IDEAS home Printed from https://ideas.repec.org/a/gam/jadmsc/v13y2023i2p56-d1064601.html
   My bibliography  Save this article

GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises

Author

Listed:
  • Jones Luís Schaefer

    (Department of Production Engineering, Federal University of Santa Maria (UFSM), Santa Maria 97105-900, Brazil)

  • Paulo Roberto Tardio

    (Production and Systems Engineering Graduate Program, Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, Brazil)

  • Ismael Cristofer Baierle

    (Graduate Program in Agro-Industrial Systems and Processes, Federal University of Rio Grande, Rio Grande 96203-900, Brazil)

  • Elpidio Oscar Benitez Nara

    (Production and Systems Engineering Graduate Program, Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, Brazil)

Abstract

The adoption of models based on key performance indicators to diagnose and evaluate the competitiveness of companies has been presented as a trend in the operations’ management. These models are structured with different variables in complex interrelationships, making diagnosis and monitoring difficult due to the number of variables involved, which is one of the main management challenges of Small and Medium-sized Enterprises. In this sense, this article proposes the Gain Information Artificial Neural Network (GIANN) method. GIANN is a method to optimize the number of variables of assessment models for the competitiveness and operational performance of Small and Medium-sized Enterprises. GIANN is a hybrid methodology combining Multi-attribute Utility Theory with Entropy and Information Gain concepts and computational modeling through Multilayer Perceptron Artificial Neural Network. The model used in this article integrates variables such as fundamental points of view, critical success factors, and key performance indicators. GIANN was validated through a survey of managers of Small and Medium-sized Enterprises in Southern Brazil. The initial model was adjusted, reducing the number of key performance indicators by 39% while maintaining the accuracy of the results of the competitiveness measurement. With GIANN, the number of variables to be monitored decreases considerably, facilitating the management of Small and Medium-sized Enterprises.

Suggested Citation

  • Jones Luís Schaefer & Paulo Roberto Tardio & Ismael Cristofer Baierle & Elpidio Oscar Benitez Nara, 2023. "GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises," Administrative Sciences, MDPI, vol. 13(2), pages 1-16, February.
    • Handle: RePEc:gam:jadmsc:v:13:y:2023:i:2:p:56-:d:1064601
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-3387/13/2/56/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-3387/13/2/56/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ningxuan Kang & Cong Zhao & Jingshan Li & John A. Horst, 2016. "A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6333-6350, November.
    2. Doyeong Kim & Wongyun Oh & Jiyeong Yun & Jongyoung Youn & Sunglok Do & Donghoon Lee, 2021. "Development of Key Performance Indicators for Measuring the Management Performance of Small Construction Firms in Korea," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    3. Bana e Costa, Carlos A. & Ensslin, Leonardo & Correa, Emerson C. & Vansnick, Jean-Claude, 1999. "Decision Support Systems in action: Integrated application in a multicriteria decision aid process," European Journal of Operational Research, Elsevier, vol. 113(2), pages 315-335, March.
    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. Huwei Wen & Yutong Liu & Fengxiu Zhou, 2023. "Promoting the International Competitiveness of Small and Medium-Sized Enterprises Through Cross-Border E-Commerce Development," SAGE Open, , vol. 13(4), pages 21582440231, November.

    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. Ensslin, Leonardo & Dezem, Vinicius & Dutra, Ademar & Ensslin, Sandra R. & Somensi, Karine, 2017. "Management support for agricultural enterprises: a case study for a fruit-producing company," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 20(4), March.
    2. Tsoukias, Alexis, 2008. "From decision theory to decision aiding methodology," European Journal of Operational Research, Elsevier, vol. 187(1), pages 138-161, May.
    3. Maria Franca Norese & Diana Rolando & Rocco Curto, 2023. "DIKEDOC: a multicriteria methodology to organise and communicate knowledge," Annals of Operations Research, Springer, vol. 325(2), pages 1049-1082, June.
    4. 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.
    5. Carayannis, Elias G. & Ferreira, Fernando A.F. & Bento, Paulo & Ferreira, João J.M. & Jalali, Marjan S. & Fernandes, Bernardo M.Q., 2018. "Developing a socio-technical evaluation index for tourist destination competitiveness using cognitive mapping and MCDA," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 147-158.
    6. Gomes, Luís S. & Santos, Sérgio P. & Coelho, Luís Serra & Rebelo, Efigénio L., 2023. "Using MCDA to assist an Intermunicipal community develop a resilience strategy in face of the pandemic caused by the SARS-CoV-2," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    7. Grabisch, Michel & Labreuche, Christophe & Vansnick, Jean-Claude, 2003. "On the extension of pseudo-Boolean functions for the aggregation of interacting criteria," European Journal of Operational Research, Elsevier, vol. 148(1), pages 28-47, July.
    8. Dimitrios Gouglas & Kendall Hoyt & Elizabeth Peacocke & Aristidis Kaloudis & Trygve Ottersen & John-Arne Røttingen, 2019. "Setting Strategic Objectives for the Coalition for Epidemic Preparedness Innovations: An Exploratory Decision Analysis Process," Service Science, INFORMS, vol. 49(6), pages 430-446, November.
    9. Alexis Tsoukiàs, 2007. "On the concept of decision aiding process: an operational perspective," Annals of Operations Research, Springer, vol. 154(1), pages 3-27, October.
    10. Bustinza, Oscar F. & Opazo-Basaez, Marco & Tarba, Shlomo, 2022. "Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance," Technovation, Elsevier, vol. 118(C).
    11. Sunkuk Kim, 2021. "Technology and Management for Sustainable Buildings and Infrastructures," Sustainability, MDPI, vol. 13(16), pages 1-3, August.
    12. Jean-Charles Billaut & Denis Bouyssou & Philippe Vincke, 2009. "Should you believe in the Shanghai ranking? An MCDM view," Working Papers hal-00877050, HAL.
    13. Meinard, Y. & Tsoukiàs, A., 2019. "On the rationality of decision aiding processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1074-1084.
    14. De Brucker, Klaas & Macharis, Cathy & Verbeke, Alain, 2013. "Multi-criteria analysis and the resolution of sustainable development dilemmas: A stakeholder management approach," European Journal of Operational Research, Elsevier, vol. 224(1), pages 122-131.
    15. María Carmen Carnero & Andrés Gómez, 2019. "Optimization of Decision Making in the Supply of Medicinal Gases Used in Health Care," Sustainability, MDPI, vol. 11(10), pages 1-31, May.
    16. J. Vicente Tébar-Rubio & F. Javier Ramírez & M. José Ruiz-Ortega, 2023. "Conducting Action Research to Improve Operational Efficiency in Manufacturing: The Case of a First-Tier Automotive Supplier," Systemic Practice and Action Research, Springer, vol. 36(3), pages 427-459, June.
    17. María Carmen Carnero, 2020. "Fuzzy Multicriteria Models for Decision Making in Gamification," Mathematics, MDPI, vol. 8(5), pages 1-23, May.
    18. Ozum Egilmez & Gozde Koca, 2021. "Drivers, Challenges, and Integration of Health 4.0 Societal Engagement: Evidence from Turkey," Istanbul Business Research, Istanbul University Business School, vol. 50(1), pages 127-148, May.
    19. Wagner Silva Costa & Plácido R. Pinheiro & Nádia M. dos Santos & Lucídio dos A. F. Cabral, 2023. "Aligning the Goals Hybrid Model for the Diagnosis of Mental Health Quality," Sustainability, MDPI, vol. 15(7), pages 1-31, March.
    20. L M P Neves & A G Martins & C H Antunes & L C Dias, 2004. "Using SSM to rethink the analysis of energy efficiency initiatives," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 968-975, September.

    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:gam:jadmsc:v:13:y:2023:i:2:p:56-:d:1064601. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.