Brint, A. orcid.org/0000-0002-8863-407X, Genovese, A. orcid.org/0000-0002-5652-4634, Piccolo, C. et al. (1 more author) (2021) Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer. International Journal of Production Economics, 233. 107967. ISSN 0925-5273
Abstract
The recent trend towards collecting large amounts of data potentially allows organisations to identify previously unknown data patterns that can lead to significant improvements in their performance. However, carrying on collecting this data over time and across numerous locations is expensive. Consequently, when monitoring performance, organisations can be faced with a dichotomy between continuing to collect large amounts of data or whether to use a much reduced set of data. This is a particular problem with Key Performance Indicators (KPIs). Additionally, too many indicators can lead to difficulty in data interpretation and significant overlaps between the indicators, making the understanding and managing of changes in performance more difficult. In this paper, a novel statistical approach is introduced based on the use of Principal Component Analysis (PCA) to reduce the number of KPIs, followed by TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) for validating the results. It is applied to the case of a multinational automotive component manufacturer where 28 KPIs were reduced to 8. The performance of the original set of 28 KPIs was compared with that of the reduced set of 8 KPIs. The peaks of the two TOPSIS time-series coincided, and there was a high correlation between them. Therefore, having the extra 20 indicators provided little extra precision for the considered time interval. Hence, the approach is a valuable tool in helping to reduce a large number of KPIs down to a more practical and useable number.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Published by Elsevier B.V. This is an author produced version of a paper subsequently published in International Journal of Production Economics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Key performance indicators; TOPSIS; Principal component analysis; Supply chain management |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jan 2021 12:26 |
Last Modified: | 21 Apr 2022 00:39 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ijpe.2020.107967 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167916 |