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The Possibilities of Using Scoring to Determine the Relevance of Software Development Tenders

Author

Listed:
  • Ivan Tikshaev

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Roman Kulshin

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Gennadii Volokitin

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Pavel Senchenko

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Anatoly Sidorov

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

Abstract

The issue of searching for tender proposals satisfying the conditions of selection on the basis of the relevance assessment algorithm is considered. The algorithm is based on a mathematical scoring model. The approbation of the model based on the historical data of the software company is presented. The possibility of using such a method to determine relevance is proved. The assumption is made about the possibility of using scoring to evaluate tenders not only for the development of software products but also in other market segments.

Suggested Citation

  • Ivan Tikshaev & Roman Kulshin & Gennadii Volokitin & Pavel Senchenko & Anatoly Sidorov, 2022. "The Possibilities of Using Scoring to Determine the Relevance of Software Development Tenders," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4800-:d:1006124
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    References listed on IDEAS

    as
    1. Mohammad H. Saleh & Jamil J. Jaber & Abdullah A. Al-khawaldeh, 2016. "The Role of Credit Scoring, Cost and Product Discrimination in Improving the Competitiveness of Jordanian Insurance Companies," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(5), pages 252-259, May.
    2. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    3. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    4. Thomas, Lyn C. & Edelman, David B. & Crook, Jonathan, 2004. "Readings in Credit Scoring: Foundations, Developments, and Aims," OUP Catalogue, Oxford University Press, number 9780198527978.
    5. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
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