The Mathematical Model for Describing the Principles of Enterprise Management “Just in Time, Design to Cost, Risks Management”

  • Igor LutoshkinEmail author
  • Svetlana Lipatova
  • Yuriy Polyanskov
  • Nailya Yamaltdinova
  • Margarita Yardaeva
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)


The formalization of the principles “just in time”, “design to cost” and “risks management” is described in the form of a mathematical model. These principles are chosen on the basis of the analysis of the management methodologies used in the practice of industrial enterprises. The model is recommended to be used in the design of information systems to support management decisions. The model can be built on the basis of different methods: stochastic, parametric, heuristic, etc. The proposed approach allows to use heterogeneous submodels for the assessment and management task based on one or more specific criteria: just in time, design to cost and risks management.

The proposed mathematical model is a model of the upper level of abstraction, in practical use it is required to take into account the task being solved, selected criteria and available limitations. Practical implementation of the proposed model and its introduction into digital production presuppose the implementation and monitoring with the help of an information system. The use of such a methodology is advisable at large enterprises, in particular machine building and aviation.


Assessment of the company’s activities A comprehensive model for assessing the activities of the enterprise The methodology for assessing the activities of the enterprise Just in time Design to cost Risks management 



Work carried out in the framework of the state task 2.1816.2017/PCH Ministry of Education and Science of the Russian Federation.


  1. 1.
    Aven, T.: Risk assessment and risk management: review of recent advances on their foundation. Eur. J. Oper. Res. 253(1), 1–13 (2016)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Aven, T.: Risk analysis validation and trust in risk management: a postscript. Saf. Sci. 99(part B), 255–256 (2017)CrossRefGoogle Scholar
  3. 3.
    Baklashov, V.I., Kazanskaya, D.N., Skobelev, P.O., Shpilevoy, V.F., Shepilov, Y.Y.: Multi-agent system “Smart Factory” for strategic and operational management of machine-building production “Just in time” and “For a given price”. Izvestiya Samara Sci. Center Russ. Acad. Sci. 16, 1292–1295 (2014). 1(5). (in Russian)Google Scholar
  4. 4.
    Che-Ani, M.N., Kamaruddin, S., Azid, I.A.: Towards just-in-time (JIT) production system through enhancing part preparation process. In: Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management, pp. 669–673. IEEE (2017)Google Scholar
  5. 5.
    Chursin, A.A., Davydov, V.A.: Economic and mathematical model of the impact of risks on the competitiveness of enterprises of the rocket and space industry. Econ. Manag. Eng. 5, 46–52 (2012). (in Russian)Google Scholar
  6. 6.
    Denkena, B., Horst, P., Schmidt, C., Behr, M., Krieglsteiner, J.: Estimation of production cost in an early design stage of CFRP lightweight structures. Proc. CIRP 62, 45–50 (2017)CrossRefGoogle Scholar
  7. 7.
    Feduzi, A., Runde, J.: Uncovering unknown unknowns: towards a Baconian approach to management decision-making. Org. Behav. Hum. Decis. Process. 124, 268–283 (2014)CrossRefGoogle Scholar
  8. 8.
    Flage, R., Aven, T., Baraldi, P., Zio, E.: Concerns, challenges and directions of development for the issue of representing uncertainty in risk assessment. Risk Anal. 34(7), 1196–1207 (2014)CrossRefGoogle Scholar
  9. 9.
    Flage, R., Aven, T.: Emerging risk—conceptual definition and a relation to black swan types of events. Reliab. Eng. Syst. Saf. 144, 61–67 (2015)CrossRefGoogle Scholar
  10. 10.
    Gabrel, V., Murat, C., Thiele, A.: Recent advances in robust optimization: an overview. Eur. J. Oper. Res. 235, 471–483 (2014)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Giannakis, M., Papadopoulos, T.: Supply chain sustainability: a risk management approach. Int. J. Prod. Econ. 171(4), 455–470 (2016)CrossRefGoogle Scholar
  12. 12.
    Goerlandt, F., Khakzad, N., Reniers, G.: Validity and validation of safety-related quantitative risk analysis: a review. Saf. Sci. 99(part B), 127–139 (2017). Scholar
  13. 13.
    Guskova, T.N., Spiridonova, E.E.: Static methodology and practical issues of risk management. Bull. Volga State Univ. Serv. Series: Econ. 1(47), 87–93 (2017). (in Russian)Google Scholar
  14. 14.
    Hansson, S.O., Aven, T.: Is risk analysis scientific? Risk Anal. 34(7), 1173–1183 (2014)CrossRefGoogle Scholar
  15. 15.
    Heckmann, I., Comes, T., Nickel, S.: A critical review on supply chain risk—Definition, measure and modeling. Omega 52, 119–132 (2015)CrossRefGoogle Scholar
  16. 16.
    Jardini, B., Kyal, M.E., Amri, M.: The management of the supply chain by the JIT system (Just in Time) and the EDI technology (Electronic Data Interchange). In: Proceedings of the 3rd IEEE International Conference on Logistics Operations Management, Article ID 7731712 (2016)Google Scholar
  17. 17.
    Khan, F., Rathnayaka, S., Ahmed, S.: Methods and models in process safety and risk management: past, present and future. Process Saf. Environ. Prot. 98, 116–147 (2015)CrossRefGoogle Scholar
  18. 18.
    Klochkov, V.V., Vdovenkov, V.A.: The problem of ensuring the production of aviation equipment “Just in time” and the concept of “fast-reacting production”. Izvestiya Samara Sci. Center Russ. Acad. Sci. 16, 1418–1425 (2014). 1(5). (in Russian)Google Scholar
  19. 19.
    Li, X., Guo, S., Liu, Y., Du, B., Wang, L.: A production planning model for make-to-order foundry flow shop with capacity constraint. Math. Probl. Eng. (2017). Scholar
  20. 20.
    Liu, L., Wang, J.J., Liu, F., Liu, M.: Single machine-by-product planning and resource allocation scheduling problem with learning and general positional effects. J. Manuf. Syst. 43, 1–14 (2017)CrossRefGoogle Scholar
  21. 21.
    Lundberg, J., Johansson, B.J.E.: Systemic resilience model. Reliab. Eng. Syst. Saf. 141, 22–32 (2015)CrossRefGoogle Scholar
  22. 22.
    Lutoshkin, I.V.: The parameterization method for optimizing the systems which have integro-differential equations. Bull. Irkutsk State Univ. Series “Mathematics” 4(1), 44–56 (2011). (in Russian)zbMATHGoogle Scholar
  23. 23.
    Malek, R., Baxter, B., Hsiao, C.: A decision-based perspective on assessing system robustness. Proc. Comput. Sci. 44, 619–629 (2015)CrossRefGoogle Scholar
  24. 24.
    Pasman, H., Reniers, G.: Past, present and future of Quantitative Risk Assessment (QRA) and the incentive it obtained from Land-Use Planning (LUP). J. Loss Prev. Process Ind. 28, 2–9 (2014)CrossRefGoogle Scholar
  25. 25.
    Petrenya, Y.K., Glukhov, V.V., Shilin, P.S.: The concept of “designing for competition” as the basis for the formation of an innovative enterprise policy. Econ. Sci. 10(1), 155–163 (2017). Scientific and technical statements of the St. Petersburg State Polytechnic University. (in Russian)Google Scholar
  26. 26.
    Sahebjamnia, N., Torabi, S.A., Mansouri, S.A.: Innovative applications of O.R. integrated business continuity and disaster recovery planning: towards organizational resilience. Eur. J. Oper. Res. 242, 261–273 (2015)CrossRefGoogle Scholar
  27. 27.
    Spiegelhalter, D.J., Riesch, H.: Don’t know, can’t know: embracing deeper uncertainties when analysing risks. Philos. Trans. Roy. Soc. A 369, 4730–4750 (2014)MathSciNetCrossRefGoogle Scholar
  28. 28.
    SRA: Glossary society for risk analysis (2015). Accessed 14 Aug 2018
  29. 29.
    Turnbull, P., Oliver, N., Wilkinson, B.: Buyer-supplier relations in the UK - automotive industry: strategic implications of the Japanese manufacturing model. Strateg. Manag. J. 13, 159–168 (1992)CrossRefGoogle Scholar
  30. 30.
    Zahedi, R., Yusriski, R.: Stepwise optimization for model of integrated batch production and maintenance scheduling for single item processed on flow shop with two machines in JIT environment. Proc. Comput. Sci. 116, 408–420 (2017)CrossRefGoogle Scholar
  31. 31.
  32. 32.
    Wang D., Chen Y., Chen D.: Efficiency optimization and simulation to manufacturing and service systems based on manufacturing technology Just-In-Time. Pers. Ubiquit. Comput. 22, 1–13 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ulyanovsk State UniversityUlyanovskRussia

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