst Method (BWM) is used to aid in developing a performance evaluation framework for manufacturing industry to evaluate their overall performance. Findings: The proposed BWM-based manufacturing performance evaluation framework is implemented in an Indian steel manufacturing company to evaluate their overall manufacturing performance. Operational performance of the organization is very consistent and range between 60% to 70% throughout the year. Management performance can be seen high in percentage in the first two quarter of the financial year ranging from 70% to 80% whereas a slight decrease in the management performance is observed in the 3rd and 4th quarter ranging from 60% to 70%. The social stakeholder performance has a peak in first quarter ranging from 80% to 100% as at start of financial year. Originality/value: This paper utilized BWM, a MCDM method in developing a performance evaluation index that integrates several categories of manufacturing and evaluates overall manufacturing performance. This is a novel contribution to BWM decision-making application.
DOI Link: 10.1108/K-03-2021-0202
Rights: Publisher policy allows this work to be made available in this repository. Published in Kybernetes by Emerald. The original publication is available at: https://doi.org/10.1108/K-03-2021-0202. This article is deposited under the Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0). Any reuse is allowed in accordance with the terms outlined by the licence (https://creativecommons.org/licenses/by-nc/4.0/) To reuse the AAM for commercial purposes, permission should be sought by contacting permissions@emeraldinsight.com.
Notes: Output Status: Forthcoming/Available Online
Licence URL(s): http://creativecommons.org/licenses/by-nc/4.0/

Files in This Item:
File Description SizeFormat 
FinalManuscriptBlind-R2.pdfFulltext - Accepted Version700.61 kBAdobe PDFView/Open



This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.