The paper objective is the preparation, processing and analysis of the expert information to determine the organization maturity level on the basis of the self-assessment. GOST R ISO 9004–2010 crite-ria are used to establish the maturity level. The following tasks are set: to determine the sequence of actions for assessing the maturity level of the organization, and to develop methods of generating the expert information that gives an adequate view of the actual situa-tion. A mathematical apparatus of fuzzy sets theory is used to solve the problems. The preparation and analysis of the expert information is the gist of the fuzzification stage in creating an expert system. The solution to the problem is illustrated by a model example. Linguistic variables and additive and multiplicative indices of conformity are determined. Membership functions and matrices of consistency and of fuzziness indexes are constructed. The program system of expert information input is used to obtain these characteristics. Sufficiently high quality of the expert information and its applicability on the subsequent stages of the expert system operation are determined. The proposed methods can be applied both for the expert information generation under determining the organization maturity level, and for solving any problem of the development of the expert systems that operate on the basis of the fuzzy expert information.
expert system, linguistic variable, membership function, consistency indices, maturity level of organization.
В целях достижения и сохранения устойчивого успеха в процессе функционирования организации проводится мониторинг ее состояния и оценка перспектив. Обеспечение успеха организации предполагает оптимальное соотношение высоких показателей удовлетворенности всех заинтересованных сторон: потребителей, владельцев, акционеров, поставщиков, партнеров и общества. В условиях сложной и нестабильной обстановки эта задача приобретает большую актуальность, для ее решения необходим четкий и практически автоматизированный алгоритм. Для определения состояния организации и уровня зрелости с точки зрения достижения устойчивого успеха используется самооценка. В результате самооценки организация получает информацию, полезную для всех заинтересованных сторон. В частности, выявляются сильные и слабые стороны, определяются приоритеты деятельности, корректируется стратегия развития.Методика самооценки установлена стандартом ГОСТ Р ИСО 9004–2010 [1]. В ней определены 5 уровней зрелости, которые выставляются по каждому направлению деятельности, и 6 основных критериев, способных помочь организации решить внутренние проблемы.
1. GOST R ISO 9004-2010. Menedzhment dlya dostizheniya ustoychivogo uspekha organizatsii. [GOST R ISO 9004-2010. Managing for the sustained success of an organization -A quality management approach.] Federal Agency for Technical Regulation and Metrology. Moscow: Standartinform, 2011, 32 p. (in Russian).
2. Shumskaya, N.N., et al. O podkhode k otsenke urovnya zrelosti organizatsii s ispol´zovaniem teorii nechetkikh mnozhestv. [On approach to assessment of organization maturity level with the use of fuzzy-set theory.] Sostoyanie i perspektivy razvitiya sel´skokhozyaystvennogo mashinostroeniya: sb. statey 9-y mezhdunar. nauch.-prakt. konf. v ramkakh 19-y mezhdunar. agroprom. vystavki «Interagromash-2016». [Current state and development trends of agricultural ma-chinery: Coll. of sci. papers of 8th Int. Sci.-Pract. Conf. within the framework of 18th Int. Agroindustrial Exhibition “Interagromash-2016”.] Rostov-on-Don, 2016, pp. 364–366 (in Russian).
3. Averkin, A.N., et al. Nechetkie mnozhestva v modelyakh upravleniya i iskusstvennogo intellekta. [Fuzzy sets in models of control and artificial intelligence.] Pospelov, D.A., ed. Moscow: Nauka, 1986, 312 p. (in Russian).
4. Borisov, A.N., et al. Obrabotka nechetkoy informatsii v sistemakh prinyatiya resheniy. [Fuzzy information pro-cessing in the decision-making systems.] Moscow: Radio i svyaz´, 1989, 394 p. (in Russian).
5. Asai, К., Sugeno, S. Prikladnye nechetkie sistemy.[Applied fuzzy systems.] Moscow: Mir, 1993, 368 p. (in Rus-sian).
6. Dimitrov, V.P. Sovershenstvovanie metodov tekhnicheskogo obsluzhivaniya zernouborochnoy tekhniki na osnove ekspertnykh sistem : dis. … d-ra tekhn. nauk. [Improving maintenance methods of harvesters based on expert sys-tems: Dr.Sci. (Eng.) diss.] Rostov-on-Don, 2002, 300 p. (in Russian).
7. Tugengold, А.К., Dimitrov, V.P., Borisova, L.V. K voprosu postroeniya nechetkoy ekspertnoy sistemy produk-tsionnogo tipa dlya tekhnologicheskoy regulirovki mashin. [To the question of fuzzy expert system constructing production type for technological adjustment of machines.] Vestnik of DSTU, 2008, vol. 8, no. 3 (38), pp. 419–426 (in Russian).
8. Hrehova, S.,Vagaska, A. Application of fuzzy principles in evaluating quality of manufacturing process . WSEAS Transactions on Power Systems, 2012, vol. 7, pp. 50–59.
9. Borisova, L.V. et al. Osobennosti ekspertnogo kontrolya kachestva v sfere obsluzhivaniya. [Features of export quality control in the service sector.] Kachestvo produktsii: kontrol´, upravlenie, povyshenie, planirovanie : sb. nauch. tr. mezhdunar. nauch.-prakt. konf. [Quality of products: control, management, improvement, planning: Coll. sci. papers Int. Sci.-Pract. Conf.] Kursk, 2014, pp. 110–113 (in Russian).
10. Shumskaya, N.N., et al. O podkhode k ekspertnoy otsenke kachestva znaniy. [On approach to the expert assess-ment of the knowledge quality.] Sostoyanie i perspektivy razvitiya sel´skokhozyaystvennogo mashinostroeniya: sb. statey 8-y mezhdunar. nauch.-prakt. konf. v ramkakh 18-y mezhdunar. agroprom. vystavki «Interagromash-2015». [Current state and development trends of agricultural machinery: Proc. 8th Int. Sci.-Pract. Conf. within the framework of 18th Int. Agroindustrial Exhibition “Interagromash-2015”.] Rostov-on-Don, 2015, pp. 321–324 (in Russian).
11. Zadeh, L.-A. Fuzzy sets. Fuzzy sets and systems, 1965, no. 8, pp. 338–353.
12. Yager, R.-R., ed., Zadeh, L.-A. Knowledge representation in fuzzy logic. An Introduction to Fuzzy Logic Applica-tions in Intelligent Systems. New York: Springer, 1992, vol. 165, pp. 1–27 (The Springer International Series in Engineering and Computer Science).
13. Dimitrov, V.P., Borisova, L.V. Teoreticheskie i prikladnye aspekty razrabotki ekspertnykh sistem dlya tekhnich-eskogo obsluzhivaniya mashin. [Theoretical and applied aspects of the development of expert systems for maintenance of machinery.] Rostov-on-Don: DSTU Publ. Centre, 2007, 202 p. (in Russian).
14. Dimitrov, V.P., Borisova, L.V., Nurutdinova, I.N. O metodike fazzifikatsii nechetkoy ekspertnoy informatsii. [On expert information fuzzification method.] Vestnik of DSTU, 2012, vol. 11, no. 2 (62), pp. 46–50 (in Russian).
15. Dimitrov, V.P., Borisova, L.V., Nurutdinova, I.N. O metodike predstavleniya nechetkikh ekspertnykh znaniy. [On method of representation of fuzzy expertise.] Vestnik of DSTU, 2014, vol. 14, no. 4 (79), pp. 93–102 (in Russian).
16. Dimitrov, V.P., Borisova, L.V., Nurutdinova, I.N. Metodika otsenki soglasovannosti modeley nechetkikh ek-spertnykh znaniy. [Methods for estimating coordination of fuzzy expert knowledge models.] Vestnik of DSTU, 2010, vol. 10, no. 2 (45), pp. 205–216 (in Russian).
17. Dimitrov, V.P., Borisova, L.V., Nurutdinova, I.N. O pokazatelyakh soglasovannosti modeley ekspertnogo otsenivaniya. [On reconciliation expert estimation models.] Sostoyanie i perspektivy razvitiya sel´skokhozyaystvennogo mashinostroeniya: sb. statey 3-y mezhdunar. nauch.-prakt. konf. v ramkakh 13-y mezhdunar. agroprom. vystavki «Interagromash-2010». [Current state and development trends of agricultural machinery: Proc. 3rd Int. Sci.-Pract. Conf. within the framework of 13th Int. Agroindustrial Exhibition “Interagromash-2010”.] Rostov-on-Don, 2010, pp. 283–286 (in Russian).
18. Dimitrov, V.P., et al. Programmnaya sistema dlya vvoda ekspertnykh znaniy. [Programmed system for input of expert knowledge.] Vestnik of DSTU, 2011, vol. 11, no. 1 (52), pp. 83–90 (in Russian).
19. Makarov, I.M., et al. Iskusstvennyy intellekt i intellektual´nye sistemy upravleniya. [Artificial intelligence and in-telligent control systems.]. Moscow: Nauka, 2006, 333 p. (in Russian).
20. Dimitrov, V.P., Borisova, L.V., Nurutdinova, I.N. O metodike defazzifikatsii nechetkoy ekspertnoy informatsii. [On defuzzification method in fuzzy expert information processing.] Vestnik of DSTU, 2010, vol. 10, no. 6 (49), pp. 868–878 (in Russian).
