IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v233y2021ics0925527320303170.html
   My bibliography  Save this article

Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer

Author

Listed:
  • Brint, Andrew
  • Genovese, Andrea
  • Piccolo, Carmela
  • Taboada-Perez, Gerardo J.

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.

Suggested Citation

  • Brint, Andrew & Genovese, Andrea & Piccolo, Carmela & Taboada-Perez, Gerardo J., 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, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:proeco:v:233:y:2021:i:c:s0925527320303170
    DOI: 10.1016/j.ijpe.2020.107967
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527320303170
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2020.107967?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Paolo Taticchi & Patrizia Garengo & Sai S. Nudurupati & Flavio Tonelli & Roberto Pasqualino, 2015. "A review of decision-support tools and performance measurement and sustainable supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 53(21), pages 6473-6494, November.
    2. Mohammed, Ahmed & Harris, Irina & Govindan, Kannan, 2019. "A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation," International Journal of Production Economics, Elsevier, vol. 217(C), pages 171-184.
    3. Jollands, Nigel & Lermit, Jonathan & Patterson, Murray, 2004. "Aggregate eco-efficiency indices for New Zealand – a Principal Components Analysis," 2004 Conference, June 25-26, 2004, Blenheim, New Zealand 97773, New Zealand Agricultural and Resource Economics Society.
    4. Li-Chang Hsu, 2013. "Investment decision making using a combined factor analysis and entropy-based topsis model," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 14(3), pages 448-466, June.
    5. Okoshi, Cleina Yayoe & Pinheiro de Lima, Edson & Gouvea Da Costa, Sergio Eduardo, 2019. "Performance cause and effect studies: Analyzing high performance manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 27-41.
    6. Jinqiu Li & Qingqin Wang & Hao Zhou, 2020. "Establishment of Key Performance Indicators for Green Building Operations Monitoring—An Application to China Case Study," Energies, MDPI, vol. 13(4), pages 1-20, February.
    7. Krakovics, Fabio & Eugenio Leal, José & Mendes Jr., Paulo & Lorenzo Santos, Rafael, 2008. "Defining and calibrating performance indicators of a 4PL in the chemical industry in Brazil," International Journal of Production Economics, Elsevier, vol. 115(2), pages 502-514, October.
    8. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    9. Estampe, Dominique & Lamouri, Samir & Paris, Jean-Luc & Brahim-Djelloul, Sakina, 2013. "A framework for analysing supply chain performance evaluation models," International Journal of Production Economics, Elsevier, vol. 142(2), pages 247-258.
    10. Athena Forghani & Seyed Jafar Sadjadi & Babak Farhang Moghadam, 2018. "A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: A case study," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-17, August.
    11. Nicole Stricker & Fabio Echsler Minguillon & Gisela Lanza, 2017. "Selecting key performance indicators for production with a linear programming approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5537-5549, October.
    12. Schmitz, J. & Platts, K. W., 2004. "Supplier logistics performance measurement: Indications from a study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 89(2), pages 231-243, May.
    13. Lai, Young-Jou & Liu, Ting-Yun & Hwang, Ching-Lai, 1994. "TOPSIS for MODM," European Journal of Operational Research, Elsevier, vol. 76(3), pages 486-500, August.
    14. Genovese, Andrea & Acquaye, Adolf A. & Figueroa, Alejandro & Koh, S.C. Lenny, 2017. "Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications," Omega, Elsevier, vol. 66(PB), pages 344-357.
    15. I. T. Jolliffe, 1973. "Discarding Variables in a Principal Component Analysis. Ii: Real Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 21-31, March.
    16. Gunasekaran, A. & Patel, C. & McGaughey, Ronald E., 2004. "A framework for supply chain performance measurement," International Journal of Production Economics, Elsevier, vol. 87(3), pages 333-347, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Snežana Nestić & Ranka Gojković & Tijana Petrović & Danijela Tadić & Predrag Mimović, 2022. "Quality Performance Indicators Evaluation and Ranking by Using TOPSIS with the Interval-Intuitionistic Fuzzy Sets in Project-Oriented Manufacturing Companies," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    2. Diogo Rodrigues & Radu Godina & Pedro Espadinha da Cruz, 2021. "Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    3. Bruno, Giuseppe & Diglio, Antonio & Piccolo, Carmela & Pipicelli, Eduardo, 2023. "A reduced Composite Indicator for Digital Divide measurement at the regional level: An application to the Digital Economy and Society Index (DESI)," Technological Forecasting and Social Change, Elsevier, vol. 190(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hald, Kim Sundtoft & Mouritsen, Jan, 2018. "The evolution of performance measurement systems in a supply chain: A longitudinal case study on the role of interorganisational factors," International Journal of Production Economics, Elsevier, vol. 205(C), pages 256-271.
    2. Jędrzej Charłampowicz, 2018. "Supply Chain Efficiency On The Maritime Container Shipping Markets – Selected Issues," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 18, pages 357-368.
    3. Dennis Vegter & Jos van Hillegersberg & Matthias Olthaar, 2021. "Performance Measurement Systems for Circular Supply Chain Management: Current State of Development," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    4. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    5. Kayakutlu, Gulgun & Buyukozkan, Gulcin, 2011. "Assessing performance factors for a 3PL in a value chain," International Journal of Production Economics, Elsevier, vol. 131(2), pages 441-452, June.
    6. Martínez-Ventura, Constanza & Mariño-Martínez, Ricardo & Miguélez-Márquez, Javier, 2023. "Redundancy of Centrality Measures in Financial Market Infrastructures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(4).
    7. Bauer, Jan O. & Drabant, Bernhard, 2021. "Principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    8. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    9. Valenzuela, Lionel & Maturana, Sergio, 2016. "Designing a three-dimensional performance measurement system (SMD3D) for the wine industry: A Chilean example," Agricultural Systems, Elsevier, vol. 142(C), pages 112-121.
    10. Adivar, Burcu & Hüseyinoğlu, Işık Özge Yumurtacı & Christopher, Martin, 2019. "A quantitative performance management framework for assessing omnichannel retail supply chains," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 257-269.
    11. Seiler, A. & Papanagnou, C. & Scarf, P., 2020. "On the relationship between financial performance and position of businesses in supply chain networks," International Journal of Production Economics, Elsevier, vol. 227(C).
    12. Zhiwen Su & Mingyu Zhang & Wenbing Wu, 2021. "Visualizing Sustainable Supply Chain Management: A Systematic Scientometric Review," Sustainability, MDPI, vol. 13(8), pages 1-25, April.
    13. Diego Bernardo Avanzini, 2009. "Designing Composite Entrepreneurship Indicators: An Application Using Consensus PCA," WIDER Working Paper Series RP2009-41, World Institute for Development Economic Research (UNU-WIDER).
    14. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2019. "Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks," International Journal of Production Economics, Elsevier, vol. 212(C), pages 19-38.
    15. Braz, Renata Gomes Frutuoso & Scavarda, Luiz Felipe & Martins, Roberto Antonio, 2011. "Reviewing and improving performance measurement systems: An action research," International Journal of Production Economics, Elsevier, vol. 133(2), pages 751-760, October.
    16. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    17. Schnetzler, Matthias J. & Sennheiser, Andreas & Schonsleben, Paul, 2007. "A decomposition-based approach for the development of a supply chain strategy," International Journal of Production Economics, Elsevier, vol. 105(1), pages 21-42, January.
    18. Manju Saroha & Dixit Garg & Sunil Luthra, 2022. "Analyzing the circular supply chain management performance measurement framework: the modified balanced scorecard technique," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 951-960, June.
    19. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    20. António Pedro Duarte Silva, 2002. "Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons," Computational Statistics, Springer, vol. 17(2), pages 251-271, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:233:y:2021:i:c:s0925527320303170. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.