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Intelligent System for Assessing Organization’s Possibilities to Achieve Sustained Success

  • Inna Nurutdinova
  • Liubov Dimitrova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)

Abstract

The questions of creating intelligent information system designed for assessing an organization’s maturity level in the direction of achievement of stable development are considered. An unbiased evaluation of the possibility of achieving a stable success is an important task of any organization. The urgency of this problem is growing in the conditions of contemporary dynamically changing world and national economies. The choice of a linguistic approach to the solution of this problem, based on the application of the theory of fuzzy sets, has been substantiated. The architecture of the expert system and subsystem interrelations, including standard blocks, and also original subsystems, has been described. The problem solving algorithm on the basis of expert assessments of the main criteria, contributing to elimination of obstacles in the organization activity, has been suggested. We have considered the problems of presentation and quality analysis of the fuzzy expert information, determined linguistic variables, and developed membership functions. The base of knowledge has been created, a technique of fuzzy inference has been explained, an example of assessment of the organization’s maturity level on the basis of expert assessments of the main criteria has been provided. Application of the suggested intelligent system of monitoring the organization’s state makes it possible to react immediately to quickly changing conditions, in which the organization is functioning, correct both tactical methods and strategic aims.

Keywords

Intelligent system Organization maturity level Linguistic approach Linguistic variable Membership function Fuzzification Knowledge base Production rule Fuzzy inference Defuzzification 

References

  1. 1.
    Nacional’nyj standart RF GOST R ISO 9004-2010. Menedzhment dlya dostizheniya ustojchivogo uspekha organizacii (Managing for the sustained success of an organization. A quality management approach). Standartinform, Moscow (2011). (in Russian)Google Scholar
  2. 2.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  3. 3.
    Zadeh, L.: Knowledge representation in fuzzy logic. In: Yager, R.R., Zadeh L.A. (eds.) An Introduction to Fuzzy Logic Applications in Intelligent Systems, The Springer International Series in Engineering and Computer Science, vol. 165, pp. 1–27. Springer, New York (1992)Google Scholar
  4. 4.
    Nacional’nyj standart RF GOST R ISO 9001˗2015. Sistemy menedzhmenta kachestva Trebovaniya (Quality management systems. Requirements). Standartinform, Moskow (2015). (in Russian)Google Scholar
  5. 5.
    Hrehova, S., Vagaska, A.: Application of fuzzy principles in evaluating quality of manufacturing process. WSEAS Trans. Power Syst. 7(2), 50–59 (2012)Google Scholar
  6. 6.
    Borisova, L.V., Dimitrov, V.P., Nurutdinova, I.N., Serbin, D.M.: Osobennosti ehkspertnogo kontrolya kachestva v sfere obsluzhivaniya (Features of expert quality control in the service sector). In: sbornik nauchnyh trudov Mezhdunarodnoj molodezhnoj nauchno-prakticheskoj konferencii Kachestvo produkcii: kontrol’, upravlenie, povyshenie, planirovanie, pp. 110–113, ZAO “Universitetskaya kniga”, Kursk (2014). (in Russian)Google Scholar
  7. 7.
    Ding, J.-F.: Assessment of customer relationship management for global shipping carrier-based logistics service providers in Taiwan: an empirical study. WSEAS Trans. Syst. 11(6), 198–208 (2012)Google Scholar
  8. 8.
    Eremina, E.A., Vedernikov, D.N.: Informacionnaya sistema vybora postavshchika na osnove metoda nechyotkogo logicheskogo vyvoda (Information system for vendor selection based on fuzzy inference method). Sovremennye problemy nauki i obrazovaniya. 3, 294 (2013). (in Russian)Google Scholar
  9. 9.
    Zaharova, A.A.: Integral’naya ocenka innovacionnogo razvitiya regionov na osnove nechyotkih mnozhestv (Integral assessment of innovative development of regions based on fuzzy sets) Sovremennye problemy nauki i obrazovaniya. 3, 25 (2013). (in Russian)Google Scholar
  10. 10.
    Nurutdinova, I.N., Borisova D.V.: Nechetkoe modelirovanie ocenki mezhregional’noj ehkonomicheskoj integracii (Fuzzy modeling of the interregional economic integration assessment). КANT 4(25), 226–231 (2017). (in Russian)Google Scholar
  11. 11.
    Wang, G., Chen, S., Zhou, Z., Liu, J.: Modelling and analyzing trust conformity in e-commerce based on fuzzy logic. WSEAS Trans. Syst. 14, 1–10 (2015)Google Scholar
  12. 12.
    Dimitrov, V., Borisova, L., Nurutdinova, I.: Modelling of fuzzy expert information in the problem of a machine technological adjustment. In: MATEC Web of Conference 13. Ser. 13th International Scientific˗Technical Conference “Dynamic of Technical Systems”, DTS˗2017, P. 04009 (2017)CrossRefGoogle Scholar
  13. 13.
    Asai, K., Vatada, D., Sugeno, S.: Prikladnie nechetkie sistemi (Applied fuzzy systems). Mir, Moscow (1993). (in Russian)Google Scholar
  14. 14.
    Kofman, A.: Vvedenie v teoriyu nechyotkih mnozhestv (Introduction in the theory of fuzzy sets). Radio i svyaz’, Moscow (1982). (in Russian)Google Scholar
  15. 15.
    Borisova, L., Dimitrov, V., Nurutdinova, I.: Algorithm for assessing quality of fuzzy expert information. In: Proceedings of IEEE East˗West Design & Test Symposium (EWDTS 2017), Novi Sad, Serbia, pp. 319–322 (2017)Google Scholar
  16. 16.
    Averkin, A.N., Batyrshin, I.Z., Blishun, A.F., Silov, V.B., Tarasov, V.B.: Nechetkie mnojestva v modelyah upravleniya i iskusstvennogo intellekta (Fuzzy sets in the models of management and artificial intelligence). Nauka, Moscow (1986). (in Russian)zbMATHGoogle Scholar
  17. 17.
    Saaty, T.L., Kearns K.P.: Analiticheskoe planirovanie. Organizaciya sistem (Analytical Planning. The organization of Systems). Radio i svyaz’, Moscow (1993). (in Russian)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Don State Technical UniversityRostov-on-DonRussian Federation

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