IDEAS home Printed from https://ideas.repec.org/a/eee/oprepe/v9y2022ics2214716022000252.html
   My bibliography  Save this article

A novel methodology employed for ranking and consolidating performance indicators in holding companies with multiple power plants based on multi-criteria decision-making method

Author

Listed:
  • Murad, C.A.
  • Bellinello, M.M.
  • Silva, A.J.
  • Netto, A. Caminada
  • de Souza, G.F.M.
  • Nabeta, S.I.

Abstract

A large international energy corporation owning power operations in several countries, including several generation plants as well as transmission and distribution units in Brazil's energy matrix, adopts for quite some time now the policy that performance monitoring should be left to each individual operation's management. This decentralized approach is viewed as undoubtedly having a number of managerial advantages, so much, so that it is currently in force and performance indicators are being established locally. This approach, however, has the basic disadvantage that quantitative comparisons are hard to make between operations, i.e., adequate tactical comparative assessment at corporate level, with immediate and potential unfavorable implications. Therefore, the present work undertakes to investigate the possibility of consolidating a comprehensive set of indicators capable of accommodating more possibilities of comparison between different power generation plants. The research also employs an effective ranking method in order to take as much managerial advantage as possible of the new body of identified indicators. An application example involving a set of selected indicators is presented.

Suggested Citation

  • Murad, C.A. & Bellinello, M.M. & Silva, A.J. & Netto, A. Caminada & de Souza, G.F.M. & Nabeta, S.I., 2022. "A novel methodology employed for ranking and consolidating performance indicators in holding companies with multiple power plants based on multi-criteria decision-making method," Operations Research Perspectives, Elsevier, vol. 9(C).
    • Handle: RePEc:eee:oprepe:v:9:y:2022:i:c:s2214716022000252
    DOI: 10.1016/j.orp.2022.100254
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214716022000252
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.orp.2022.100254?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. Muchiri, Peter & Pintelon, Liliane & Gelders, Ludo & Martin, Harry, 2011. "Development of maintenance function performance measurement framework and indicators," International Journal of Production Economics, Elsevier, vol. 131(1), pages 295-302, May.
    2. Betti, Alessandro & Crisostomi, Emanuele & Paolinelli, Gianluca & Piazzi, Antonio & Ruffini, Fabrizio & Tucci, Mauro, 2021. "Condition monitoring and predictive maintenance methodologies for hydropower plants equipment," Renewable Energy, Elsevier, vol. 171(C), pages 246-253.
    3. Dionysios Pramangioulis & Konstantinos Atsonios & Nikos Nikolopoulos & Dimitrios Rakopoulos & Panagiotis Grammelis & Emmanuel Kakaras, 2019. "A Methodology for Determination and Definition of Key Performance Indicators for Smart Grids Development in Island Energy Systems," Energies, MDPI, vol. 12(2), pages 1-22, January.
    4. Forman, Ernest & Peniwati, Kirti, 1998. "Aggregating individual judgments and priorities with the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 108(1), pages 165-169, July.
    5. Jafar Rezaei, 2020. "A Concentration Ratio for Nonlinear Best Worst Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 891-907, May.
    6. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    7. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    8. Thomas L. Saaty & Luis G. Vargas, 2012. "Models, Methods, Concepts & Applications of the Analytic Hierarchy Process," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4614-3597-6, December.
    9. Mohammadi, Majid & Rezaei, Jafar, 2020. "Bayesian best-worst method: A probabilistic group decision making model," Omega, Elsevier, vol. 96(C).
    10. Katharina Schmidt & Ana Babac & Frédéric Pauer & Kathrin Damm & J-Matthias von der Schulenburg, 2016. "Measuring patients’ priorities using the Analytic Hierarchy Process in comparison with Best-Worst-Scaling and rating cards: methodological aspects and ranking tasks," Health Economics Review, Springer, vol. 6(1), pages 1-11, December.
    11. Mi, Xiaomei & Tang, Ming & Liao, Huchang & Shen, Wenjing & Lev, Benjamin, 2019. "The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?," Omega, Elsevier, vol. 87(C), pages 205-225.
    Full references (including those not matched with items on IDEAS)

    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. Wu, Qun & Liu, Xinwang & Zhou, Ligang & Qin, Jindong & Rezaei, Jafar, 2024. "An analytical framework for the best–worst method," Omega, Elsevier, vol. 123(C).
    2. Corrente, Salvatore & Greco, Salvatore & Rezaei, Jafar, 2024. "Better decisions with less cognitive load: The Parsimonious BWM," Omega, Elsevier, vol. 126(C).
    3. Vieira, Fabiana C. & Ferreira, Fernando A.F. & Govindan, Kannan & Ferreira, Neuza C.M.Q.F. & Banaitis, Audrius, 2022. "Measuring urban digitalization using cognitive mapping and the best worst method (BWM)," Technology in Society, Elsevier, vol. 71(C).
    4. Kheybari, Siamak & Javdanmehr, Mahsa & Rezaie, Fariba Mahdi & Rezaei, Jafar, 2021. "Corn cultivation location selection for bioethanol production: An application of BWM and extended PROMETHEE II," Energy, Elsevier, vol. 228(C).
    5. Mohammadi, Majid & Rezaei, Jafar, 2020. "Bayesian best-worst method: A probabilistic group decision making model," Omega, Elsevier, vol. 96(C).
    6. Burak Can Altay & Abdullah Erdem Boztas & Abdullah Okumuş & Muhammet Gul & Erkan Çelik, 2023. "How Will Autonomous Vehicles Decide in Case of an Accident? An Interval Type-2 Fuzzy Best–Worst Method for Weighting the Criteria from Moral Values Point of View," Sustainability, MDPI, vol. 15(11), pages 1-20, June.
    7. Liang, Fuqi & Brunelli, Matteo & Rezaei, Jafar, 2020. "Consistency issues in the best worst method: Measurements and thresholds," Omega, Elsevier, vol. 96(C).
    8. Besharati Fard, Moein & Moradian, Parisa & Emarati, Mohammadreza & Ebadi, Mehdi & Gholamzadeh Chofreh, Abdoulmohammad & Klemeŝ, Jiří Jaromír, 2022. "Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: A case study Guilan province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    9. Xiao-Kang Wang & Wen-Hui Hou & Chao Song & Min-Hui Deng & Yong-Yi Li & Jian-Qiang Wang, 2021. "BW-MaxEnt: A Novel MCDM Method for Limited Knowledge," Mathematics, MDPI, vol. 9(14), pages 1-17, July.
    10. Chong Li & He Huang & Ya Luo, 2022. "An Integrated Two-Dimension Linguistic Intuitionistic Fuzzy Decision-Making Approach for Unmanned Aerial Vehicle Supplier Selection," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    11. Sarbast Moslem & Muhammet Gul & Danish Farooq & Erkan Celik & Omid Ghorbanzadeh & Thomas Blaschke, 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    12. Lin, Xueshan & Huang, Tao & Bompard, Ettore & Wang, Beibei & Zheng, Yaxian, 2023. "Ex-ante market power evaluation and mitigation in day-ahead electricity market considering market maturity levels," Energy, Elsevier, vol. 278(C).
    13. Göçmen Polat, Elifcan & Yücesan, Melih & Gül, Muhammet, 2023. "A comparative framework for criticality assessment of strategic raw materials in Turkey," Resources Policy, Elsevier, vol. 82(C).
    14. Zsombor Szádoczki & Sándor Bozóki & Patrik Juhász & Sergii V. Kadenko & Vitaliy Tsyganok, 2023. "Incomplete pairwise comparison matrices based on graphs with average degree approximately 3," Annals of Operations Research, Springer, vol. 326(2), pages 783-807, July.
    15. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    16. Vineet Kaushik & Shobha Tewari, 2023. "Modeling Opportunity Indicators Fostering Social Entrepreneurship: A Hybrid Delphi and Best-Worst Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 168(1), pages 667-698, August.
    17. Jian Wang & Jin-Chun Huang & Shan-Lin Huang & Gwo-Hshiung Tzeng & Ting Zhu, 2021. "Improvement Path for Resource-Constrained Cities Identified Using an Environmental Co-Governance Assessment Framework Based on BWM-mV Model," IJERPH, MDPI, vol. 18(9), pages 1-30, May.
    18. Gholamreza Haseli & Reza Sheikh & Jianqiang Wang & Hana Tomaskova & Erfan Babaee Tirkolaee, 2021. "A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    19. Shashank Bansal & Mohul Mukhopadhyay & Shipra Maurya, 2023. "Strategic drivers for sustainable implementation of carbon trading in India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4411-4435, May.
    20. Bai, Chunguang & Zhu, Qingyun & Sarkis, Joseph, 2021. "Joint blockchain service vendor-platform selection using social network relationships: A multi-provider multi-user decision perspective," International Journal of Production Economics, Elsevier, vol. 238(C).

    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:oprepe:v:9:y:2022:i:c:s2214716022000252. 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.journals.elsevier.com/operations-research-perspectives .

    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.